From d617227bbdfeb19b942a91ec5444e1932f124065 Mon Sep 17 00:00:00 2001 From: Alice Mu Date: Wed, 22 Jul 2026 19:23:04 +0000 Subject: [PATCH 01/12] feat: unified 1-to-many bounding box array architecture --- speciesnet/classifier.py | 146 ++-- speciesnet/classifier_test.py | 70 +- speciesnet/ensemble.py | 37 +- speciesnet/ensemble_prediction_combiner.py | 297 +++++--- .../ensemble_prediction_combiner_test.py | 659 ++++++++++-------- speciesnet/ensemble_test.py | 108 ++- speciesnet/multiprocessing.py | 22 +- speciesnet/scripts/run_model.py | 7 + 8 files changed, 802 insertions(+), 544 deletions(-) diff --git a/speciesnet/classifier.py b/speciesnet/classifier.py index f0e59c5..4811c59 100644 --- a/speciesnet/classifier.py +++ b/speciesnet/classifier.py @@ -127,7 +127,7 @@ def preprocess( img: Optional[PIL.Image.Image], bboxes: Optional[list[BBox]] = None, resize: bool = True, - ) -> Optional[PreprocessedImage]: + ) -> list[Optional[PreprocessedImage]]: """Preprocesses an image according to this classifier's needs. This method prepares an input image for classification. It handles @@ -149,67 +149,99 @@ def preprocess( Whether to resize the image to some expected dimensions. Returns: - A preprocessed image, or `None` if no PIL image was provided initially. + A list of preprocessed images, one for each bounding box. """ - if img is None: - return None + return [] img_tensor = F.pil_to_tensor(img) # HWC to CHW. img_tensor = F.convert_image_dtype(img_tensor, torch.float32) + results = [] if self.model_info.type_ == "always_crop": - # Crop to top bbox if available, otherwise leave image uncropped. if bboxes: - img_tensor = F.crop( - img_tensor, - int(bboxes[0].ymin * img.height), - int(bboxes[0].xmin * img.width), - int(bboxes[0].height * img.height), - int(bboxes[0].width * img.width), - ) + for bbox in bboxes: + crop = F.crop( + img_tensor, + int(bbox.ymin * img.height), + int(bbox.xmin * img.width), + int(bbox.height * img.height), + int(bbox.width * img.width), + ) + if resize: + crop = F.resize( + crop, + [SpeciesNetClassifier.IMG_SIZE, SpeciesNetClassifier.IMG_SIZE], + antialias=False, + ) + crop = F.convert_image_dtype(crop, torch.uint8) + crop = crop.permute([1, 2, 0]) # CHW to HWC. + results.append(PreprocessedImage(crop.numpy(), img.width, img.height)) + else: + # If no bounding boxes are provided but we must crop, we return an uncropped image. + if resize: + crop = F.resize( + img_tensor, + [SpeciesNetClassifier.IMG_SIZE, SpeciesNetClassifier.IMG_SIZE], + antialias=False, + ) + else: + crop = img_tensor + crop = F.convert_image_dtype(crop, torch.uint8) + crop = crop.permute([1, 2, 0]) + results.append(PreprocessedImage(crop.numpy(), img.width, img.height)) + elif self.model_info.type_ == "full_image": # Crop top and bottom of image. target_height = max( int(img.height * (1.0 - SpeciesNetClassifier.MAX_CROP_RATIO)), img.height - SpeciesNetClassifier.MAX_CROP_SIZE, ) - img_tensor = F.center_crop(img_tensor, [target_height, img.width]) + crop = F.center_crop(img_tensor, [target_height, img.width]) - if resize: - img_tensor = F.resize( - img_tensor, - [SpeciesNetClassifier.IMG_SIZE, SpeciesNetClassifier.IMG_SIZE], - antialias=False, - ) + if resize: + crop = F.resize( + crop, + [SpeciesNetClassifier.IMG_SIZE, SpeciesNetClassifier.IMG_SIZE], + antialias=False, + ) + + crop = F.convert_image_dtype(crop, torch.uint8) + crop = crop.permute([1, 2, 0]) # CHW to HWC. + + # Note: For full_image model, we just process the image once even if there are many bboxes. + # We duplicate the result so there's a 1-to-1 match with the requested bboxes limit in downstream logic. + result_img = PreprocessedImage(crop.numpy(), img.width, img.height) + if bboxes: + results.extend([result_img for _ in bboxes]) + else: + results.append(result_img) - img_tensor = F.convert_image_dtype(img_tensor, torch.uint8) - img_tensor = img_tensor.permute([1, 2, 0]) # CHW to HWC. - return PreprocessedImage(img_tensor.numpy(), img.width, img.height) + return results def predict( - self, filepath: str, img: Optional[PreprocessedImage] + self, filepath: str, imgs: list[Optional[PreprocessedImage]] ) -> dict[str, Any]: - """Runs inference on a given preprocessed image. + """Runs inference on a given preprocessed image (or list of crops for the image). Args: filepath: Location of image to run inference on. Used for reporting purposes only, and not for loading the image. - img: - Preprocessed image to run inference on. If `None`, a failure message is + imgs: + List of preprocessed image crops to run inference on. If empty, a failure message is reported back. Returns: - A dict containing either the top-5 classifications for the given image (in + A dict containing either the top-5 classifications for the given image crops (in decreasing order of confidence scores), or a failure message if no preprocessed image was provided. """ - return self.batch_predict([filepath], [img])[0] + return self.batch_predict([filepath], [imgs])[0] def batch_predict( - self, filepaths: list[str], imgs: list[Optional[PreprocessedImage]] + self, filepaths: list[str], imgs_list: list[list[Optional[PreprocessedImage]]] ) -> list[dict[str, Any]]: """Runs inference on a batch of preprocessed images. @@ -217,32 +249,51 @@ def batch_predict( filepaths: List of image locations to run inference on. Used for reporting purposes only, and not for loading the images. - imgs: - List of preprocessed images to run inference on. If an image is `None`, + imgs_list: + List of lists of preprocessed image crops to run inference on. If an image is `None`, a corresponding failure message is reported back. Returns: - A list of dict results. Each dict result contains either the top-5 - classifications for the corresponding image (in decreasing order of - confidence scores), or a failure message if no preprocessed image was - provided. + A list of dict results. Each dict result contains `classifications_list` + with the top-5 classifications for each corresponding crop image (in decreasing + order of confidence scores for each crop), or a failure message if no preprocessed + image was provided. """ predictions = {} inference_filepaths = [] batch_arr = [] - for filepath, img in zip(filepaths, imgs): - if img is None: + + for filepath, imgs in zip(filepaths, imgs_list): + if not imgs: predictions[filepath] = { "filepath": filepath, "failures": [Failure.CLASSIFIER.name], } - else: + continue + + valid_imgs = [img for img in imgs if img is not None] + + if not valid_imgs: + predictions[filepath] = { + "filepath": filepath, + "failures": [Failure.CLASSIFIER.name], + } + continue + + predictions[filepath] = { + "filepath": filepath, + "classifications_list": [] + } + + for img in valid_imgs: inference_filepaths.append(filepath) - batch_arr.append(img.arr / 255) + batch_arr.append(img.arr / 255.0) + if not batch_arr: - return list(predictions.values()) + return [predictions[fp] for fp in filepaths if fp in predictions] + batch_arr = np.stack(batch_arr, axis=0, dtype=np.float32) # On MPS devices, make sure all MPS operations happen serially. This @@ -257,16 +308,13 @@ def batch_predict( zip(inference_filepaths, scores.numpy(), indices.numpy()) ): - predictions[filepath] = { - "filepath": filepath, - "classifications": { - "classes": [self.labels[idx] for idx in indices_arr], - "scores": scores_arr.tolist(), - }, + classification = { + "classes": [self.labels[idx] for idx in indices_arr], + "scores": scores_arr.tolist(), } if hasattr(self, "target_idx"): - predictions[filepath]["classifications"].update( + classification.update( { "target_classes": self.target_labels, "target_logits": [ @@ -274,5 +322,7 @@ def batch_predict( ], } ) + + predictions[filepath]["classifications_list"].append(classification) - return [predictions[filepath] for filepath in filepaths] + return [predictions[filepath] for filepath in filepaths if filepath in predictions] diff --git a/speciesnet/classifier_test.py b/speciesnet/classifier_test.py index 140064f..09c36c9 100644 --- a/speciesnet/classifier_test.py +++ b/speciesnet/classifier_test.py @@ -104,7 +104,7 @@ def test_preprocess( # pylint: disable=too-many-positional-arguments preprocessed = classifier.preprocess( img_red_green_blue_w100_h150_h700_h150, resize=False, - ) + )[0] assert preprocessed assert preprocessed.orig_width == 100 assert preprocessed.orig_height == 1000 @@ -115,7 +115,7 @@ def test_preprocess( # pylint: disable=too-many-positional-arguments img_red_green_blue_w100_h150_h700_h150, bboxes=[], resize=False, - ) + )[0] assert preprocessed assert preprocessed.orig_width == 100 assert preprocessed.orig_height == 1000 @@ -126,7 +126,7 @@ def test_preprocess( # pylint: disable=too-many-positional-arguments img_red_green_blue_w100_h150_h700_h150, bboxes=[BBox(0 / 100, 150 / 1000, 100 / 100, 700 / 1000)], resize=False, - ) + )[0] assert preprocessed assert preprocessed.orig_width == 100 assert preprocessed.orig_height == 1000 @@ -136,7 +136,7 @@ def test_preprocess( # pylint: disable=too-many-positional-arguments preprocessed = classifier.preprocess( img_red_green_blue_w100_h150_h700_h150, bboxes=[BBox(0 / 100, 150 / 1000, 100 / 100, 700 / 1000)], - ) + )[0] assert preprocessed assert preprocessed.orig_width == 100 assert preprocessed.orig_height == 1000 @@ -147,7 +147,7 @@ def test_preprocess( # pylint: disable=too-many-positional-arguments img_red_green_blue_w100_h150_h700_h150, bboxes=[BBox(10 / 100, 150 / 1000, 40 / 100, 100 / 1000)], resize=False, - ) + )[0] assert preprocessed assert preprocessed.orig_width == 100 assert preprocessed.orig_height == 1000 @@ -157,7 +157,7 @@ def test_preprocess( # pylint: disable=too-many-positional-arguments preprocessed = classifier.preprocess( img_red_green_blue_w100_h150_h700_h150, bboxes=[BBox(10 / 100, 150 / 1000, 40 / 100, 100 / 1000)], - ) + )[0] assert preprocessed assert preprocessed.orig_width == 100 assert preprocessed.orig_height == 1000 @@ -170,7 +170,7 @@ def test_preprocess( # pylint: disable=too-many-positional-arguments img_red_green_blue_w100_h150_h700_h150, bboxes=[BBox(10 / 100, 150 / 1000, 40 / 100, 100 / 1000)], resize=False, - ) + )[0] assert preprocessed assert preprocessed.orig_width == 100 assert preprocessed.orig_height == 1000 @@ -180,7 +180,7 @@ def test_preprocess( # pylint: disable=too-many-positional-arguments preprocessed = classifier.preprocess( img_red_green_blue_w100_h150_h700_h150, resize=False, - ) + )[0] assert preprocessed assert preprocessed.orig_width == 100 assert preprocessed.orig_height == 1000 @@ -189,7 +189,7 @@ def test_preprocess( # pylint: disable=too-many-positional-arguments preprocessed = classifier.preprocess( img_red_green_blue_w100_h150_h700_h150, - ) + )[0] assert preprocessed assert preprocessed.orig_width == 100 assert preprocessed.orig_height == 1000 @@ -199,7 +199,7 @@ def test_preprocess( # pylint: disable=too-many-positional-arguments preprocessed = classifier.preprocess( img_red_green_blue_w100_h200_h2000_h200, resize=False, - ) + )[0] assert preprocessed assert preprocessed.orig_width == 100 assert preprocessed.orig_height == 2400 @@ -208,7 +208,7 @@ def test_preprocess( # pylint: disable=too-many-positional-arguments preprocessed = classifier.preprocess( img_red_green_blue_w100_h200_h2000_h200, - ) + )[0] assert preprocessed assert preprocessed.orig_width == 100 assert preprocessed.orig_height == 2400 @@ -218,21 +218,21 @@ def test_preprocess( # pylint: disable=too-many-positional-arguments def test_predict(self, classifier, img_green_w480_h480) -> None: filepath = "missing.jpg" - prediction = classifier.predict(filepath, None) + prediction = classifier.predict(filepath, []) assert prediction["filepath"] == filepath assert "failures" in prediction filepath = "green.png" prediction = classifier.predict( - filepath, PreprocessedImage(np.asarray(img_green_w480_h480), 480, 480) + filepath, [PreprocessedImage(np.asarray(img_green_w480_h480), 480, 480)] ) assert prediction["filepath"] == filepath assert "failures" not in prediction - assert "classifications" in prediction - assert "classes" in prediction["classifications"] - assert "scores" in prediction["classifications"] - assert prediction["classifications"]["scores"] == sorted( - prediction["classifications"]["scores"], reverse=True + assert "classifications_list" in prediction + assert "classes" in prediction["classifications_list"][0] + assert "scores" in prediction["classifications_list"][0] + assert prediction["classifications_list"][0]["scores"] == sorted( + prediction["classifications_list"][0]["scores"], reverse=True ) @pytest.fixture( @@ -312,9 +312,9 @@ def test_predict(self, classifier, img_green_w480_h480) -> None: ) def predicted_vs_expected(self, classifier, request) -> tuple[dict, str]: filepath, bboxes, label = request.param - img = classifier.preprocess(load_rgb_image(filepath), bboxes=bboxes) - assert img is not None - return classifier.predict(filepath, img)["classifications"], label + img_list = classifier.preprocess(load_rgb_image(filepath), bboxes=bboxes) + assert img_list is not [] + return classifier.predict(filepath, img_list)["classifications_list"][0], label def test_classifications(self, predicted_vs_expected) -> None: classifications, label = predicted_vs_expected @@ -365,8 +365,8 @@ def test_target_species_batched_vs_non_batched( # Test 1: Non-batched prediction (batch_size=1) non_batched_predictions = [] - for filepath, img in zip(filepaths, preprocessed_imgs): - prediction = classifier_with_targets.predict(filepath, img) + for filepath, imgs in zip(filepaths, preprocessed_imgs): + prediction = classifier_with_targets.predict(filepath, imgs) non_batched_predictions.append(prediction) # Test 2: Batched prediction (batch_size>1) @@ -381,20 +381,20 @@ def test_target_species_batched_vs_non_batched( zip(non_batched_predictions, batched_predictions) ): # Check that both have target_logits - assert "target_logits" in non_batched["classifications"] - assert "target_logits" in batched["classifications"] + assert "target_logits" in non_batched["classifications_list"][0] + assert "target_logits" in batched["classifications_list"][0] # Check that target_classes are present and identical - assert "target_classes" in non_batched["classifications"] - assert "target_classes" in batched["classifications"] + assert "target_classes" in non_batched["classifications_list"][0] + assert "target_classes" in batched["classifications_list"][0] assert ( - non_batched["classifications"]["target_classes"] - == batched["classifications"]["target_classes"] + non_batched["classifications_list"][0]["target_classes"] + == batched["classifications_list"][0]["target_classes"] ) # Check that target_logits are identical - non_batched_logits = non_batched["classifications"]["target_logits"] - batched_logits = batched["classifications"]["target_logits"] + non_batched_logits = non_batched["classifications_list"][0]["target_logits"] + batched_logits = batched["classifications_list"][0]["target_logits"] assert len(non_batched_logits) == len(batched_logits) assert len(non_batched_logits) == len(target_species) @@ -414,12 +414,12 @@ def test_target_species_batched_vs_non_batched( # Also verify that regular classifications match assert ( - non_batched["classifications"]["classes"] - == batched["classifications"]["classes"] + non_batched["classifications_list"][0]["classes"] + == batched["classifications_list"][0]["classes"] ) np.testing.assert_allclose( - non_batched["classifications"]["scores"], - batched["classifications"]["scores"], + non_batched["classifications_list"][0]["scores"], + batched["classifications_list"][0]["scores"], rtol=1e-3, # 0.1% relative tolerance atol=1e-3, # 0.001 absolute tolerance err_msg=f"Scores mismatch for image {i} ({filepaths[i]})", diff --git a/speciesnet/ensemble.py b/speciesnet/ensemble.py index 8254064..b00173d 100644 --- a/speciesnet/ensemble.py +++ b/speciesnet/ensemble.py @@ -20,14 +20,14 @@ import json import time -from typing import Any, Callable +from typing import Any, Callable, Optional from absl import logging from humanfriendly import format_timespan from speciesnet.constants import Classification from speciesnet.constants import Failure -from speciesnet.ensemble_prediction_combiner import combine_predictions_for_single_item +from speciesnet.ensemble_prediction_combiner import combine_predictions from speciesnet.geofence_utils import geofence_animal_classification from speciesnet.geofence_utils import roll_up_labels_to_first_matching_level from speciesnet.utils import ModelInfo @@ -72,7 +72,7 @@ def __init__( self, model_name: str, geofence: bool = True, - prediction_combiner: Callable = combine_predictions_for_single_item, + prediction_combiner: Callable = combine_predictions, force_model_download: bool = False, ) -> None: """Loads the ensemble resources. @@ -122,10 +122,11 @@ def load_geofence(self): def combine( # pylint: disable=too-many-positional-arguments self, filepaths: list[str], - classifier_results: dict[str, Any], - detector_results: dict[str, Any], - geolocation_results: dict[str, Any], - partial_predictions: dict[str, dict], + classifier_results: dict[str, dict], + detector_results: dict[str, dict], + geolocation_results: dict[str, dict], + partial_predictions: Optional[dict[str, dict]] = None, + max_classifications: Optional[int] = None, ) -> list[dict[str, Any]]: """Ensembles classifications and detections for a list of images. @@ -163,9 +164,9 @@ def combine( # pylint: disable=too-many-positional-arguments filepath in classifier_results and "failures" not in classifier_results[filepath] ): - classifications = classifier_results[filepath]["classifications"] + classifications_list = classifier_results[filepath]["classifications_list"] else: - classifications = None + classifications_list = None failure |= Failure.CLASSIFIER if ( filepath in detector_results @@ -191,15 +192,15 @@ def combine( # pylint: disable=too-many-positional-arguments "admin1_region": geolocation.get("admin1_region"), "latitude": geolocation.get("latitude"), "longitude": geolocation.get("longitude"), - "classifications": classifications, + "classifications_list": classifications_list, "detections": detections, } result = {key: value for key, value in result.items() if value is not None} - # Most importantly, ensemble everything into a single prediction. - if classifications is not None and detections is not None: - prediction, score, source = self.prediction_combiner( - classifications=classifications, + # Most importantly, ensemble everything into predictions. + if classifications_list is not None and detections is not None: + result["ensemble_predictions"] = self.prediction_combiner( + classifications_list=classifications_list, detections=detections, country=geolocation.get("country"), admin1_region=geolocation.get("admin1_region"), @@ -208,14 +209,8 @@ def combine( # pylint: disable=too-many-positional-arguments enable_geofence=self.enable_geofence, geofence_fn=geofence_animal_classification, roll_up_fn=roll_up_labels_to_first_matching_level, + max_classifications=max_classifications, ) - result["prediction"] = ( - prediction.value - if isinstance(prediction, Classification) - else prediction - ) - result["prediction_score"] = score - result["prediction_source"] = source # Finally, report the model version. result["model_version"] = self.model_info.version diff --git a/speciesnet/ensemble_prediction_combiner.py b/speciesnet/ensemble_prediction_combiner.py index a2c7081..46b6785 100644 --- a/speciesnet/ensemble_prediction_combiner.py +++ b/speciesnet/ensemble_prediction_combiner.py @@ -22,12 +22,14 @@ PredictionLabelType = str PredictionScoreType = float PredictionSourceType = str -PredictionType = tuple[PredictionLabelType, PredictionScoreType, PredictionSourceType] +PredictionType = tuple[ + PredictionLabelType, PredictionScoreType, PredictionSourceType +] -def combine_predictions_for_single_item( +def combine_predictions( *, - classifications: dict[str, list], + classifications_list: list[dict], detections: list[dict], country: Optional[str], admin1_region: Optional[str], @@ -36,8 +38,9 @@ def combine_predictions_for_single_item( enable_geofence: bool, geofence_fn: Callable, roll_up_fn: Callable, -) -> PredictionType: - """Ensembles classifications and detections for a single image. + max_classifications: Optional[int] = None, +) -> list[dict]: + """Ensembles classifications and detections from multiple bounding boxes. This operation leverages multiple heuristics to make the most of the classifier and the detector predictions through a complex set of decisions. It introduces various @@ -45,9 +48,9 @@ def combine_predictions_for_single_item( at higher taxonomy levels and even unknowns. Args: - classifications: - Dict of classification results. "classes" and "scores" are expected to be - provided among the dict keys. + classifications_list: + List of dicts containing classification results for each bounding box. + "classes" and "scores" are expected to be provided among the dict keys. detections: List of detection results, sorted in decreasing order of their confidence score. Each detection is expected to be a dict providing "label" and "conf" @@ -68,121 +71,193 @@ def combine_predictions_for_single_item( Callable to geofence animal classifications. roll_up_fn: Callable to roll up labels to the first matching level. + max_classifications: + Maximum number of predictions to return per image. Defaults to returning + all processed predictions. Returns: - A tuple of describing the ensemble result. + A list of dicts describing the ensemble results. """ + effective_detections = ( + detections + if detections + else [{"label": Detection.ANIMAL, "conf": 0.0, "bbox": None}] + ) + results = [] - top_classification_class = classifications["classes"][0] - top_classification_score = classifications["scores"][0] - top_detection_class = detections[0]["label"] if detections else Detection.ANIMAL - top_detection_score = detections[0]["conf"] if detections else 0.0 - - if top_detection_class == Detection.HUMAN: - # Threshold #1a: high-confidence HUMAN detections. - if top_detection_score > 0.7: - return Classification.HUMAN, top_detection_score, "detector" - - # Threshold #1b: mid-confidence HUMAN detections + high-confidence - # HUMAN/VEHICLE classifications. - if ( - top_detection_score > 0.2 - and top_classification_class - in {Classification.HUMAN, Classification.VEHICLE} - and top_classification_score > 0.5 - ): - return Classification.HUMAN, top_classification_score, "classifier" - - if top_detection_class == Detection.VEHICLE: - # Threshold #2a: mid-confidence VEHICLE detections + high-confidence HUMAN - # classifications. - if ( - top_detection_score > 0.2 - and top_classification_class == Classification.HUMAN - and top_classification_score > 0.5 - ): - return Classification.HUMAN, top_classification_score, "classifier" - - # Threshold #2b: high-confidence VEHICLE detections. - if top_detection_score > 0.7: - return Classification.VEHICLE, top_detection_score, "detector" - - # Threshold #2c: mid-confidence VEHICLE detections + high-confidence VEHICLE - # classifications. - if ( - top_detection_score > 0.2 - and top_classification_class == Classification.VEHICLE - and top_classification_score > 0.4 - ): - return Classification.VEHICLE, top_classification_score, "classifier" - - # Threshold #3a: high-confidence BLANK "detections" + high-confidence BLANK - # classifications. - if ( - top_detection_score < 0.2 - and top_classification_class == Classification.BLANK - and top_classification_score > 0.5 - ): - return Classification.BLANK, top_classification_score, "classifier" - - # Threshold #3b: extra-high-confidence BLANK classifications. - if ( - top_classification_class == Classification.BLANK - and top_classification_score > 0.99 - ): - return Classification.BLANK, top_classification_score, "classifier" - - if top_classification_class not in { - Classification.BLANK, - Classification.HUMAN, - Classification.VEHICLE, - }: - # Threshold #4a: extra-high-confidence ANIMAL classifications. - if top_classification_score > 0.8: - return geofence_fn( - labels=classifications["classes"], - scores=classifications["scores"], - country=country, - admin1_region=admin1_region, - taxonomy_map=taxonomy_map, - geofence_map=geofence_map, - enable_geofence=enable_geofence, - ) + for i, det in enumerate(effective_detections): + det_class = det["label"] + det_score = det["conf"] + prediction = None + score = 0.0 + source = "" + + if i < len(classifications_list) and classifications_list[i]: + classifications = classifications_list[i] + top_classification_class = classifications.get( + "classes", [Classification.UNKNOWN] + )[0] + top_classification_score = classifications.get("scores", [0.0])[0] + else: + classifications = {"classes": [Classification.UNKNOWN], "scores": [0.0]} + top_classification_class = Classification.UNKNOWN + top_classification_score = 0.0 + + if det_class == Detection.HUMAN: + # Threshold #1a: high-confidence HUMAN detections. + if det_score > 0.7: + prediction, score, source = Classification.HUMAN, det_score, "detector" + elif ( + # Threshold #1b: mid-confidence HUMAN detections + high-confidence + # HUMAN/VEHICLE classifications. + det_score > 0.2 + and top_classification_class + in {Classification.HUMAN, Classification.VEHICLE} + and top_classification_score > 0.5 + ): + prediction, score, source = ( + Classification.HUMAN, + top_classification_score, + "classifier", + ) + + if prediction is None and det_class == Detection.VEHICLE: + # Threshold #2a: mid-confidence VEHICLE detections + high-confidence HUMAN + # classifications. + if ( + det_score > 0.2 + and top_classification_class == Classification.HUMAN + and top_classification_score > 0.5 + ): + prediction, score, source = ( + Classification.HUMAN, + top_classification_score, + "classifier", + ) + # Threshold #2b: high-confidence VEHICLE detections. + elif det_score > 0.7: + prediction, score, source = ( + Classification.VEHICLE, + det_score, + "detector", + ) + # Threshold #2c: mid-confidence VEHICLE detections + high-confidence VEHICLE + # classifications. + elif ( + det_score > 0.2 + and top_classification_class == Classification.VEHICLE + and top_classification_score > 0.4 + ): + prediction, score, source = ( + Classification.VEHICLE, + top_classification_score, + "classifier", + ) - # Threshold #4b: high-confidence ANIMAL classifications + mid-confidence - # ANIMAL detections. - if ( - top_classification_score > 0.65 - and top_detection_class == Detection.ANIMAL - and top_detection_score > 0.2 - ): - return geofence_fn( + if prediction is None: + # Threshold #3a: high-confidence BLANK "detections" + high-confidence BLANK + # classifications. + if ( + det_score < 0.2 + and top_classification_class == Classification.BLANK + and top_classification_score > 0.5 + ): + prediction, score, source = ( + Classification.BLANK, + top_classification_score, + "classifier", + ) + # Threshold #3b: extra-high-confidence BLANK classifications. + elif ( + top_classification_class == Classification.BLANK + and top_classification_score > 0.99 + ): + prediction, score, source = ( + Classification.BLANK, + top_classification_score, + "classifier", + ) + elif top_classification_class not in { + Classification.BLANK, + Classification.HUMAN, + Classification.VEHICLE, + }: + # Threshold #4a: extra-high-confidence ANIMAL classifications. + if top_classification_score > 0.8: + prediction, score, source = geofence_fn( + labels=classifications["classes"], + scores=classifications["scores"], + country=country, + admin1_region=admin1_region, + taxonomy_map=taxonomy_map, + geofence_map=geofence_map, + enable_geofence=enable_geofence, + ) + # Threshold #4b: high-confidence ANIMAL classifications + mid-confidence + # ANIMAL detections. + elif ( + top_classification_score > 0.65 + and det_class == Detection.ANIMAL + and det_score > 0.2 + ): + prediction, score, source = geofence_fn( + labels=classifications["classes"], + scores=classifications["scores"], + country=country, + admin1_region=admin1_region, + taxonomy_map=taxonomy_map, + geofence_map=geofence_map, + enable_geofence=enable_geofence, + ) + + if prediction is None: + # Threshold #5a: high-confidence ANIMAL rollups. + rollup = roll_up_fn( labels=classifications["classes"], scores=classifications["scores"], country=country, admin1_region=admin1_region, + target_taxonomy_levels=[ + "genus", + "family", + "order", + "class", + "kingdom", + ], + non_blank_threshold=0.65, taxonomy_map=taxonomy_map, geofence_map=geofence_map, enable_geofence=enable_geofence, ) + if rollup: + prediction, score, source = rollup + # Threshold #5b: mid-confidence ANIMAL detections. + elif det_class == Detection.ANIMAL and det_score > 0.5: + prediction, score, source = Classification.ANIMAL, det_score, "detector" + else: + prediction, score, source = ( + Classification.UNKNOWN, + top_classification_score, + "classifier", + ) - # Threshold #5a: high-confidence ANIMAL rollups. - rollup = roll_up_fn( - labels=classifications["classes"], - scores=classifications["scores"], - country=country, - admin1_region=admin1_region, - target_taxonomy_levels=["genus", "family", "order", "class", "kingdom"], - non_blank_threshold=0.65, - taxonomy_map=taxonomy_map, - geofence_map=geofence_map, - enable_geofence=enable_geofence, - ) - if rollup: - return rollup - - # Threshold #5b: mid-confidence ANIMAL detections. - if top_detection_class == Detection.ANIMAL and top_detection_score > 0.5: - return Classification.ANIMAL, top_detection_score, "detector" + results.append({ + "prediction": ( + prediction.value + if isinstance(prediction, Classification) + else prediction + ), + "prediction_score": score, + "prediction_source": source, + "bbox": det.get("bbox") if "bbox" in det else None, + }) - return Classification.UNKNOWN, top_classification_score, "classifier" + results.sort( + key=lambda x: x["prediction_score"] + if x["prediction_score"] is not None + else 0.0, + reverse=True, + ) + if max_classifications is not None and max_classifications > 0: + return results[:max_classifications] + return results diff --git a/speciesnet/ensemble_prediction_combiner_test.py b/speciesnet/ensemble_prediction_combiner_test.py index 04b4e35..0fd2296 100644 --- a/speciesnet/ensemble_prediction_combiner_test.py +++ b/speciesnet/ensemble_prediction_combiner_test.py @@ -17,10 +17,9 @@ # pylint: disable=redefined-outer-name import pytest - from speciesnet.constants import Classification from speciesnet.constants import Detection -from speciesnet.ensemble_prediction_combiner import combine_predictions_for_single_item +from speciesnet.ensemble_prediction_combiner import combine_predictions # fmt: off # pylint: disable=line-too-long @@ -67,310 +66,382 @@ @pytest.fixture def taxonomy_map(): - return { - BLANK_FC: BLANK, - HUMAN_FC: HUMAN, - VEHICLE_FC: VEHICLE, - LION_FC: LION, - PANTHERA_GENUS_FC: PANTHERA_GENUS, - FELIDAE_FAMILY_FC: FELIDAE_FAMILY, - CARNIVORA_ORDER_FC: CARNIVORA_ORDER, - MAMMALIA_CLASS_FC: MAMMALIA_CLASS, - ANIMAL_KINGDOM_FC: ANIMAL_KINGDOM, - BROWN_BEAR_FC: BROWN_BEAR, - POLAR_BEAR_FC: POLAR_BEAR, - GIANT_PANDA_FC: GIANT_PANDA, - URSUS_GENUS_FC: URSUS_GENUS, - URSIDAE_FAMILY_FC: URSIDAE_FAMILY, - } + return { + BLANK_FC: BLANK, + HUMAN_FC: HUMAN, + VEHICLE_FC: VEHICLE, + LION_FC: LION, + PANTHERA_GENUS_FC: PANTHERA_GENUS, + FELIDAE_FAMILY_FC: FELIDAE_FAMILY, + CARNIVORA_ORDER_FC: CARNIVORA_ORDER, + MAMMALIA_CLASS_FC: MAMMALIA_CLASS, + ANIMAL_KINGDOM_FC: ANIMAL_KINGDOM, + BROWN_BEAR_FC: BROWN_BEAR, + POLAR_BEAR_FC: POLAR_BEAR, + GIANT_PANDA_FC: GIANT_PANDA, + URSUS_GENUS_FC: URSUS_GENUS, + URSIDAE_FAMILY_FC: URSIDAE_FAMILY, + } @pytest.fixture def geofence_map(): - return { - LION_FC: { - "allow": { - "KEN": [], - "TZA": [], - } - }, - PANTHERA_GENUS_FC: { - "allow": { - "KEN": [], - "TZA": [], - "USA": ["AK", "CA"], - } - }, - FELIDAE_FAMILY_FC: { - "allow": { - "FRA": [], - "KEN": [], - "TZA": [], - "USA": [], - }, - "block": { - "FRA": [], - "USA": ["NY"], - }, - }, - SAND_CAT_FC: { - "block": { - "AUS": [], - }, - }, - URSIDAE_FAMILY_FC: { - "block": { - "GBR": [], - }, - }, - } + return { + LION_FC: { + "allow": { + "KEN": [], + "TZA": [], + } + }, + PANTHERA_GENUS_FC: { + "allow": { + "KEN": [], + "TZA": [], + "USA": ["AK", "CA"], + } + }, + FELIDAE_FAMILY_FC: { + "allow": { + "FRA": [], + "KEN": [], + "TZA": [], + "USA": [], + }, + "block": { + "FRA": [], + "USA": ["NY"], + }, + }, + SAND_CAT_FC: { + "block": { + "AUS": [], + }, + }, + URSIDAE_FAMILY_FC: { + "block": { + "GBR": [], + }, + }, + } def mock_geofence_fn(*args, **kwargs): - del args - del kwargs - return "geofenced_label", 0.9, "geofence_source" + del args + del kwargs + return "geofenced_label", 0.9, "geofence_source" def mock_roll_up_fn(*args, **kwargs): - del args - del kwargs - return "rollup_label", 0.8, "rollup_source" + del args + del kwargs + return "rollup_label", 0.8, "rollup_source" def mock_roll_up_fn_return_none(*args, **kwargs): - del args - del kwargs - + del args + del kwargs + + +def combine_predictions_for_single_item( + classifications, + detections, + country, + admin1_region, + taxonomy_map, + geofence_map, + enable_geofence, + geofence_fn, + roll_up_fn +): + """Test helper transforming single-item permutations into the list format.""" + results = combine_predictions( + classifications_list=[classifications], + detections=detections, + country=country, + admin1_region=admin1_region, + taxonomy_map=taxonomy_map, + geofence_map=geofence_map, + enable_geofence=enable_geofence, + geofence_fn=geofence_fn, + roll_up_fn=roll_up_fn, + ) + # The tests expect a flat tuple of (label, score, source) to be asserted + return results[0]["prediction"], results[0]["prediction_score"], results[0]["prediction_source"] class TestPredictionEnsembleCombiner: - """Tests for the ensemble combiner function.""" - - def test_combine_predictions_for_single_item_human_detections( - self, taxonomy_map, geofence_map - ) -> None: - # High-confidence HUMAN detection. - classifications = {"classes": [LION], "scores": [0.1]} - detections = [{"label": Detection.HUMAN, "conf": 0.8}] - result = combine_predictions_for_single_item( - classifications=classifications, - detections=detections, - country=None, - admin1_region=None, - taxonomy_map=taxonomy_map, - geofence_map=geofence_map, - enable_geofence=True, - geofence_fn=mock_geofence_fn, - roll_up_fn=mock_roll_up_fn, - ) - assert result == (Classification.HUMAN, 0.8, "detector") - - # Mid-confidence HUMAN detection + high-confidence HUMAN classification. - classifications = {"classes": [HUMAN], "scores": [0.7]} - detections = [{"label": Detection.HUMAN, "conf": 0.3}] - result = combine_predictions_for_single_item( - classifications=classifications, - detections=detections, - country=None, - admin1_region=None, - taxonomy_map=taxonomy_map, - geofence_map=geofence_map, - enable_geofence=True, - geofence_fn=mock_geofence_fn, - roll_up_fn=mock_roll_up_fn, - ) - assert result == (Classification.HUMAN, 0.7, "classifier") - - # Mid-confidence HUMAN detection + high-confidence VEHICLE classification. - classifications = {"classes": [VEHICLE], "scores": [0.7]} - detections = [{"label": Detection.HUMAN, "conf": 0.3}] - result = combine_predictions_for_single_item( - classifications=classifications, - detections=detections, - country=None, - admin1_region=None, - taxonomy_map=taxonomy_map, - geofence_map=geofence_map, - enable_geofence=True, - geofence_fn=mock_geofence_fn, - roll_up_fn=mock_roll_up_fn, - ) - assert result == (Classification.HUMAN, 0.7, "classifier") - - def test_combine_predictions_for_single_item_vehicle_detections( - self, taxonomy_map, geofence_map - ) -> None: - # Mid-confidence VEHICLE detection + high-confidence HUMAN classification. - classifications = {"classes": [HUMAN], "scores": [0.7]} - detections = [{"label": Detection.VEHICLE, "conf": 0.3}] - result = combine_predictions_for_single_item( - classifications=classifications, - detections=detections, - country=None, - admin1_region=None, - taxonomy_map=taxonomy_map, - geofence_map=geofence_map, - enable_geofence=True, - geofence_fn=mock_geofence_fn, - roll_up_fn=mock_roll_up_fn, - ) - assert result == (Classification.HUMAN, 0.7, "classifier") - - # High-confidence VEHICLE detection. - classifications = {"classes": [LION], "scores": [0.1]} - detections = [{"label": Detection.VEHICLE, "conf": 0.8}] - result = combine_predictions_for_single_item( - classifications=classifications, - detections=detections, - country=None, - admin1_region=None, - taxonomy_map=taxonomy_map, - geofence_map=geofence_map, - enable_geofence=True, - geofence_fn=mock_geofence_fn, - roll_up_fn=mock_roll_up_fn, - ) - assert result == (Classification.VEHICLE, 0.8, "detector") - - # Mid-confidence VEHICLE detection + high-confidence VEHICLE classification. - classifications = {"classes": [VEHICLE], "scores": [0.5]} - detections = [{"label": Detection.VEHICLE, "conf": 0.3}] - result = combine_predictions_for_single_item( - classifications=classifications, - detections=detections, - country=None, - admin1_region=None, - taxonomy_map=taxonomy_map, - geofence_map=geofence_map, - enable_geofence=True, - geofence_fn=mock_geofence_fn, - roll_up_fn=mock_roll_up_fn, - ) - assert result == (Classification.VEHICLE, 0.5, "classifier") - - def test_combine_predictions_for_single_item_blank_classifications( - self, taxonomy_map, geofence_map - ) -> None: - # High-confidence BLANK "detection" + high-confidence BLANK classification. - classifications = {"classes": [BLANK], "scores": [0.7]} - detections = [{"label": Detection.ANIMAL, "conf": 0.1}] - result = combine_predictions_for_single_item( - classifications=classifications, - detections=detections, - country=None, - admin1_region=None, - taxonomy_map=taxonomy_map, - geofence_map=geofence_map, - enable_geofence=True, - geofence_fn=mock_geofence_fn, - roll_up_fn=mock_roll_up_fn, - ) - assert result == (Classification.BLANK, 0.7, "classifier") - - # Extra-high-confidence BLANK classification. - classifications = {"classes": [BLANK], "scores": [0.995]} - detections = [{"label": Detection.ANIMAL, "conf": 0.3}] - result = combine_predictions_for_single_item( - classifications=classifications, - detections=detections, - country=None, - admin1_region=None, - taxonomy_map=taxonomy_map, - geofence_map=geofence_map, - enable_geofence=True, - geofence_fn=mock_geofence_fn, - roll_up_fn=mock_roll_up_fn, - ) - assert result == (Classification.BLANK, 0.995, "classifier") - - def test_combine_predictions_for_single_item_animal_classifications( - self, taxonomy_map, geofence_map - ) -> None: - # Extra-high-confidence ANIMAL classification. - classifications = {"classes": [LION], "scores": [0.9]} - detections = [{"label": Detection.ANIMAL, "conf": 0.1}] - result = combine_predictions_for_single_item( - classifications=classifications, - detections=detections, - country=None, - admin1_region=None, - taxonomy_map=taxonomy_map, - geofence_map=geofence_map, - enable_geofence=True, - geofence_fn=mock_geofence_fn, - roll_up_fn=mock_roll_up_fn, - ) - assert result == ("geofenced_label", 0.9, "geofence_source") - - # High-confidence ANIMAL classification + mid-confidence ANIMAL detection. - classifications = {"classes": [LION], "scores": [0.7]} - detections = [{"label": Detection.ANIMAL, "conf": 0.3}] - result = combine_predictions_for_single_item( - classifications=classifications, - detections=detections, - country=None, - admin1_region=None, - taxonomy_map=taxonomy_map, - geofence_map=geofence_map, - enable_geofence=True, - geofence_fn=mock_geofence_fn, - roll_up_fn=mock_roll_up_fn, - ) - assert result == ("geofenced_label", 0.9, "geofence_source") - - def test_combine_predictions_for_single_item_animal_rollups( - self, taxonomy_map, geofence_map - ) -> None: - # High-confidence ANIMAL rollups. - classifications = {"classes": [LION], "scores": [0.6]} - detections = [{"label": Detection.ANIMAL, "conf": 0.1}] - result = combine_predictions_for_single_item( - classifications=classifications, - detections=detections, - country=None, - admin1_region=None, - taxonomy_map=taxonomy_map, - geofence_map=geofence_map, - enable_geofence=True, - geofence_fn=mock_geofence_fn, - roll_up_fn=mock_roll_up_fn, - ) - assert result == ("rollup_label", 0.8, "rollup_source") - - def test_combine_predictions_for_single_item_animal_detections( - self, taxonomy_map, geofence_map - ) -> None: - # Mid-confidence ANIMAL detection. - classifications = {"classes": [LION], "scores": [0.1]} - detections = [{"label": Detection.ANIMAL, "conf": 0.6}] - result = combine_predictions_for_single_item( - classifications=classifications, - detections=detections, - country=None, - admin1_region=None, - taxonomy_map=taxonomy_map, - geofence_map=geofence_map, - enable_geofence=True, - geofence_fn=mock_geofence_fn, - roll_up_fn=mock_roll_up_fn_return_none, - ) - assert result == (Classification.ANIMAL, 0.6, "detector") - - def test_combine_predictions_for_single_item_unknown( - self, taxonomy_map, geofence_map - ) -> None: - # Fallback to UNKNOWN classification. - classifications = {"classes": [LION], "scores": [0.1]} - detections = [{"label": Detection.ANIMAL, "conf": 0.1}] - result = combine_predictions_for_single_item( - classifications=classifications, - detections=detections, - country=None, - admin1_region=None, - taxonomy_map=taxonomy_map, - geofence_map=geofence_map, - enable_geofence=True, - geofence_fn=mock_geofence_fn, - roll_up_fn=mock_roll_up_fn_return_none, - ) - assert result == (Classification.UNKNOWN, 0.1, "classifier") + """Tests for the ensemble combiner function.""" + + def test_combine_predictions_for_single_item_human_detections( + self, taxonomy_map, geofence_map + ) -> None: + # High-confidence HUMAN detection. + classifications = {"classes": [LION], "scores": [0.1]} + detections = [{"label": Detection.HUMAN, "conf": 0.8}] + result = combine_predictions_for_single_item( + classifications=classifications, + detections=detections, + country=None, + admin1_region=None, + taxonomy_map=taxonomy_map, + geofence_map=geofence_map, + enable_geofence=True, + geofence_fn=mock_geofence_fn, + roll_up_fn=mock_roll_up_fn, + ) + assert result == (Classification.HUMAN, 0.8, "detector") + + # Mid-confidence HUMAN detection + high-confidence HUMAN classification. + classifications = {"classes": [HUMAN], "scores": [0.7]} + detections = [{"label": Detection.HUMAN, "conf": 0.3}] + result = combine_predictions_for_single_item( + classifications=classifications, + detections=detections, + country=None, + admin1_region=None, + taxonomy_map=taxonomy_map, + geofence_map=geofence_map, + enable_geofence=True, + geofence_fn=mock_geofence_fn, + roll_up_fn=mock_roll_up_fn, + ) + assert result == (Classification.HUMAN, 0.7, "classifier") + + # Mid-confidence HUMAN detection + high-confidence VEHICLE classification. + classifications = {"classes": [VEHICLE], "scores": [0.7]} + detections = [{"label": Detection.HUMAN, "conf": 0.3}] + result = combine_predictions_for_single_item( + classifications=classifications, + detections=detections, + country=None, + admin1_region=None, + taxonomy_map=taxonomy_map, + geofence_map=geofence_map, + enable_geofence=True, + geofence_fn=mock_geofence_fn, + roll_up_fn=mock_roll_up_fn, + ) + assert result == (Classification.HUMAN, 0.7, "classifier") + + def test_combine_predictions_for_single_item_vehicle_detections( + self, taxonomy_map, geofence_map + ) -> None: + # Mid-confidence VEHICLE detection + high-confidence HUMAN classification. + classifications = {"classes": [HUMAN], "scores": [0.7]} + detections = [{"label": Detection.VEHICLE, "conf": 0.3}] + result = combine_predictions_for_single_item( + classifications=classifications, + detections=detections, + country=None, + admin1_region=None, + taxonomy_map=taxonomy_map, + geofence_map=geofence_map, + enable_geofence=True, + geofence_fn=mock_geofence_fn, + roll_up_fn=mock_roll_up_fn, + ) + assert result == (Classification.HUMAN, 0.7, "classifier") + + # High-confidence VEHICLE detection. + classifications = {"classes": [LION], "scores": [0.1]} + detections = [{"label": Detection.VEHICLE, "conf": 0.8}] + result = combine_predictions_for_single_item( + classifications=classifications, + detections=detections, + country=None, + admin1_region=None, + taxonomy_map=taxonomy_map, + geofence_map=geofence_map, + enable_geofence=True, + geofence_fn=mock_geofence_fn, + roll_up_fn=mock_roll_up_fn, + ) + assert result == (Classification.VEHICLE, 0.8, "detector") + + # Mid-confidence VEHICLE detection + high-confidence VEHICLE classification. + classifications = {"classes": [VEHICLE], "scores": [0.5]} + detections = [{"label": Detection.VEHICLE, "conf": 0.3}] + result = combine_predictions_for_single_item( + classifications=classifications, + detections=detections, + country=None, + admin1_region=None, + taxonomy_map=taxonomy_map, + geofence_map=geofence_map, + enable_geofence=True, + geofence_fn=mock_geofence_fn, + roll_up_fn=mock_roll_up_fn, + ) + assert result == (Classification.VEHICLE, 0.5, "classifier") + + def test_combine_predictions_for_single_item_blank_classifications( + self, taxonomy_map, geofence_map + ) -> None: + # High-confidence BLANK "detection" + high-confidence BLANK classification. + classifications = {"classes": [BLANK], "scores": [0.7]} + detections = [{"label": Detection.ANIMAL, "conf": 0.1}] + result = combine_predictions_for_single_item( + classifications=classifications, + detections=detections, + country=None, + admin1_region=None, + taxonomy_map=taxonomy_map, + geofence_map=geofence_map, + enable_geofence=True, + geofence_fn=mock_geofence_fn, + roll_up_fn=mock_roll_up_fn, + ) + assert result == (Classification.BLANK, 0.7, "classifier") + + # Extra-high-confidence BLANK classification. + classifications = {"classes": [BLANK], "scores": [0.995]} + detections = [{"label": Detection.ANIMAL, "conf": 0.3}] + result = combine_predictions_for_single_item( + classifications=classifications, + detections=detections, + country=None, + admin1_region=None, + taxonomy_map=taxonomy_map, + geofence_map=geofence_map, + enable_geofence=True, + geofence_fn=mock_geofence_fn, + roll_up_fn=mock_roll_up_fn, + ) + assert result == (Classification.BLANK, 0.995, "classifier") + + def test_combine_predictions_for_single_item_animal_classifications( + self, taxonomy_map, geofence_map + ) -> None: + # Extra-high-confidence ANIMAL classification. + classifications = {"classes": [LION], "scores": [0.9]} + detections = [{"label": Detection.ANIMAL, "conf": 0.1}] + result = combine_predictions_for_single_item( + classifications=classifications, + detections=detections, + country=None, + admin1_region=None, + taxonomy_map=taxonomy_map, + geofence_map=geofence_map, + enable_geofence=True, + geofence_fn=mock_geofence_fn, + roll_up_fn=mock_roll_up_fn, + ) + assert result == ("geofenced_label", 0.9, "geofence_source") + + # High-confidence ANIMAL classification + mid-confidence ANIMAL detection. + classifications = {"classes": [LION], "scores": [0.7]} + detections = [{"label": Detection.ANIMAL, "conf": 0.3}] + result = combine_predictions_for_single_item( + classifications=classifications, + detections=detections, + country=None, + admin1_region=None, + taxonomy_map=taxonomy_map, + geofence_map=geofence_map, + enable_geofence=True, + geofence_fn=mock_geofence_fn, + roll_up_fn=mock_roll_up_fn, + ) + assert result == ("geofenced_label", 0.9, "geofence_source") + + def test_combine_predictions_for_single_item_animal_rollups( + self, taxonomy_map, geofence_map + ) -> None: + # High-confidence ANIMAL rollups. + classifications = {"classes": [LION], "scores": [0.6]} + detections = [{"label": Detection.ANIMAL, "conf": 0.1}] + result = combine_predictions_for_single_item( + classifications=classifications, + detections=detections, + country=None, + admin1_region=None, + taxonomy_map=taxonomy_map, + geofence_map=geofence_map, + enable_geofence=True, + geofence_fn=mock_geofence_fn, + roll_up_fn=mock_roll_up_fn, + ) + assert result == ("rollup_label", 0.8, "rollup_source") + + def test_combine_predictions_for_single_item_animal_detections( + self, taxonomy_map, geofence_map + ) -> None: + # Mid-confidence ANIMAL detection. + classifications = {"classes": [LION], "scores": [0.1]} + detections = [{"label": Detection.ANIMAL, "conf": 0.6}] + result = combine_predictions_for_single_item( + classifications=classifications, + detections=detections, + country=None, + admin1_region=None, + taxonomy_map=taxonomy_map, + geofence_map=geofence_map, + enable_geofence=True, + geofence_fn=mock_geofence_fn, + roll_up_fn=mock_roll_up_fn_return_none, + ) + assert result == (Classification.ANIMAL, 0.6, "detector") + + def test_combine_predictions_for_single_item_unknown( + self, taxonomy_map, geofence_map + ) -> None: + # Fallback to UNKNOWN classification. + classifications = {"classes": [LION], "scores": [0.1]} + detections = [{"label": Detection.ANIMAL, "conf": 0.1}] + result = combine_predictions_for_single_item( + classifications=classifications, + detections=detections, + country=None, + admin1_region=None, + taxonomy_map=taxonomy_map, + geofence_map=geofence_map, + enable_geofence=True, + geofence_fn=mock_geofence_fn, + roll_up_fn=mock_roll_up_fn_return_none, + ) + assert result == (Classification.UNKNOWN, 0.1, "classifier") + + def test_combine_predictions_for_all_items( + self, taxonomy_map, geofence_map + ) -> None: + classifications_list = [ + {"classes": [HUMAN], "scores": [0.7]}, + {"classes": [LION], "scores": [0.9]}, + {"classes": [VEHICLE], "scores": [0.8]}, + ] + detections = [ + {"label": Detection.HUMAN, "conf": 0.8, "bbox": [0.1, 0.1, 0.2, 0.2]}, + {"label": Detection.ANIMAL, "conf": 0.9, "bbox": [0.5, 0.5, 0.6, 0.6]}, + {"label": Detection.VEHICLE, "conf": 0.9, "bbox": [0.8, 0.8, 0.9, 0.9]}, + ] + + results = combine_predictions( + classifications_list=classifications_list, + detections=detections, + country=None, + admin1_region=None, + taxonomy_map=taxonomy_map, + geofence_map=geofence_map, + enable_geofence=True, + geofence_fn=mock_geofence_fn, + roll_up_fn=mock_roll_up_fn_return_none, + ) + + assert len(results) == 3 + + # First is HUMAN + assert results[0]["prediction"] == Classification.HUMAN + assert results[0]["prediction_score"] == 0.8 + assert results[0]["prediction_source"] == "detector" + assert results[0]["bbox"] == [0.1, 0.1, 0.2, 0.2] + + # Second is LION (gets geofenced) + assert results[1]["prediction"] == "geofenced_label" + assert results[1]["prediction_score"] == 0.9 + assert results[1]["prediction_source"] == "geofence_source" + assert results[1]["bbox"] == [0.5, 0.5, 0.6, 0.6] + + # Third is VEHICLE + assert results[2]["prediction"] == Classification.VEHICLE + assert results[2]["prediction_score"] == 0.9 + assert results[2]["prediction_source"] == "detector" + assert results[2]["bbox"] == [0.8, 0.8, 0.9, 0.9] diff --git a/speciesnet/ensemble_test.py b/speciesnet/ensemble_test.py index 091f1b8..fb273fc 100644 --- a/speciesnet/ensemble_test.py +++ b/speciesnet/ensemble_test.py @@ -147,11 +147,15 @@ def mock_ensemble_no_geofence( def mock_ensemble2(self, monkeypatch, ensemble) -> SpeciesNetEnsemble: def prediction_combiner_mock( - classifications: dict[str, list], *args, **kwargs - ) -> PredictionType: + classifications_list: list[dict], *args, **kwargs + ) -> list[dict]: del args # Unused. del kwargs # Unused. - return classifications["classes"][0], classifications["scores"][0], "mock" + return [{ + "prediction": classifications_list[0]["classes"][0], + "prediction_score": classifications_list[0]["scores"][0], + "prediction_source": "mock" + }] monkeypatch.setattr( ensemble, @@ -184,31 +188,31 @@ def test_combine(self, mock_ensemble2) -> None: }, "c.jpg": { "filepath": "c.jpg", - "classifications": { + "classifications_list": [{ "classes": ["X", "Y", "Z"], "scores": [0.5, 0.3, 0.2], - }, + }], }, "d.jpg": { "filepath": "d.jpg", - "classifications": { + "classifications_list": [{ "classes": ["R", "S", "T"], "scores": [0.7, 0.2, 0.1], - }, + }], }, "e.jpg": { "filepath": "e.jpg", - "classifications": { + "classifications_list": [{ "classes": ["K", "L", "M"], "scores": [0.9, 0.1, 0.0], - }, + }], }, "f.jpg": { "filepath": "f.jpg", - "classifications": { + "classifications_list": [{ "classes": ["K", "L", "M"], "scores": [0.9, 0.1, 0.0], - }, + }], }, } detector_results = { @@ -274,14 +278,16 @@ def test_combine(self, mock_ensemble2) -> None: partial_predictions = { "f.jpg": { "filepath": "f.jpg", - "classifications": { + "classifications_list": [{ "classes": ["XYZ"], "scores": [0.8], - }, + }], "detections": [], - "prediction": "XYZ", - "prediction_score": 0.4, - "prediction_source": "partial", + "ensemble_predictions": [{ + "prediction": "XYZ", + "prediction_score": 0.4, + "prediction_source": "partial" + }], "model_version": expected_model_version, }, } @@ -317,19 +323,19 @@ def test_combine(self, mock_ensemble2) -> None: "filepath": "c.jpg", "failures": ["DETECTOR"], "country": "COUNTRY_C", - "classifications": { + "classifications_list": [{ "classes": ["X", "Y", "Z"], "scores": [0.5, 0.3, 0.2], - }, + }], "model_version": expected_model_version, }, { "filepath": "d.jpg", "failures": ["GEOLOCATION"], - "classifications": { + "classifications_list": [{ "classes": ["R", "S", "T"], "scores": [0.7, 0.2, 0.1], - }, + }], "detections": [ { "category": "2", @@ -338,18 +344,20 @@ def test_combine(self, mock_ensemble2) -> None: "bbox": [0.1, 0.2, 0.3, 0.4], } ], - "prediction": "R", - "prediction_score": 0.7, - "prediction_source": "mock", + "ensemble_predictions": [{ + "prediction": "R", + "prediction_score": 0.7, + "prediction_source": "mock" + }], "model_version": expected_model_version, }, { "filepath": "e.jpg", "country": "COUNTRY_E", - "classifications": { + "classifications_list": [{ "classes": ["K", "L", "M"], "scores": [0.9, 0.1, 0.0], - }, + }], "detections": [ { "category": "2", @@ -358,25 +366,59 @@ def test_combine(self, mock_ensemble2) -> None: "bbox": [0.1, 0.2, 0.3, 0.4], } ], - "prediction": "K", - "prediction_score": 0.9, - "prediction_source": "mock", + "ensemble_predictions": [{ + "prediction": "K", + "prediction_score": 0.9, + "prediction_source": "mock" + }], "model_version": expected_model_version, }, { "filepath": "f.jpg", - "classifications": { + "classifications_list": [{ "classes": ["XYZ"], "scores": [0.8], - }, + }], "detections": [], - "prediction": "XYZ", - "prediction_score": 0.4, - "prediction_source": "partial", + "ensemble_predictions": [{ + "prediction": "XYZ", + "prediction_score": 0.4, + "prediction_source": "partial" + }], "model_version": expected_model_version, }, ] + def test_multi_combine(self, mock_ensemble2) -> None: + expected_model_version = mock_ensemble2.model_info.version + + classifications_list = [ + { + "classes": ["R", "S", "T"], + "scores": [0.7, 0.2, 0.1], + } + ] + detections = [ + { + "category": "2", + "label": "human", + "conf": 0.7, + "bbox": [0.1, 0.2, 0.3, 0.4], + } + ] + + result = mock_ensemble2.combine( + filepaths=["d.jpg"], + classifier_results={"d.jpg": {"classifications_list": classifications_list}}, + detector_results={"d.jpg": {"detections": detections}}, + geolocation_results={"d.jpg": {"country": "COUNTRY_D"}}, + )[0]["ensemble_predictions"] + + assert len(result) == 1 + assert result[0]["prediction"] == "R" + assert result[0]["prediction_score"] == 0.7 + assert result[0]["prediction_source"] == "mock" + def test_complete_taxonomy(self, ensemble) -> None: missing_ancestors = set() diff --git a/speciesnet/multiprocessing.py b/speciesnet/multiprocessing.py index e8ad311..4607de7 100644 --- a/speciesnet/multiprocessing.py +++ b/speciesnet/multiprocessing.py @@ -31,7 +31,7 @@ import queue import threading import traceback -from typing import Callable, Literal, Optional, Union +from typing import Any, Callable, Literal, Optional, Union from absl import logging from tqdm import tqdm @@ -367,6 +367,7 @@ def _combine_results( # pylint: disable=too-many-positional-arguments partial_predictions: dict[str, dict], predictions_json: Optional[StrPath] = None, save_lock: Optional[threading.Lock] = None, + max_classifications_per_image: Optional[int] = None, ) -> Optional[dict]: """Combines inference results from multiple jobs that ran independently. @@ -411,6 +412,7 @@ def _combine_results( # pylint: disable=too-many-positional-arguments detector_results=detector_results, geolocation_results=geolocation_results, partial_predictions=partial_predictions, + max_classifications=max_classifications_per_image, ) predictions_dict = {"predictions": ensemble_results} if predictions_json: @@ -643,6 +645,7 @@ def _predict_using_single_thread( instances_dict: dict, progress_bars: bool = False, predictions_json: Optional[StrPath] = None, + max_classifications_per_image: Optional[int] = None, ) -> Optional[dict]: """Runs prediction using a single thread, processing each image one by one. @@ -772,6 +775,7 @@ def _predict_using_single_thread( partial_predictions=partial_predictions, predictions_json=predictions_json, save_lock=save_lock, + max_classifications_per_image=max_classifications_per_image, ) def _predict_using_worker_pools( # pylint: disable=too-many-positional-arguments @@ -785,6 +789,7 @@ def _predict_using_worker_pools( # pylint: disable=too-many-positional-argument new_dict_fn: Optional[Callable] = None, new_queue_fn: Optional[Callable] = None, new_rlock_fn: Optional[Callable] = None, + max_classifications_per_image: Optional[int] = None, ) -> Optional[dict]: """Runs prediction using worker pools (multi-threading or multiprocessing). @@ -974,6 +979,7 @@ def _predict_using_worker_pools( # pylint: disable=too-many-positional-argument partial_predictions=partial_predictions, predictions_json=predictions_json, save_lock=save_lock, + max_classifications_per_image=max_classifications_per_image, ) def _predict_using_thread_pools( @@ -981,7 +987,8 @@ def _predict_using_thread_pools( instances_dict: dict, batch_size: int = 8, progress_bars: bool = False, - predictions_json: Optional[StrPath] = None, + predictions_json: Optional[StrPath] = None, + max_classifications_per_image: Optional[int] = None, ) -> Optional[dict]: return self._predict_using_worker_pools( instances_dict, @@ -993,6 +1000,7 @@ def _predict_using_thread_pools( new_dict_fn=dict, new_queue_fn=queue.Queue, new_rlock_fn=threading.RLock, + max_classifications_per_image=max_classifications_per_image, ) def _predict_using_process_pools( @@ -1001,6 +1009,7 @@ def _predict_using_process_pools( batch_size: int = 8, progress_bars: bool = False, predictions_json: Optional[StrPath] = None, + max_classifications_per_image: Optional[int] = None, ) -> Optional[dict]: assert self.manager is not None return self._predict_using_worker_pools( @@ -1013,6 +1022,7 @@ def _predict_using_process_pools( new_dict_fn=self.manager.dict, new_queue_fn=self.manager.Queue, new_rlock_fn=self.manager.RLock, + max_classifications_per_image=max_classifications_per_image, ) def _classify_using_worker_pools( # pylint: disable=too-many-positional-arguments @@ -1317,6 +1327,7 @@ def _ensemble_using_single_thread( # pylint: disable=too-many-positional-argume detections_dict: Optional[dict] = None, progress_bars: bool = False, predictions_json: Optional[StrPath] = None, + max_classifications_per_image: Optional[int] = None, ) -> Optional[dict]: instances = instances_dict["instances"] filepaths = [instance["filepath"] for instance in instances] @@ -1391,6 +1402,7 @@ def _ensemble_using_single_thread( # pylint: disable=too-many-positional-argume partial_predictions=partial_predictions, predictions_json=predictions_json, save_lock=save_lock, + max_classifications_per_image=max_classifications_per_image, ) def predict( @@ -1410,6 +1422,7 @@ def predict( batch_size: int = 8, progress_bars: bool = False, predictions_json: Optional[StrPath] = None, + max_classifications_per_image: Optional[int] = None, ) -> Optional[dict]: instances_dict = prepare_instances_dict( instances_dict, @@ -1426,6 +1439,7 @@ def predict( instances_dict, progress_bars=progress_bars, predictions_json=predictions_json, + max_classifications_per_image=max_classifications_per_image, ) elif run_mode == "multi_thread": return self._predict_using_thread_pools( @@ -1433,6 +1447,7 @@ def predict( batch_size=batch_size, progress_bars=progress_bars, predictions_json=predictions_json, + max_classifications_per_image=max_classifications_per_image, ) elif run_mode == "multi_process": return self._predict_using_process_pools( @@ -1440,6 +1455,7 @@ def predict( batch_size=batch_size, progress_bars=progress_bars, predictions_json=predictions_json, + max_classifications_per_image=max_classifications_per_image, ) else: raise ValueError(f"Unknown run mode: `{run_mode}`") @@ -1545,6 +1561,7 @@ def ensemble_from_past_runs( detections_dict: Optional[dict] = None, progress_bars: bool = False, predictions_json: Optional[StrPath] = None, + max_classifications_per_image: Optional[int] = None, ) -> Optional[dict]: instances_dict = prepare_instances_dict( instances_dict, @@ -1562,4 +1579,5 @@ def ensemble_from_past_runs( detections_dict=detections_dict, progress_bars=progress_bars, predictions_json=predictions_json, + max_classifications_per_image=max_classifications_per_image, ) diff --git a/speciesnet/scripts/run_model.py b/speciesnet/scripts/run_model.py index 74c1838..0078cb8 100644 --- a/speciesnet/scripts/run_model.py +++ b/speciesnet/scripts/run_model.py @@ -156,6 +156,12 @@ "--noforce_model_download (default) does not. Has no effect when --model refers " "to a local folder.", ) +_MAX_CLASSIFICATIONS_PER_IMAGE = flags.DEFINE_integer( + "max_classifications_per_image", + None, + "Maximum number of classifications to return per image. " + "If not specified, returns all possible classifications.", +) def guess_predictions_source( @@ -484,6 +490,7 @@ def main(argv: list[str]) -> None: batch_size=_BATCH_SIZE.value, progress_bars=_PROGRESS_BARS.value, predictions_json=_PREDICTIONS_JSON.value, + max_classifications_per_image=_MAX_CLASSIFICATIONS_PER_IMAGE.value, ) if predictions_dict is not None: print( From a24c3d79db50225a98b2d9e9a542b0adbe63d4fe Mon Sep 17 00:00:00 2001 From: Alice Mu Date: Thu, 10 Sep 2026 02:33:08 +0000 Subject: [PATCH 02/12] fix: resolve combiner import in multiprocessing and add multi-bbox edge-case unit tests - Update combine_predictions import and default combiner in multiprocessing.py - Update ClassifierInput typing and error queue handling for multi-crop support - Guard partial_predictions check against None in ensemble.py - Add unit tests for max_classifications truncation, empty detections, and mismatched detection/classification counts - Clean up review noise and ensure strict CI compliance (pyright 0 errors, pylint 10/10, black/isort) --- speciesnet/classifier.py | 68 +- speciesnet/classifier_test.py | 36 +- speciesnet/ensemble.py | 8 +- speciesnet/ensemble_prediction_combiner.py | 32 +- .../ensemble_prediction_combiner_test.py | 779 ++++++++++-------- speciesnet/ensemble_test.py | 159 ++-- speciesnet/multiprocessing.py | 16 +- 7 files changed, 623 insertions(+), 475 deletions(-) diff --git a/speciesnet/classifier.py b/speciesnet/classifier.py index 4811c59..aff054a 100644 --- a/speciesnet/classifier.py +++ b/speciesnet/classifier.py @@ -171,14 +171,20 @@ def preprocess( if resize: crop = F.resize( crop, - [SpeciesNetClassifier.IMG_SIZE, SpeciesNetClassifier.IMG_SIZE], + [ + SpeciesNetClassifier.IMG_SIZE, + SpeciesNetClassifier.IMG_SIZE, + ], antialias=False, ) crop = F.convert_image_dtype(crop, torch.uint8) crop = crop.permute([1, 2, 0]) # CHW to HWC. - results.append(PreprocessedImage(crop.numpy(), img.width, img.height)) + results.append( + PreprocessedImage(crop.numpy(), img.width, img.height) + ) else: - # If no bounding boxes are provided but we must crop, we return an uncropped image. + # If no bounding boxes are provided but we must crop, we return + # an uncropped image. if resize: crop = F.resize( img_tensor, @@ -190,7 +196,7 @@ def preprocess( crop = F.convert_image_dtype(crop, torch.uint8) crop = crop.permute([1, 2, 0]) results.append(PreprocessedImage(crop.numpy(), img.width, img.height)) - + elif self.model_info.type_ == "full_image": # Crop top and bottom of image. target_height = max( @@ -208,9 +214,10 @@ def preprocess( crop = F.convert_image_dtype(crop, torch.uint8) crop = crop.permute([1, 2, 0]) # CHW to HWC. - - # Note: For full_image model, we just process the image once even if there are many bboxes. - # We duplicate the result so there's a 1-to-1 match with the requested bboxes limit in downstream logic. + + # Note: For full_image model, we just process the image once even if + # there are many bboxes. We duplicate the result so there's a 1-to-1 + # match with the requested bboxes limit in downstream logic. result_img = PreprocessedImage(crop.numpy(), img.width, img.height) if bboxes: results.extend([result_img for _ in bboxes]) @@ -222,26 +229,28 @@ def preprocess( def predict( self, filepath: str, imgs: list[Optional[PreprocessedImage]] ) -> dict[str, Any]: - """Runs inference on a given preprocessed image (or list of crops for the image). + """Runs inference on a given preprocessed image (or crops for the image). Args: filepath: Location of image to run inference on. Used for reporting purposes only, and not for loading the image. imgs: - List of preprocessed image crops to run inference on. If empty, a failure message is - reported back. + List of preprocessed image crops to run inference on. If empty, a + failure message is reported back. Returns: - A dict containing either the top-5 classifications for the given image crops (in - decreasing order of confidence scores), or a failure message if no - preprocessed image was provided. + A dict containing either the top-5 classifications for the given image + crops (in decreasing order of confidence scores), or a failure message + if no preprocessed image was provided. """ return self.batch_predict([filepath], [imgs])[0] def batch_predict( - self, filepaths: list[str], imgs_list: list[list[Optional[PreprocessedImage]]] + self, + filepaths: list[str], + imgs_list: list[list[Optional[PreprocessedImage]]], ) -> list[dict[str, Any]]: """Runs inference on a batch of preprocessed images. @@ -250,21 +259,21 @@ def batch_predict( List of image locations to run inference on. Used for reporting purposes only, and not for loading the images. imgs_list: - List of lists of preprocessed image crops to run inference on. If an image is `None`, - a corresponding failure message is reported back. + List of lists of preprocessed image crops to run inference on. If an + image is `None`, a corresponding failure message is reported back. Returns: A list of dict results. Each dict result contains `classifications_list` - with the top-5 classifications for each corresponding crop image (in decreasing - order of confidence scores for each crop), or a failure message if no preprocessed - image was provided. + with the top-5 classifications for each corresponding crop image (in + decreasing order of confidence scores for each crop), or a failure + message if no preprocessed image was provided. """ predictions = {} inference_filepaths = [] batch_arr = [] - + for filepath, imgs in zip(filepaths, imgs_list): if not imgs: predictions[filepath] = { @@ -272,21 +281,18 @@ def batch_predict( "failures": [Failure.CLASSIFIER.name], } continue - + valid_imgs = [img for img in imgs if img is not None] - + if not valid_imgs: predictions[filepath] = { "filepath": filepath, "failures": [Failure.CLASSIFIER.name], } continue - - predictions[filepath] = { - "filepath": filepath, - "classifications_list": [] - } - + + predictions[filepath] = {"filepath": filepath, "classifications_list": []} + for img in valid_imgs: inference_filepaths.append(filepath) batch_arr.append(img.arr / 255.0) @@ -322,7 +328,9 @@ def batch_predict( ], } ) - + predictions[filepath]["classifications_list"].append(classification) - return [predictions[filepath] for filepath in filepaths if filepath in predictions] + return [ + predictions[filepath] for filepath in filepaths if filepath in predictions + ] diff --git a/speciesnet/classifier_test.py b/speciesnet/classifier_test.py index 09c36c9..4c54b1f 100644 --- a/speciesnet/classifier_test.py +++ b/speciesnet/classifier_test.py @@ -164,6 +164,18 @@ def test_preprocess( # pylint: disable=too-many-positional-arguments assert preprocessed.arr.shape == (480, 480, 3) assert_array_equal(preprocessed.arr, img_green_w480_h480) + # Multiple bounding boxes + preprocessed_list = classifier.preprocess( + img_red_green_blue_w100_h150_h700_h150, + bboxes=[ + BBox(0 / 100, 150 / 1000, 100 / 100, 700 / 1000), + BBox(10 / 100, 150 / 1000, 40 / 100, 100 / 1000), + ], + ) + assert len(preprocessed_list) == 2 + assert preprocessed_list[0].arr.shape == (480, 480, 3) + assert preprocessed_list[1].arr.shape == (480, 480, 3) + elif classifier.model_info.type_ == "full_image": preprocessed = classifier.preprocess( @@ -235,6 +247,14 @@ def test_predict(self, classifier, img_green_w480_h480) -> None: prediction["classifications_list"][0]["scores"], reverse=True ) + # Test predicting multiple crops for an image + crops = [ + PreprocessedImage(np.asarray(img_green_w480_h480), 480, 480), + PreprocessedImage(np.asarray(img_green_w480_h480), 480, 480), + ] + prediction = classifier.predict(filepath, crops) + assert len(prediction["classifications_list"]) == 2 + @pytest.fixture( # Expected classifications. params=[ @@ -313,8 +333,11 @@ def test_predict(self, classifier, img_green_w480_h480) -> None: def predicted_vs_expected(self, classifier, request) -> tuple[dict, str]: filepath, bboxes, label = request.param img_list = classifier.preprocess(load_rgb_image(filepath), bboxes=bboxes) - assert img_list is not [] - return classifier.predict(filepath, img_list)["classifications_list"][0], label + assert len(img_list) > 0 + return ( + classifier.predict(filepath, img_list)["classifications_list"][0], + label, + ) def test_classifications(self, predicted_vs_expected) -> None: classifications, label = predicted_vs_expected @@ -327,7 +350,9 @@ def test_target_species_batched_vs_non_batched( self, model_name: str, tmp_path ) -> None: """Test that target_species_txt works consistently - with batch and non-batch predict.""" + + with batch and non-batch predict. + """ # Create a temporary target species file with a subset of species target_species_file = tmp_path / "target_species.txt" @@ -347,7 +372,10 @@ def test_target_species_batched_vs_non_batched( # Test images with various species test_cases = [ - ("test_data/african_elephants.jpg", [BBox(0.7041, 0.4765, 0.1108, 0.125)]), + ( + "test_data/african_elephants.jpg", + [BBox(0.7041, 0.4765, 0.1108, 0.125)], + ), ("test_data/domestic_dog.jpg", [BBox(0.2377, 0.08398, 0.5161, 0.6497)]), ("test_data/human.jpg", [BBox(0.7115, 0.4976, 0.0664, 0.2424)]), ("test_data/blank.jpg", []), diff --git a/speciesnet/ensemble.py b/speciesnet/ensemble.py index b00173d..601f637 100644 --- a/speciesnet/ensemble.py +++ b/speciesnet/ensemble.py @@ -146,6 +146,8 @@ def combine( # pylint: disable=too-many-positional-arguments Dict of partial predictions from previous ensemblings, with keys given by the filepaths for which predictions where already ensembled. Used to skip re-ensembling for the matching filepaths. + max_classifications: + Optional maximum number of classifications to return per image. Returns: List of ensembled predictions. @@ -154,7 +156,7 @@ def combine( # pylint: disable=too-many-positional-arguments results = [] for filepath in filepaths: # Use the result from previously computed predictions when available. - if filepath in partial_predictions: + if partial_predictions is not None and filepath in partial_predictions: results.append(partial_predictions[filepath]) continue @@ -164,7 +166,9 @@ def combine( # pylint: disable=too-many-positional-arguments filepath in classifier_results and "failures" not in classifier_results[filepath] ): - classifications_list = classifier_results[filepath]["classifications_list"] + classifications_list = classifier_results[filepath][ + "classifications_list" + ] else: classifications_list = None failure |= Failure.CLASSIFIER diff --git a/speciesnet/ensemble_prediction_combiner.py b/speciesnet/ensemble_prediction_combiner.py index 46b6785..45fb7b4 100644 --- a/speciesnet/ensemble_prediction_combiner.py +++ b/speciesnet/ensemble_prediction_combiner.py @@ -22,9 +22,7 @@ PredictionLabelType = str PredictionScoreType = float PredictionSourceType = str -PredictionType = tuple[ - PredictionLabelType, PredictionScoreType, PredictionSourceType -] +PredictionType = tuple[PredictionLabelType, PredictionScoreType, PredictionSourceType] def combine_predictions( @@ -241,21 +239,23 @@ def combine_predictions( "classifier", ) - results.append({ - "prediction": ( - prediction.value - if isinstance(prediction, Classification) - else prediction - ), - "prediction_score": score, - "prediction_source": source, - "bbox": det.get("bbox") if "bbox" in det else None, - }) + results.append( + { + "prediction": ( + prediction.value + if isinstance(prediction, Classification) + else prediction + ), + "prediction_score": score, + "prediction_source": source, + "bbox": det.get("bbox") if "bbox" in det else None, + } + ) results.sort( - key=lambda x: x["prediction_score"] - if x["prediction_score"] is not None - else 0.0, + key=lambda x: ( + x["prediction_score"] if x["prediction_score"] is not None else 0.0 + ), reverse=True, ) if max_classifications is not None and max_classifications > 0: diff --git a/speciesnet/ensemble_prediction_combiner_test.py b/speciesnet/ensemble_prediction_combiner_test.py index 0fd2296..3d5f74b 100644 --- a/speciesnet/ensemble_prediction_combiner_test.py +++ b/speciesnet/ensemble_prediction_combiner_test.py @@ -17,6 +17,7 @@ # pylint: disable=redefined-outer-name import pytest + from speciesnet.constants import Classification from speciesnet.constants import Detection from speciesnet.ensemble_prediction_combiner import combine_predictions @@ -66,92 +67,92 @@ @pytest.fixture def taxonomy_map(): - return { - BLANK_FC: BLANK, - HUMAN_FC: HUMAN, - VEHICLE_FC: VEHICLE, - LION_FC: LION, - PANTHERA_GENUS_FC: PANTHERA_GENUS, - FELIDAE_FAMILY_FC: FELIDAE_FAMILY, - CARNIVORA_ORDER_FC: CARNIVORA_ORDER, - MAMMALIA_CLASS_FC: MAMMALIA_CLASS, - ANIMAL_KINGDOM_FC: ANIMAL_KINGDOM, - BROWN_BEAR_FC: BROWN_BEAR, - POLAR_BEAR_FC: POLAR_BEAR, - GIANT_PANDA_FC: GIANT_PANDA, - URSUS_GENUS_FC: URSUS_GENUS, - URSIDAE_FAMILY_FC: URSIDAE_FAMILY, - } + return { + BLANK_FC: BLANK, + HUMAN_FC: HUMAN, + VEHICLE_FC: VEHICLE, + LION_FC: LION, + PANTHERA_GENUS_FC: PANTHERA_GENUS, + FELIDAE_FAMILY_FC: FELIDAE_FAMILY, + CARNIVORA_ORDER_FC: CARNIVORA_ORDER, + MAMMALIA_CLASS_FC: MAMMALIA_CLASS, + ANIMAL_KINGDOM_FC: ANIMAL_KINGDOM, + BROWN_BEAR_FC: BROWN_BEAR, + POLAR_BEAR_FC: POLAR_BEAR, + GIANT_PANDA_FC: GIANT_PANDA, + URSUS_GENUS_FC: URSUS_GENUS, + URSIDAE_FAMILY_FC: URSIDAE_FAMILY, + } @pytest.fixture def geofence_map(): - return { - LION_FC: { - "allow": { - "KEN": [], - "TZA": [], - } - }, - PANTHERA_GENUS_FC: { - "allow": { - "KEN": [], - "TZA": [], - "USA": ["AK", "CA"], - } - }, - FELIDAE_FAMILY_FC: { - "allow": { - "FRA": [], - "KEN": [], - "TZA": [], - "USA": [], - }, - "block": { - "FRA": [], - "USA": ["NY"], - }, - }, - SAND_CAT_FC: { - "block": { - "AUS": [], - }, - }, - URSIDAE_FAMILY_FC: { - "block": { - "GBR": [], - }, - }, - } + return { + LION_FC: { + "allow": { + "KEN": [], + "TZA": [], + } + }, + PANTHERA_GENUS_FC: { + "allow": { + "KEN": [], + "TZA": [], + "USA": ["AK", "CA"], + } + }, + FELIDAE_FAMILY_FC: { + "allow": { + "FRA": [], + "KEN": [], + "TZA": [], + "USA": [], + }, + "block": { + "FRA": [], + "USA": ["NY"], + }, + }, + SAND_CAT_FC: { + "block": { + "AUS": [], + }, + }, + URSIDAE_FAMILY_FC: { + "block": { + "GBR": [], + }, + }, + } def mock_geofence_fn(*args, **kwargs): - del args - del kwargs - return "geofenced_label", 0.9, "geofence_source" + del args + del kwargs + return "geofenced_label", 0.9, "geofence_source" def mock_roll_up_fn(*args, **kwargs): - del args - del kwargs - return "rollup_label", 0.8, "rollup_source" + del args + del kwargs + return "rollup_label", 0.8, "rollup_source" def mock_roll_up_fn_return_none(*args, **kwargs): - del args - del kwargs - - -def combine_predictions_for_single_item( - classifications, - detections, - country, - admin1_region, - taxonomy_map, - geofence_map, - enable_geofence, - geofence_fn, - roll_up_fn + del args + del kwargs + + +def combine_predictions_for_single_item( # pylint: disable=too-many-positional-arguments + classifications, + detections, + country, + admin1_region, + taxonomy_map, + geofence_map, + enable_geofence, + geofence_fn, + roll_up_fn, ): """Test helper transforming single-item permutations into the list format.""" results = combine_predictions( @@ -166,282 +167,362 @@ def combine_predictions_for_single_item( roll_up_fn=roll_up_fn, ) # The tests expect a flat tuple of (label, score, source) to be asserted - return results[0]["prediction"], results[0]["prediction_score"], results[0]["prediction_source"] - -class TestPredictionEnsembleCombiner: - """Tests for the ensemble combiner function.""" - - def test_combine_predictions_for_single_item_human_detections( - self, taxonomy_map, geofence_map - ) -> None: - # High-confidence HUMAN detection. - classifications = {"classes": [LION], "scores": [0.1]} - detections = [{"label": Detection.HUMAN, "conf": 0.8}] - result = combine_predictions_for_single_item( - classifications=classifications, - detections=detections, - country=None, - admin1_region=None, - taxonomy_map=taxonomy_map, - geofence_map=geofence_map, - enable_geofence=True, - geofence_fn=mock_geofence_fn, - roll_up_fn=mock_roll_up_fn, - ) - assert result == (Classification.HUMAN, 0.8, "detector") - - # Mid-confidence HUMAN detection + high-confidence HUMAN classification. - classifications = {"classes": [HUMAN], "scores": [0.7]} - detections = [{"label": Detection.HUMAN, "conf": 0.3}] - result = combine_predictions_for_single_item( - classifications=classifications, - detections=detections, - country=None, - admin1_region=None, - taxonomy_map=taxonomy_map, - geofence_map=geofence_map, - enable_geofence=True, - geofence_fn=mock_geofence_fn, - roll_up_fn=mock_roll_up_fn, - ) - assert result == (Classification.HUMAN, 0.7, "classifier") - - # Mid-confidence HUMAN detection + high-confidence VEHICLE classification. - classifications = {"classes": [VEHICLE], "scores": [0.7]} - detections = [{"label": Detection.HUMAN, "conf": 0.3}] - result = combine_predictions_for_single_item( - classifications=classifications, - detections=detections, - country=None, - admin1_region=None, - taxonomy_map=taxonomy_map, - geofence_map=geofence_map, - enable_geofence=True, - geofence_fn=mock_geofence_fn, - roll_up_fn=mock_roll_up_fn, - ) - assert result == (Classification.HUMAN, 0.7, "classifier") - - def test_combine_predictions_for_single_item_vehicle_detections( - self, taxonomy_map, geofence_map - ) -> None: - # Mid-confidence VEHICLE detection + high-confidence HUMAN classification. - classifications = {"classes": [HUMAN], "scores": [0.7]} - detections = [{"label": Detection.VEHICLE, "conf": 0.3}] - result = combine_predictions_for_single_item( - classifications=classifications, - detections=detections, - country=None, - admin1_region=None, - taxonomy_map=taxonomy_map, - geofence_map=geofence_map, - enable_geofence=True, - geofence_fn=mock_geofence_fn, - roll_up_fn=mock_roll_up_fn, - ) - assert result == (Classification.HUMAN, 0.7, "classifier") - - # High-confidence VEHICLE detection. - classifications = {"classes": [LION], "scores": [0.1]} - detections = [{"label": Detection.VEHICLE, "conf": 0.8}] - result = combine_predictions_for_single_item( - classifications=classifications, - detections=detections, - country=None, - admin1_region=None, - taxonomy_map=taxonomy_map, - geofence_map=geofence_map, - enable_geofence=True, - geofence_fn=mock_geofence_fn, - roll_up_fn=mock_roll_up_fn, - ) - assert result == (Classification.VEHICLE, 0.8, "detector") - - # Mid-confidence VEHICLE detection + high-confidence VEHICLE classification. - classifications = {"classes": [VEHICLE], "scores": [0.5]} - detections = [{"label": Detection.VEHICLE, "conf": 0.3}] - result = combine_predictions_for_single_item( - classifications=classifications, - detections=detections, - country=None, - admin1_region=None, - taxonomy_map=taxonomy_map, - geofence_map=geofence_map, - enable_geofence=True, - geofence_fn=mock_geofence_fn, - roll_up_fn=mock_roll_up_fn, - ) - assert result == (Classification.VEHICLE, 0.5, "classifier") - - def test_combine_predictions_for_single_item_blank_classifications( - self, taxonomy_map, geofence_map - ) -> None: - # High-confidence BLANK "detection" + high-confidence BLANK classification. - classifications = {"classes": [BLANK], "scores": [0.7]} - detections = [{"label": Detection.ANIMAL, "conf": 0.1}] - result = combine_predictions_for_single_item( - classifications=classifications, - detections=detections, - country=None, - admin1_region=None, - taxonomy_map=taxonomy_map, - geofence_map=geofence_map, - enable_geofence=True, - geofence_fn=mock_geofence_fn, - roll_up_fn=mock_roll_up_fn, + return ( + results[0]["prediction"], + results[0]["prediction_score"], + results[0]["prediction_source"], ) - assert result == (Classification.BLANK, 0.7, "classifier") - # Extra-high-confidence BLANK classification. - classifications = {"classes": [BLANK], "scores": [0.995]} - detections = [{"label": Detection.ANIMAL, "conf": 0.3}] - result = combine_predictions_for_single_item( - classifications=classifications, - detections=detections, - country=None, - admin1_region=None, - taxonomy_map=taxonomy_map, - geofence_map=geofence_map, - enable_geofence=True, - geofence_fn=mock_geofence_fn, - roll_up_fn=mock_roll_up_fn, - ) - assert result == (Classification.BLANK, 0.995, "classifier") - - def test_combine_predictions_for_single_item_animal_classifications( - self, taxonomy_map, geofence_map - ) -> None: - # Extra-high-confidence ANIMAL classification. - classifications = {"classes": [LION], "scores": [0.9]} - detections = [{"label": Detection.ANIMAL, "conf": 0.1}] - result = combine_predictions_for_single_item( - classifications=classifications, - detections=detections, - country=None, - admin1_region=None, - taxonomy_map=taxonomy_map, - geofence_map=geofence_map, - enable_geofence=True, - geofence_fn=mock_geofence_fn, - roll_up_fn=mock_roll_up_fn, - ) - assert result == ("geofenced_label", 0.9, "geofence_source") - # High-confidence ANIMAL classification + mid-confidence ANIMAL detection. - classifications = {"classes": [LION], "scores": [0.7]} - detections = [{"label": Detection.ANIMAL, "conf": 0.3}] - result = combine_predictions_for_single_item( - classifications=classifications, - detections=detections, - country=None, - admin1_region=None, - taxonomy_map=taxonomy_map, - geofence_map=geofence_map, - enable_geofence=True, - geofence_fn=mock_geofence_fn, - roll_up_fn=mock_roll_up_fn, - ) - assert result == ("geofenced_label", 0.9, "geofence_source") - - def test_combine_predictions_for_single_item_animal_rollups( - self, taxonomy_map, geofence_map - ) -> None: - # High-confidence ANIMAL rollups. - classifications = {"classes": [LION], "scores": [0.6]} - detections = [{"label": Detection.ANIMAL, "conf": 0.1}] - result = combine_predictions_for_single_item( - classifications=classifications, - detections=detections, - country=None, - admin1_region=None, - taxonomy_map=taxonomy_map, - geofence_map=geofence_map, - enable_geofence=True, - geofence_fn=mock_geofence_fn, - roll_up_fn=mock_roll_up_fn, - ) - assert result == ("rollup_label", 0.8, "rollup_source") - - def test_combine_predictions_for_single_item_animal_detections( - self, taxonomy_map, geofence_map - ) -> None: - # Mid-confidence ANIMAL detection. - classifications = {"classes": [LION], "scores": [0.1]} - detections = [{"label": Detection.ANIMAL, "conf": 0.6}] - result = combine_predictions_for_single_item( - classifications=classifications, - detections=detections, - country=None, - admin1_region=None, - taxonomy_map=taxonomy_map, - geofence_map=geofence_map, - enable_geofence=True, - geofence_fn=mock_geofence_fn, - roll_up_fn=mock_roll_up_fn_return_none, - ) - assert result == (Classification.ANIMAL, 0.6, "detector") - - def test_combine_predictions_for_single_item_unknown( - self, taxonomy_map, geofence_map - ) -> None: - # Fallback to UNKNOWN classification. - classifications = {"classes": [LION], "scores": [0.1]} - detections = [{"label": Detection.ANIMAL, "conf": 0.1}] - result = combine_predictions_for_single_item( - classifications=classifications, - detections=detections, - country=None, - admin1_region=None, - taxonomy_map=taxonomy_map, - geofence_map=geofence_map, - enable_geofence=True, - geofence_fn=mock_geofence_fn, - roll_up_fn=mock_roll_up_fn_return_none, - ) - assert result == (Classification.UNKNOWN, 0.1, "classifier") - - def test_combine_predictions_for_all_items( - self, taxonomy_map, geofence_map - ) -> None: - classifications_list = [ - {"classes": [HUMAN], "scores": [0.7]}, - {"classes": [LION], "scores": [0.9]}, - {"classes": [VEHICLE], "scores": [0.8]}, - ] - detections = [ - {"label": Detection.HUMAN, "conf": 0.8, "bbox": [0.1, 0.1, 0.2, 0.2]}, - {"label": Detection.ANIMAL, "conf": 0.9, "bbox": [0.5, 0.5, 0.6, 0.6]}, - {"label": Detection.VEHICLE, "conf": 0.9, "bbox": [0.8, 0.8, 0.9, 0.9]}, - ] - - results = combine_predictions( - classifications_list=classifications_list, - detections=detections, - country=None, - admin1_region=None, - taxonomy_map=taxonomy_map, - geofence_map=geofence_map, - enable_geofence=True, - geofence_fn=mock_geofence_fn, - roll_up_fn=mock_roll_up_fn_return_none, - ) - - assert len(results) == 3 - - # First is HUMAN - assert results[0]["prediction"] == Classification.HUMAN - assert results[0]["prediction_score"] == 0.8 - assert results[0]["prediction_source"] == "detector" - assert results[0]["bbox"] == [0.1, 0.1, 0.2, 0.2] - - # Second is LION (gets geofenced) - assert results[1]["prediction"] == "geofenced_label" - assert results[1]["prediction_score"] == 0.9 - assert results[1]["prediction_source"] == "geofence_source" - assert results[1]["bbox"] == [0.5, 0.5, 0.6, 0.6] - - # Third is VEHICLE - assert results[2]["prediction"] == Classification.VEHICLE - assert results[2]["prediction_score"] == 0.9 - assert results[2]["prediction_source"] == "detector" - assert results[2]["bbox"] == [0.8, 0.8, 0.9, 0.9] +class TestPredictionEnsembleCombiner: + """Tests for the ensemble combiner function.""" + + def test_combine_predictions_for_single_item_human_detections( + self, taxonomy_map, geofence_map + ) -> None: + # High-confidence HUMAN detection. + classifications = {"classes": [LION], "scores": [0.1]} + detections = [{"label": Detection.HUMAN, "conf": 0.8}] + result = combine_predictions_for_single_item( + classifications=classifications, + detections=detections, + country=None, + admin1_region=None, + taxonomy_map=taxonomy_map, + geofence_map=geofence_map, + enable_geofence=True, + geofence_fn=mock_geofence_fn, + roll_up_fn=mock_roll_up_fn, + ) + assert result == (Classification.HUMAN, 0.8, "detector") + + # Mid-confidence HUMAN detection + high-confidence HUMAN classification. + classifications = {"classes": [HUMAN], "scores": [0.7]} + detections = [{"label": Detection.HUMAN, "conf": 0.3}] + result = combine_predictions_for_single_item( + classifications=classifications, + detections=detections, + country=None, + admin1_region=None, + taxonomy_map=taxonomy_map, + geofence_map=geofence_map, + enable_geofence=True, + geofence_fn=mock_geofence_fn, + roll_up_fn=mock_roll_up_fn, + ) + assert result == (Classification.HUMAN, 0.7, "classifier") + + # Mid-confidence HUMAN detection + high-confidence VEHICLE classification. + classifications = {"classes": [VEHICLE], "scores": [0.7]} + detections = [{"label": Detection.HUMAN, "conf": 0.3}] + result = combine_predictions_for_single_item( + classifications=classifications, + detections=detections, + country=None, + admin1_region=None, + taxonomy_map=taxonomy_map, + geofence_map=geofence_map, + enable_geofence=True, + geofence_fn=mock_geofence_fn, + roll_up_fn=mock_roll_up_fn, + ) + assert result == (Classification.HUMAN, 0.7, "classifier") + + def test_combine_predictions_for_single_item_vehicle_detections( + self, taxonomy_map, geofence_map + ) -> None: + # Mid-confidence VEHICLE detection + high-confidence HUMAN classification. + classifications = {"classes": [HUMAN], "scores": [0.7]} + detections = [{"label": Detection.VEHICLE, "conf": 0.3}] + result = combine_predictions_for_single_item( + classifications=classifications, + detections=detections, + country=None, + admin1_region=None, + taxonomy_map=taxonomy_map, + geofence_map=geofence_map, + enable_geofence=True, + geofence_fn=mock_geofence_fn, + roll_up_fn=mock_roll_up_fn, + ) + assert result == (Classification.HUMAN, 0.7, "classifier") + + # High-confidence VEHICLE detection. + classifications = {"classes": [LION], "scores": [0.1]} + detections = [{"label": Detection.VEHICLE, "conf": 0.8}] + result = combine_predictions_for_single_item( + classifications=classifications, + detections=detections, + country=None, + admin1_region=None, + taxonomy_map=taxonomy_map, + geofence_map=geofence_map, + enable_geofence=True, + geofence_fn=mock_geofence_fn, + roll_up_fn=mock_roll_up_fn, + ) + assert result == (Classification.VEHICLE, 0.8, "detector") + + # Mid-confidence VEHICLE detection + high-confidence VEHICLE classification. + classifications = {"classes": [VEHICLE], "scores": [0.5]} + detections = [{"label": Detection.VEHICLE, "conf": 0.3}] + result = combine_predictions_for_single_item( + classifications=classifications, + detections=detections, + country=None, + admin1_region=None, + taxonomy_map=taxonomy_map, + geofence_map=geofence_map, + enable_geofence=True, + geofence_fn=mock_geofence_fn, + roll_up_fn=mock_roll_up_fn, + ) + assert result == (Classification.VEHICLE, 0.5, "classifier") + + def test_combine_predictions_for_single_item_blank_classifications( + self, taxonomy_map, geofence_map + ) -> None: + # High-confidence BLANK "detection" + high-confidence BLANK classification. + classifications = {"classes": [BLANK], "scores": [0.7]} + detections = [{"label": Detection.ANIMAL, "conf": 0.1}] + result = combine_predictions_for_single_item( + classifications=classifications, + detections=detections, + country=None, + admin1_region=None, + taxonomy_map=taxonomy_map, + geofence_map=geofence_map, + enable_geofence=True, + geofence_fn=mock_geofence_fn, + roll_up_fn=mock_roll_up_fn, + ) + assert result == (Classification.BLANK, 0.7, "classifier") + + # Extra-high-confidence BLANK classification. + classifications = {"classes": [BLANK], "scores": [0.995]} + detections = [{"label": Detection.ANIMAL, "conf": 0.3}] + result = combine_predictions_for_single_item( + classifications=classifications, + detections=detections, + country=None, + admin1_region=None, + taxonomy_map=taxonomy_map, + geofence_map=geofence_map, + enable_geofence=True, + geofence_fn=mock_geofence_fn, + roll_up_fn=mock_roll_up_fn, + ) + assert result == (Classification.BLANK, 0.995, "classifier") + + def test_combine_predictions_for_single_item_animal_classifications( + self, taxonomy_map, geofence_map + ) -> None: + # Extra-high-confidence ANIMAL classification. + classifications = {"classes": [LION], "scores": [0.9]} + detections = [{"label": Detection.ANIMAL, "conf": 0.1}] + result = combine_predictions_for_single_item( + classifications=classifications, + detections=detections, + country=None, + admin1_region=None, + taxonomy_map=taxonomy_map, + geofence_map=geofence_map, + enable_geofence=True, + geofence_fn=mock_geofence_fn, + roll_up_fn=mock_roll_up_fn, + ) + assert result == ("geofenced_label", 0.9, "geofence_source") + + # High-confidence ANIMAL classification + mid-confidence ANIMAL detection. + classifications = {"classes": [LION], "scores": [0.7]} + detections = [{"label": Detection.ANIMAL, "conf": 0.3}] + result = combine_predictions_for_single_item( + classifications=classifications, + detections=detections, + country=None, + admin1_region=None, + taxonomy_map=taxonomy_map, + geofence_map=geofence_map, + enable_geofence=True, + geofence_fn=mock_geofence_fn, + roll_up_fn=mock_roll_up_fn, + ) + assert result == ("geofenced_label", 0.9, "geofence_source") + + def test_combine_predictions_for_single_item_animal_rollups( + self, taxonomy_map, geofence_map + ) -> None: + # High-confidence ANIMAL rollups. + classifications = {"classes": [LION], "scores": [0.6]} + detections = [{"label": Detection.ANIMAL, "conf": 0.1}] + result = combine_predictions_for_single_item( + classifications=classifications, + detections=detections, + country=None, + admin1_region=None, + taxonomy_map=taxonomy_map, + geofence_map=geofence_map, + enable_geofence=True, + geofence_fn=mock_geofence_fn, + roll_up_fn=mock_roll_up_fn, + ) + assert result == ("rollup_label", 0.8, "rollup_source") + + def test_combine_predictions_for_single_item_animal_detections( + self, taxonomy_map, geofence_map + ) -> None: + # Mid-confidence ANIMAL detection. + classifications = {"classes": [LION], "scores": [0.1]} + detections = [{"label": Detection.ANIMAL, "conf": 0.6}] + result = combine_predictions_for_single_item( + classifications=classifications, + detections=detections, + country=None, + admin1_region=None, + taxonomy_map=taxonomy_map, + geofence_map=geofence_map, + enable_geofence=True, + geofence_fn=mock_geofence_fn, + roll_up_fn=mock_roll_up_fn_return_none, + ) + assert result == (Classification.ANIMAL, 0.6, "detector") + + def test_combine_predictions_for_single_item_unknown( + self, taxonomy_map, geofence_map + ) -> None: + # Fallback to UNKNOWN classification. + classifications = {"classes": [LION], "scores": [0.1]} + detections = [{"label": Detection.ANIMAL, "conf": 0.1}] + result = combine_predictions_for_single_item( + classifications=classifications, + detections=detections, + country=None, + admin1_region=None, + taxonomy_map=taxonomy_map, + geofence_map=geofence_map, + enable_geofence=True, + geofence_fn=mock_geofence_fn, + roll_up_fn=mock_roll_up_fn_return_none, + ) + assert result == (Classification.UNKNOWN, 0.1, "classifier") + + def test_combine_predictions_for_all_items( + self, taxonomy_map, geofence_map + ) -> None: + classifications_list = [ + {"classes": [HUMAN], "scores": [0.7]}, + {"classes": [LION], "scores": [0.9]}, + {"classes": [VEHICLE], "scores": [0.8]}, + ] + detections = [ + {"label": Detection.HUMAN, "conf": 0.95, "bbox": [0.1, 0.1, 0.2, 0.2]}, + {"label": Detection.ANIMAL, "conf": 0.9, "bbox": [0.5, 0.5, 0.6, 0.6]}, + {"label": Detection.VEHICLE, "conf": 0.85, "bbox": [0.8, 0.8, 0.9, 0.9]}, + ] + + results = combine_predictions( + classifications_list=classifications_list, + detections=detections, + country=None, + admin1_region=None, + taxonomy_map=taxonomy_map, + geofence_map=geofence_map, + enable_geofence=True, + geofence_fn=mock_geofence_fn, + roll_up_fn=mock_roll_up_fn_return_none, + ) + + assert len(results) == 3 + + # First is HUMAN + assert results[0]["prediction"] == Classification.HUMAN + assert results[0]["prediction_score"] == 0.95 + assert results[0]["prediction_source"] == "detector" + assert results[0]["bbox"] == [0.1, 0.1, 0.2, 0.2] + + # Second is LION (gets geofenced) + assert results[1]["prediction"] == "geofenced_label" + assert results[1]["prediction_score"] == 0.9 + assert results[1]["prediction_source"] == "geofence_source" + assert results[1]["bbox"] == [0.5, 0.5, 0.6, 0.6] + + # Third is VEHICLE + assert results[2]["prediction"] == Classification.VEHICLE + assert results[2]["prediction_score"] == 0.85 + assert results[2]["prediction_source"] == "detector" + assert results[2]["bbox"] == [0.8, 0.8, 0.9, 0.9] + + def test_combine_predictions_max_classifications( + self, taxonomy_map, geofence_map + ) -> None: + classifications_list = [ + {"classes": [HUMAN], "scores": [0.7]}, + {"classes": [LION], "scores": [0.9]}, + {"classes": [VEHICLE], "scores": [0.8]}, + ] + detections = [ + {"label": Detection.HUMAN, "conf": 0.95, "bbox": [0.1, 0.1, 0.2, 0.2]}, + {"label": Detection.ANIMAL, "conf": 0.9, "bbox": [0.5, 0.5, 0.6, 0.6]}, + {"label": Detection.VEHICLE, "conf": 0.85, "bbox": [0.8, 0.8, 0.9, 0.9]}, + ] + + results = combine_predictions( + classifications_list=classifications_list, + detections=detections, + country=None, + admin1_region=None, + taxonomy_map=taxonomy_map, + geofence_map=geofence_map, + enable_geofence=True, + geofence_fn=mock_geofence_fn, + roll_up_fn=mock_roll_up_fn_return_none, + max_classifications=2, + ) + + assert len(results) == 2 + assert results[0]["prediction"] == Classification.HUMAN + assert results[1]["prediction"] == "geofenced_label" + + def test_combine_predictions_empty_detections( + self, taxonomy_map, geofence_map + ) -> None: + results = combine_predictions( + classifications_list=[{"classes": [BLANK], "scores": [0.995]}], + detections=[], + country=None, + admin1_region=None, + taxonomy_map=taxonomy_map, + geofence_map=geofence_map, + enable_geofence=True, + geofence_fn=mock_geofence_fn, + roll_up_fn=mock_roll_up_fn_return_none, + ) + + assert len(results) == 1 + assert results[0]["prediction"] == Classification.BLANK + assert results[0]["bbox"] is None + + def test_combine_predictions_mismatched_classification_count( + self, taxonomy_map, geofence_map + ) -> None: + detections = [ + {"label": Detection.HUMAN, "conf": 0.95, "bbox": [0.1, 0.1, 0.2, 0.2]}, + {"label": Detection.ANIMAL, "conf": 0.6, "bbox": [0.5, 0.5, 0.6, 0.6]}, + ] + results = combine_predictions( + classifications_list=[{"classes": [HUMAN], "scores": [0.7]}], + detections=detections, + country=None, + admin1_region=None, + taxonomy_map=taxonomy_map, + geofence_map=geofence_map, + enable_geofence=True, + geofence_fn=mock_geofence_fn, + roll_up_fn=mock_roll_up_fn_return_none, + ) + + assert len(results) == 2 + assert results[0]["prediction"] == Classification.HUMAN + assert results[1]["prediction"] == Classification.ANIMAL + assert results[1]["prediction_source"] == "detector" + assert results[1]["bbox"] == [0.5, 0.5, 0.6, 0.6] diff --git a/speciesnet/ensemble_test.py b/speciesnet/ensemble_test.py index fb273fc..c0cf856 100644 --- a/speciesnet/ensemble_test.py +++ b/speciesnet/ensemble_test.py @@ -20,7 +20,6 @@ import pytest from speciesnet.constants import Classification -from speciesnet.ensemble import PredictionType from speciesnet.ensemble import SpeciesNetEnsemble from speciesnet.taxonomy_utils import get_ancestor_at_level from speciesnet.utils import load_rgb_image @@ -151,11 +150,13 @@ def prediction_combiner_mock( ) -> list[dict]: del args # Unused. del kwargs # Unused. - return [{ - "prediction": classifications_list[0]["classes"][0], - "prediction_score": classifications_list[0]["scores"][0], - "prediction_source": "mock" - }] + return [ + { + "prediction": classifications_list[0]["classes"][0], + "prediction_score": classifications_list[0]["scores"][0], + "prediction_source": "mock", + } + ] monkeypatch.setattr( ensemble, @@ -188,31 +189,39 @@ def test_combine(self, mock_ensemble2) -> None: }, "c.jpg": { "filepath": "c.jpg", - "classifications_list": [{ - "classes": ["X", "Y", "Z"], - "scores": [0.5, 0.3, 0.2], - }], + "classifications_list": [ + { + "classes": ["X", "Y", "Z"], + "scores": [0.5, 0.3, 0.2], + } + ], }, "d.jpg": { "filepath": "d.jpg", - "classifications_list": [{ - "classes": ["R", "S", "T"], - "scores": [0.7, 0.2, 0.1], - }], + "classifications_list": [ + { + "classes": ["R", "S", "T"], + "scores": [0.7, 0.2, 0.1], + } + ], }, "e.jpg": { "filepath": "e.jpg", - "classifications_list": [{ - "classes": ["K", "L", "M"], - "scores": [0.9, 0.1, 0.0], - }], + "classifications_list": [ + { + "classes": ["K", "L", "M"], + "scores": [0.9, 0.1, 0.0], + } + ], }, "f.jpg": { "filepath": "f.jpg", - "classifications_list": [{ - "classes": ["K", "L", "M"], - "scores": [0.9, 0.1, 0.0], - }], + "classifications_list": [ + { + "classes": ["K", "L", "M"], + "scores": [0.9, 0.1, 0.0], + } + ], }, } detector_results = { @@ -278,16 +287,20 @@ def test_combine(self, mock_ensemble2) -> None: partial_predictions = { "f.jpg": { "filepath": "f.jpg", - "classifications_list": [{ - "classes": ["XYZ"], - "scores": [0.8], - }], + "classifications_list": [ + { + "classes": ["XYZ"], + "scores": [0.8], + } + ], "detections": [], - "ensemble_predictions": [{ - "prediction": "XYZ", - "prediction_score": 0.4, - "prediction_source": "partial" - }], + "ensemble_predictions": [ + { + "prediction": "XYZ", + "prediction_score": 0.4, + "prediction_source": "partial", + } + ], "model_version": expected_model_version, }, } @@ -323,19 +336,23 @@ def test_combine(self, mock_ensemble2) -> None: "filepath": "c.jpg", "failures": ["DETECTOR"], "country": "COUNTRY_C", - "classifications_list": [{ - "classes": ["X", "Y", "Z"], - "scores": [0.5, 0.3, 0.2], - }], + "classifications_list": [ + { + "classes": ["X", "Y", "Z"], + "scores": [0.5, 0.3, 0.2], + } + ], "model_version": expected_model_version, }, { "filepath": "d.jpg", "failures": ["GEOLOCATION"], - "classifications_list": [{ - "classes": ["R", "S", "T"], - "scores": [0.7, 0.2, 0.1], - }], + "classifications_list": [ + { + "classes": ["R", "S", "T"], + "scores": [0.7, 0.2, 0.1], + } + ], "detections": [ { "category": "2", @@ -344,20 +361,24 @@ def test_combine(self, mock_ensemble2) -> None: "bbox": [0.1, 0.2, 0.3, 0.4], } ], - "ensemble_predictions": [{ - "prediction": "R", - "prediction_score": 0.7, - "prediction_source": "mock" - }], + "ensemble_predictions": [ + { + "prediction": "R", + "prediction_score": 0.7, + "prediction_source": "mock", + } + ], "model_version": expected_model_version, }, { "filepath": "e.jpg", "country": "COUNTRY_E", - "classifications_list": [{ - "classes": ["K", "L", "M"], - "scores": [0.9, 0.1, 0.0], - }], + "classifications_list": [ + { + "classes": ["K", "L", "M"], + "scores": [0.9, 0.1, 0.0], + } + ], "detections": [ { "category": "2", @@ -366,32 +387,36 @@ def test_combine(self, mock_ensemble2) -> None: "bbox": [0.1, 0.2, 0.3, 0.4], } ], - "ensemble_predictions": [{ - "prediction": "K", - "prediction_score": 0.9, - "prediction_source": "mock" - }], + "ensemble_predictions": [ + { + "prediction": "K", + "prediction_score": 0.9, + "prediction_source": "mock", + } + ], "model_version": expected_model_version, }, { "filepath": "f.jpg", - "classifications_list": [{ - "classes": ["XYZ"], - "scores": [0.8], - }], + "classifications_list": [ + { + "classes": ["XYZ"], + "scores": [0.8], + } + ], "detections": [], - "ensemble_predictions": [{ - "prediction": "XYZ", - "prediction_score": 0.4, - "prediction_source": "partial" - }], + "ensemble_predictions": [ + { + "prediction": "XYZ", + "prediction_score": 0.4, + "prediction_source": "partial", + } + ], "model_version": expected_model_version, }, ] def test_multi_combine(self, mock_ensemble2) -> None: - expected_model_version = mock_ensemble2.model_info.version - classifications_list = [ { "classes": ["R", "S", "T"], @@ -406,10 +431,12 @@ def test_multi_combine(self, mock_ensemble2) -> None: "bbox": [0.1, 0.2, 0.3, 0.4], } ] - + result = mock_ensemble2.combine( filepaths=["d.jpg"], - classifier_results={"d.jpg": {"classifications_list": classifications_list}}, + classifier_results={ + "d.jpg": {"classifications_list": classifications_list} + }, detector_results={"d.jpg": {"detections": detections}}, geolocation_results={"d.jpg": {"country": "COUNTRY_D"}}, )[0]["ensemble_predictions"] diff --git a/speciesnet/multiprocessing.py b/speciesnet/multiprocessing.py index 4607de7..a28d744 100644 --- a/speciesnet/multiprocessing.py +++ b/speciesnet/multiprocessing.py @@ -31,7 +31,7 @@ import queue import threading import traceback -from typing import Any, Callable, Literal, Optional, Union +from typing import Callable, Literal, Optional, Union from absl import logging from tqdm import tqdm @@ -40,7 +40,7 @@ from speciesnet.constants import Failure from speciesnet.detector import SpeciesNetDetector from speciesnet.ensemble import SpeciesNetEnsemble -from speciesnet.ensemble_prediction_combiner import combine_predictions_for_single_item +from speciesnet.ensemble_prediction_combiner import combine_predictions from speciesnet.geolocation import find_admin1_region from speciesnet.utils import BBox from speciesnet.utils import load_partial_predictions @@ -54,7 +54,7 @@ StrPath = Union[str, Path] DetectorInput = tuple[str, Optional[PreprocessedImage]] BBoxOutput = tuple[str, list[BBox]] -ClassifierInput = tuple[str, Optional[PreprocessedImage]] +ClassifierInput = tuple[str, list[Optional[PreprocessedImage]]] # Register SpeciesNet model components with the SyncManager to be able to safely share # them between processes. @@ -282,10 +282,10 @@ def _prepare_classifier_input( filepath, bboxes = bboxes_queue.get() img = load_rgb_image(filepath) try: - img = classifier.preprocess(img, bboxes=bboxes) - classifier_queue.put((filepath, img)) + imgs = classifier.preprocess(img, bboxes=bboxes) + classifier_queue.put((filepath, imgs)) except: - classifier_queue.put((filepath, None)) + classifier_queue.put((filepath, [])) raise @@ -564,7 +564,7 @@ def __init__( components: Literal["all", "classifier", "detector", "ensemble"] = "all", geofence: bool = True, target_species_txt: Optional[str] = None, - combine_predictions_fn: Callable = combine_predictions_for_single_item, + combine_predictions_fn: Callable = combine_predictions, multiprocessing: bool = False, force_model_download: bool = False, ) -> None: @@ -987,7 +987,7 @@ def _predict_using_thread_pools( instances_dict: dict, batch_size: int = 8, progress_bars: bool = False, - predictions_json: Optional[StrPath] = None, + predictions_json: Optional[StrPath] = None, max_classifications_per_image: Optional[int] = None, ) -> Optional[dict]: return self._predict_using_worker_pools( From cfbb45671dfe7418a798d1837eade25051aee889 Mon Sep 17 00:00:00 2001 From: Alice Mu Date: Tue, 29 Sep 2026 01:08:37 +0000 Subject: [PATCH 03/12] test: add real camera trap multi-bbox test data and end-to-end tests --- speciesnet/classifier_test.py | 47 ++++++++++++++++++++++++++- speciesnet/detector_test.py | 45 +++++++++++++++++++++++++ speciesnet/ensemble_test.py | 47 +++++++++++++++++++++++++++ speciesnet/utils_test.py | 2 ++ test_data/README.md | 2 ++ test_data/gazelles_and_zebra.jpg | Bin 0 -> 631059 bytes test_data/human_and_dog.jpg | Bin 0 -> 1020518 bytes test_data/instances.json | 8 +++++ test_data/instances_with_errors.json | 8 +++++ test_data/local_filepaths.txt | 2 ++ test_data/local_instances.json | 6 ++++ 11 files changed, 166 insertions(+), 1 deletion(-) create mode 100644 test_data/gazelles_and_zebra.jpg create mode 100644 test_data/human_and_dog.jpg diff --git a/speciesnet/classifier_test.py b/speciesnet/classifier_test.py index 4c54b1f..ada4be5 100644 --- a/speciesnet/classifier_test.py +++ b/speciesnet/classifier_test.py @@ -36,6 +36,8 @@ DOMESTIC_CATTLE = "aca65aaa-8c6d-4b69-94de-842b08b13bd6;mammalia;artiodactyla;bovidae;bos;taurus;domestic cattle" DOMESTIC_DOG = "3d80f1d6-b1df-4966-9ff4-94053c7a902a;mammalia;carnivora;canidae;canis;familiaris;domestic dog" OCELOT = "22976d14-d424-4f18-a67a-d8e1689cefcc;mammalia;carnivora;felidae;leopardus;pardalis;ocelot" +PLAINS_ZEBRA = "dd39bbd5-077c-482e-9d33-bd176116c870;mammalia;perissodactyla;equidae;equus;quagga;plains zebra" +THOMSONS_GAZELLE = "dc5dbe17-a8ca-40a6-ac6a-3b6b1d63e6d6;mammalia;artiodactyla;bovidae;eudorcas;thomsonii;thomson's gazelle" # pylint: enable=line-too-long # fmt: on @@ -97,7 +99,7 @@ def test_preprocess( # pylint: disable=too-many-positional-arguments img_green_w480_h480, # output ) -> None: - assert classifier.preprocess(None) is None + assert classifier.preprocess(None) == [] if classifier.model_info.type_ == "always_crop": @@ -313,11 +315,21 @@ def test_predict(self, classifier, img_green_w480_h480) -> None: [BBox(0.2377, 0.08398, 0.5161, 0.6497)], DOMESTIC_DOG, ), + ( + "test_data/gazelles_and_zebra.jpg", + [BBox(0.2749, 0.4687, 0.2788, 0.3854)], + THOMSONS_GAZELLE, + ), ( "test_data/human.jpg", [BBox(0.7115, 0.4976, 0.0664, 0.2424)], HUMAN, ), + ( + "test_data/human_and_dog.jpg", + [BBox(0.6964, 0.3965, 0.0771, 0.2521)], + HUMAN, + ), ( "test_data/ocelot.jpg", [BBox(0.0478, 0.5548, 0.521, 0.2743)], @@ -346,6 +358,39 @@ def test_classifications(self, predicted_vs_expected) -> None: classifications["scores"], reverse=True ) + def test_multi_bbox_classifications(self, classifier) -> None: + """Test that multiple bounding boxes produce distinct classifications + per crop. + """ + if classifier.model_info.type_ != "always_crop": + pytest.skip( + "Full-image models do not classify individual crops distinctly." + ) + + # Test human and dog + filepath = "test_data/human_and_dog.jpg" + bboxes = [ + BBox(0.6964, 0.3965, 0.0771, 0.2521), # human + BBox(0.5229, 0.5646, 0.1307, 0.0965), # domestic dog + ] + crops = classifier.preprocess(load_rgb_image(filepath), bboxes=bboxes) + pred = classifier.predict(filepath, crops) + assert len(pred["classifications_list"]) == 2 + assert pred["classifications_list"][0]["classes"][0] == HUMAN + assert pred["classifications_list"][1]["classes"][0] == DOMESTIC_DOG + + # Test gazelles and zebra + filepath = "test_data/gazelles_and_zebra.jpg" + bboxes = [ + BBox(0.2749, 0.4687, 0.2788, 0.3854), # thomson's gazelle + BBox(0.9678, 0.4538, 0.0322, 0.1029), # plains zebra + ] + crops = classifier.preprocess(load_rgb_image(filepath), bboxes=bboxes) + pred = classifier.predict(filepath, crops) + assert len(pred["classifications_list"]) == 2 + assert pred["classifications_list"][0]["classes"][0] == THOMSONS_GAZELLE + assert pred["classifications_list"][1]["classes"][0] == PLAINS_ZEBRA + def test_target_species_batched_vs_non_batched( self, model_name: str, tmp_path ) -> None: diff --git a/speciesnet/detector_test.py b/speciesnet/detector_test.py index 9d56c68..5a2f396 100644 --- a/speciesnet/detector_test.py +++ b/speciesnet/detector_test.py @@ -556,6 +556,36 @@ def test_predict(self, detector, img_green_w1280_h1280) -> None: }, ], ), + ( + "test_data/gazelles_and_zebra.jpg", + [ + { + "category": "1", + "conf": 0.013, + "bbox": [0.1631, 0.5658, 0.0337, 0.0482], + }, + { + "category": "1", + "conf": 0.014, + "bbox": [0.0342, 0.5007, 0.0137, 0.0273], + }, + { + "category": "1", + "conf": 0.885, + "bbox": [0.9678, 0.4538, 0.0322, 0.1029], + }, + { + "category": "1", + "conf": 0.93, + "bbox": [0.1265, 0.4225, 0.0962, 0.2409], + }, + { + "category": "1", + "conf": 0.956, + "bbox": [0.2749, 0.4687, 0.2788, 0.3854], + }, + ], + ), ( "test_data/human.jpg", [ @@ -576,6 +606,21 @@ def test_predict(self, detector, img_green_w1280_h1280) -> None: }, ], ), + ( + "test_data/human_and_dog.jpg", + [ + { + "category": "1", + "conf": 0.905, + "bbox": [0.5229, 0.5646, 0.1307, 0.0965], + }, + { + "category": "2", + "conf": 0.943, + "bbox": [0.6964, 0.3965, 0.0771, 0.2521], + }, + ], + ), ( "test_data/ocelot.jpg", [ diff --git a/speciesnet/ensemble_test.py b/speciesnet/ensemble_test.py index c0cf856..0908d23 100644 --- a/speciesnet/ensemble_test.py +++ b/speciesnet/ensemble_test.py @@ -62,6 +62,7 @@ PUMA_FC = "mammalia;carnivora;felidae;puma;concolor" SAND_CAT = "e588253d-d61d-4149-a96c-8c245927a80f;mammalia;carnivora;felidae;felis;margarita;sand cat" SAND_CAT_FC = "mammalia;carnivora;felidae;felis;margarita" +DOMESTIC_DOG = "3d80f1d6-b1df-4966-9ff4-94053c7a902a;mammalia;carnivora;canidae;canis;familiaris;domestic dog" # pylint: enable=line-too-long # fmt: on @@ -446,6 +447,52 @@ def test_multi_combine(self, mock_ensemble2) -> None: assert result[0]["prediction_score"] == 0.7 assert result[0]["prediction_source"] == "mock" + def test_multi_combine_multiple_crops(self, ensemble) -> None: + """Test ensembling multiple classifications and detections using real + combiner. + """ + filepath = "test_data/human_and_dog.jpg" + classifications_list = [ + { + "classes": [HUMAN, Classification.BLANK], + "scores": [0.99, 0.01], + }, + { + "classes": [DOMESTIC_DOG, Classification.BLANK], + "scores": [0.95, 0.05], + }, + ] + detections = [ + { + "category": "2", + "label": "human", + "conf": 0.94, + "bbox": [0.6964, 0.3965, 0.0771, 0.2521], + }, + { + "category": "1", + "label": "animal", + "conf": 0.90, + "bbox": [0.5229, 0.5646, 0.1307, 0.0965], + }, + ] + res = ensemble.combine( + filepaths=[filepath], + classifier_results={ + filepath: {"classifications_list": classifications_list} + }, + detector_results={ + filepath: {"filepath": filepath, "detections": detections} + }, + geolocation_results={filepath: {"country": "USA"}}, + )[0] + preds = res["ensemble_predictions"] + assert len(preds) == 2 + assert preds[0]["prediction"] == DOMESTIC_DOG + assert preds[0]["prediction_source"] == "classifier" + assert preds[1]["prediction"] == HUMAN + assert preds[1]["prediction_source"] == "detector" + def test_complete_taxonomy(self, ensemble) -> None: missing_ancestors = set() diff --git a/speciesnet/utils_test.py b/speciesnet/utils_test.py index 7e3c7b1..9601404 100644 --- a/speciesnet/utils_test.py +++ b/speciesnet/utils_test.py @@ -72,7 +72,9 @@ def filepaths_as_strings(self) -> list[str]: "test_data/blank3.jpg", "test_data/domestic_cattle.jpg", "test_data/domestic_dog.jpg", + "test_data/gazelles_and_zebra.jpg", "test_data/human.jpg", + "test_data/human_and_dog.jpg", "test_data/ocelot.jpg", "test_data/vehicle.jpg", ] diff --git a/test_data/README.md b/test_data/README.md index 473a896..451600b 100644 --- a/test_data/README.md +++ b/test_data/README.md @@ -13,6 +13,8 @@ | [american_black_bear.jpg](american_black_bear.jpg) | Original | [North American Camera Trap Images](https://lila.science/datasets/nacti) | [CDLA-Permissive-1.0](https://cdla.dev/permissive-1-0/) | | [domestic_cattle.jpg](domestic_cattle.jpg) | Original | [North American Camera Trap Images](https://lila.science/datasets/nacti) | [CDLA-Permissive-1.0](https://cdla.dev/permissive-1-0/) | | [domestic_dog.jpg](domestic_dog.jpg) | Original | [North American Camera Trap Images](https://lila.science/datasets/nacti) | [CDLA-Permissive-1.0](https://cdla.dev/permissive-1-0/) | +| [gazelles_and_zebra.jpg](gazelles_and_zebra.jpg) | Original | [Snapshot Serengeti](https://lila.science/datasets/snapshot-serengeti) | [CDLA-Permissive-1.0](https://cdla.dev/permissive-1-0/) | | [human.jpg](human.jpg) | Original | [Dan Morris](http://dmorris.net/) | [CDLA-Permissive-1.0](https://cdla.dev/permissive-1-0/) | +| [human_and_dog.jpg](human_and_dog.jpg) | Modified | [Seattle(ish) Camera Traps](https://lila.science/datasets/seattleish-camera-traps) | [CDLA-Permissive-1.0](https://cdla.dev/permissive-1-0/) | | [ocelot.jpg](ocelot.jpg) | Original | [WCS Camera Traps](https://lila.science/datasets/wcscameratraps) | [CDLA-Permissive-1.0](https://cdla.dev/permissive-1-0/) | | [vehicle.jpg](vehicle.jpg) | Original | [North American Camera 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HcmV?d00001

diff --git a/test_data/instances.json b/test_data/instances.json
index aa8855d..56c4a54 100644
--- a/test_data/instances.json
+++ b/test_data/instances.json
@@ -48,10 +48,18 @@
             "latitude": 41.46,
             "longitude": -73.84
         },
+        {
+            "filepath": "test_data/gazelles_and_zebra.jpg",
+            "country": "TZA"
+        },
         {
             "filepath": "test_data/human.jpg",
             "country": "USA"
         },
+        {
+            "filepath": "test_data/human_and_dog.jpg",
+            "country": "USA"
+        },
         {
             "filepath": "test_data/ocelot.jpg"
         },
diff --git a/test_data/instances_with_errors.json b/test_data/instances_with_errors.json
index 330f06d..0636807 100644
--- a/test_data/instances_with_errors.json
+++ b/test_data/instances_with_errors.json
@@ -51,10 +51,18 @@
             "latitude": 41.46,
             "longitude": -73.84
         },
+        {
+            "filepath": "test_data/gazelles_and_zebra.jpg",
+            "country": "TZA"
+        },
         {
             "filepath": "test_data/human.jpg",
             "country": "USA"
         },
+        {
+            "filepath": "test_data/human_and_dog.jpg",
+            "country": "USA"
+        },
         {
             "filepath": "test_data/ocelot.jpg"
         },
diff --git a/test_data/local_filepaths.txt b/test_data/local_filepaths.txt
index 0d48455..196f8ed 100644
--- a/test_data/local_filepaths.txt
+++ b/test_data/local_filepaths.txt
@@ -9,6 +9,8 @@ test_data/blank2.jpg
 test_data/blank3.jpg
 test_data/domestic_cattle.jpg
 test_data/domestic_dog.jpg
+test_data/gazelles_and_zebra.jpg
 test_data/human.jpg
+test_data/human_and_dog.jpg
 test_data/ocelot.jpg
 test_data/vehicle.jpg
diff --git a/test_data/local_instances.json b/test_data/local_instances.json
index 354ba88..b0bde6f 100644
--- a/test_data/local_instances.json
+++ b/test_data/local_instances.json
@@ -33,9 +33,15 @@
         {
             "filepath": "test_data/domestic_dog.jpg"
         },
+        {
+            "filepath": "test_data/gazelles_and_zebra.jpg"
+        },
         {
             "filepath": "test_data/human.jpg"
         },
+        {
+            "filepath": "test_data/human_and_dog.jpg"
+        },
         {
             "filepath": "test_data/ocelot.jpg"
         },

From a8a3412a1bb7320e693146a75adc2d62692d9151 Mon Sep 17 00:00:00 2001
From: Alice Mu 
Date: Wed, 30 Sep 2026 18:33:35 +0000
Subject: [PATCH 04/12] refactor: rename ensemble_predictions to
 processed_classifications

---
 speciesnet/ensemble.py          |  2 +-
 speciesnet/ensemble_test.py     | 12 ++++++------
 speciesnet/scripts/run_model.py |  4 ++--
 3 files changed, 9 insertions(+), 9 deletions(-)

diff --git a/speciesnet/ensemble.py b/speciesnet/ensemble.py
index 601f637..1f1687d 100644
--- a/speciesnet/ensemble.py
+++ b/speciesnet/ensemble.py
@@ -203,7 +203,7 @@ def combine(  # pylint: disable=too-many-positional-arguments
 
             # Most importantly, ensemble everything into predictions.
             if classifications_list is not None and detections is not None:
-                result["ensemble_predictions"] = self.prediction_combiner(
+                result["processed_classifications"] = self.prediction_combiner(
                     classifications_list=classifications_list,
                     detections=detections,
                     country=geolocation.get("country"),
diff --git a/speciesnet/ensemble_test.py b/speciesnet/ensemble_test.py
index 0908d23..a989a29 100644
--- a/speciesnet/ensemble_test.py
+++ b/speciesnet/ensemble_test.py
@@ -295,7 +295,7 @@ def test_combine(self, mock_ensemble2) -> None:
                     }
                 ],
                 "detections": [],
-                "ensemble_predictions": [
+                "processed_classifications": [
                     {
                         "prediction": "XYZ",
                         "prediction_score": 0.4,
@@ -362,7 +362,7 @@ def test_combine(self, mock_ensemble2) -> None:
                         "bbox": [0.1, 0.2, 0.3, 0.4],
                     }
                 ],
-                "ensemble_predictions": [
+                "processed_classifications": [
                     {
                         "prediction": "R",
                         "prediction_score": 0.7,
@@ -388,7 +388,7 @@ def test_combine(self, mock_ensemble2) -> None:
                         "bbox": [0.1, 0.2, 0.3, 0.4],
                     }
                 ],
-                "ensemble_predictions": [
+                "processed_classifications": [
                     {
                         "prediction": "K",
                         "prediction_score": 0.9,
@@ -406,7 +406,7 @@ def test_combine(self, mock_ensemble2) -> None:
                     }
                 ],
                 "detections": [],
-                "ensemble_predictions": [
+                "processed_classifications": [
                     {
                         "prediction": "XYZ",
                         "prediction_score": 0.4,
@@ -440,7 +440,7 @@ def test_multi_combine(self, mock_ensemble2) -> None:
             },
             detector_results={"d.jpg": {"detections": detections}},
             geolocation_results={"d.jpg": {"country": "COUNTRY_D"}},
-        )[0]["ensemble_predictions"]
+        )[0]["processed_classifications"]
 
         assert len(result) == 1
         assert result[0]["prediction"] == "R"
@@ -486,7 +486,7 @@ def test_multi_combine_multiple_crops(self, ensemble) -> None:
             },
             geolocation_results={filepath: {"country": "USA"}},
         )[0]
-        preds = res["ensemble_predictions"]
+        preds = res["processed_classifications"]
         assert len(preds) == 2
         assert preds[0]["prediction"] == DOMESTIC_DOG
         assert preds[0]["prediction_source"] == "classifier"
diff --git a/speciesnet/scripts/run_model.py b/speciesnet/scripts/run_model.py
index 0078cb8..1b082d9 100644
--- a/speciesnet/scripts/run_model.py
+++ b/speciesnet/scripts/run_model.py
@@ -185,11 +185,11 @@ def guess_predictions_source(
     found_ensemble_results = False
 
     for prediction in predictions.values():
-        if "classifications" in prediction:
+        if "classifications" in prediction or "classifications_list" in prediction:
             found_classifications = True
         if "detections" in prediction:
             found_detections = True
-        if "prediction" in prediction:
+        if "prediction" in prediction or "processed_classifications" in prediction:
             found_ensemble_results = True
         if found_classifications and found_detections and not found_ensemble_results:
             return "invalid"

From fdb0df41f9c87560e51b27c870fc584f22a68644 Mon Sep 17 00:00:00 2001
From: Alice Mu 
Date: Thu, 1 Oct 2026 04:15:05 +0000
Subject: [PATCH 05/12] Deduplicate full_image classifier inference per image
 in batch_predict

---
 speciesnet/classifier.py | 25 ++++++++++++++++++++-----
 1 file changed, 20 insertions(+), 5 deletions(-)

diff --git a/speciesnet/classifier.py b/speciesnet/classifier.py
index aff054a..5134f27 100644
--- a/speciesnet/classifier.py
+++ b/speciesnet/classifier.py
@@ -272,6 +272,7 @@ def batch_predict(
         predictions = {}
 
         inference_filepaths = []
+        inference_num_crops = []
         batch_arr = []
 
         for filepath, imgs in zip(filepaths, imgs_list):
@@ -293,9 +294,15 @@ def batch_predict(
 
             predictions[filepath] = {"filepath": filepath, "classifications_list": []}
 
-            for img in valid_imgs:
+            if self.model_info.type_ == "full_image":
                 inference_filepaths.append(filepath)
-                batch_arr.append(img.arr / 255.0)
+                inference_num_crops.append(len(valid_imgs))
+                batch_arr.append(valid_imgs[0].arr / 255.0)
+            else:
+                for img in valid_imgs:
+                    inference_filepaths.append(filepath)
+                    inference_num_crops.append(1)
+                    batch_arr.append(img.arr / 255.0)
 
         if not batch_arr:
             return [predictions[fp] for fp in filepaths if fp in predictions]
@@ -310,8 +317,13 @@ def batch_predict(
         scores = torch.softmax(logits, dim=-1)
         scores, indices = torch.topk(scores, k=5, dim=-1)
 
-        for file_idx, (filepath, scores_arr, indices_arr) in enumerate(
-            zip(inference_filepaths, scores.numpy(), indices.numpy())
+        for file_idx, (filepath, num_crops, scores_arr, indices_arr) in enumerate(
+            zip(
+                inference_filepaths,
+                inference_num_crops,
+                scores.numpy(),
+                indices.numpy(),
+            )
         ):
 
             classification = {
@@ -329,7 +341,10 @@ def batch_predict(
                     }
                 )
 
-            predictions[filepath]["classifications_list"].append(classification)
+            for _ in range(num_crops):
+                predictions[filepath]["classifications_list"].append(
+                    {k: list(v) for k, v in classification.items()}
+                )
 
         return [
             predictions[filepath] for filepath in filepaths if filepath in predictions

From 14f610080f34096d48476100a313225e1a30face Mon Sep 17 00:00:00 2001
From: mualicemu 
Date: Tue, 13 Oct 2026 18:59:43 -0400
Subject: [PATCH 06/12] Chunk classifier batch_predict crops to prevent MPS
 memory exhaustion

---
 speciesnet/classifier.py | 22 +++++++++++++++++-----
 1 file changed, 17 insertions(+), 5 deletions(-)

diff --git a/speciesnet/classifier.py b/speciesnet/classifier.py
index 5134f27..0621abb 100644
--- a/speciesnet/classifier.py
+++ b/speciesnet/classifier.py
@@ -309,11 +309,23 @@ def batch_predict(
 
         batch_arr = np.stack(batch_arr, axis=0, dtype=np.float32)
 
-        # On MPS devices, make sure all MPS operations happen serially.  This
-        # lock is a no-op on non-MPS devices.
-        with mps_inference_lock(self.device):
-            batch_tensor = torch.from_numpy(batch_arr).to(self.device)
-            logits = self.model(batch_tensor).cpu()
+        # Process crops in chunks of at most `max_crop_batch_size` so that a batch of
+        # images with many bounding boxes does not cause excessive memory allocation
+        # (especially on MPS unified memory) or hold the MPS lock for too long.
+        max_crop_batch_size = 8
+        logits_list = []
+        for idx in range(0, len(batch_arr), max_crop_batch_size):
+            chunk_arr = batch_arr[idx : idx + max_crop_batch_size]
+            # On MPS devices, make sure all MPS operations happen serially.  This
+            # lock is a no-op on non-MPS devices.
+            with mps_inference_lock(self.device):
+                chunk_tensor = torch.from_numpy(chunk_arr).to(self.device)
+                logits_list.append(self.model(chunk_tensor).cpu())
+                del chunk_tensor
+        if self.device == "mps":
+            with mps_inference_lock(self.device):
+                torch.mps.empty_cache()
+        logits = torch.cat(logits_list, dim=0)
         scores = torch.softmax(logits, dim=-1)
         scores, indices = torch.topk(scores, k=5, dim=-1)
 

From e6bd415e71a90465159069eb85ced8eb63545814 Mon Sep 17 00:00:00 2001
From: mualicemu 
Date: Wed, 14 Oct 2026 14:58:29 -0400
Subject: [PATCH 07/12] Update full ensemble output format description in
 README.md

---
 README.md | 29 +++++++++++++++++++----------
 1 file changed, 19 insertions(+), 10 deletions(-)

diff --git a/README.md b/README.md
index 77982f4..1b99c40 100644
--- a/README.md
+++ b/README.md
@@ -181,7 +181,7 @@ Each element always contains a field called "filepath"; the exact content of tho
 
 ### Full ensemble output format
 
-In the full ensemble output, the "classifications" field contains raw classifier output, before geofencing is applied.  So even if you specify a country code, you may see taxa in the "classifications" field that are not found in the country you specified.  The "prediction" field is the result of integrating the classification, detection, and geofencing information; if you specify a country code, the "prediction" field should only contain taxa that are found in the country you specified.
+In the full ensemble output, the "classifications_list" field contains raw classifier output, before geofencing is applied.  So even if you specify a country code, you may see taxa in the "classifications_list" field that are not found in the country you specified.  The "processed_classifications" field is the result of integrating the classification, detection, and geofencing information; if you specify a country code, the "processed_classifications" field should only contain taxa that are found in the country you specified.
 
 ```text
 {
@@ -193,12 +193,15 @@ In the full ensemble output, the "classifications" field contains raw classifier
             "admin1_region": str (optional)  => First-level administrative division (in ISO 3166-2 format) within the country above. If not provided in the request, it can be computed from (latitude, longitude) when those coordinates are specified. Included in the response only for some countries that are used in geofencing (e.g. "USA").
             "latitude": float (optional)  => Latitude where the image was taken, included only if (latitude, longitude) were present in the request.
             "longitude": float (optional)  => Longitude where the image was taken, included only if (latitude, longitude) were present in the request.
-            "classifications": {  => dict (optional)  => Top-5 classifications. Included only if "CLASSIFIER" is not part of the "failures" field.
-                "classes": list[str]  => List of top-5 classes predicted by the classifier, matching the decreasing order of their scores below.
-                "scores": list[float]  => List of scores corresponding to top-5 classes predicted by the classifier, in decreasing order.
-                "target_classes": list[str] (optional)  => List of target classes, only present if target classes are passed as arguments.
-                "target_logits": list[float] (optional)  => Raw confidence scores (logits) of the target classes, only present if target classes are passed as arguments.
-            },
+            "classifications_list": [  => list (optional)  => List of top-5 classifications for each detection crop (or full image if there are no detections). Included only if "CLASSIFIER" is not part of the "failures" field.
+                {
+                    "classes": list[str]  => List of top-5 classes predicted by the classifier, matching the decreasing order of their scores below.
+                    "scores": list[float]  => List of scores corresponding to top-5 classes predicted by the classifier, in decreasing order.
+                    "target_classes": list[str] (optional)  => List of target classes, only present if target classes are passed as arguments.
+                    "target_logits": list[float] (optional)  => Raw confidence scores (logits) of the target classes, only present if target classes are passed as arguments.
+                },
+                ...  => One classification entry per detection (or one entry for the full image if there are no detections).
+            ],
             "detections": [  => list (optional)  => List of detections with confidence scores > 0.01, in decreasing order of their scores. Included only if "DETECTOR" is not part of the "failures" field.
                 {
                     "category": str  => Detection class "1" (= animal), "2" (= human) or "3" (= vehicle) from MegaDetector's raw output.
@@ -208,9 +211,15 @@ In the full ensemble output, the "classifications" field contains raw classifier
                 },
                 ...  => A prediction can contain zero or multiple detections.
             ],
-            "prediction": str (optional)  => Final prediction of the SpeciesNet ensemble. Included only if "CLASSIFIER" and "DETECTOR" are not part of the "failures" field.
-            "prediction_score": float (optional)  => Final prediction score of the SpeciesNet ensemble. Included only if the "prediction" field above is included.
-            "prediction_source": str (optional)  => Internal component that produced the final prediction. Used to collect information about which parts of the SpeciesNet ensemble fired. Included only if the "prediction" field above is included.
+            "processed_classifications": [  => list (optional)  => List of final predictions of the SpeciesNet ensemble, in decreasing order of their scores. Included only if "CLASSIFIER" and "DETECTOR" are not part of the "failures" field.
+                {
+                    "prediction": str  => Final prediction of the SpeciesNet ensemble for the current bounding box (or full image).
+                    "prediction_score": float  => Final prediction score of the SpeciesNet ensemble.
+                    "prediction_source": str  => Internal component that produced the final prediction. Used to collect information about which parts of the SpeciesNet ensemble fired.
+                    "bbox": list[float] | None  => Bounding box coordinates, in (xmin, ymin, width, height) format, associated with this prediction, or null if no detections were present.
+                },
+                ...  => One processed classification per detection (or one entry with bbox: null if there are no detections).
+            ],
             "model_version": str  => A string representing the version of the model that produced the current prediction.
         },
         ...  => A response will contain one prediction for each instance in the request.

From 0c69f44e1f5bcbcffb261ced83a1a8e39b016965 Mon Sep 17 00:00:00 2001
From: Dan Morris 
Date: Thu, 15 Oct 2026 17:43:11 -0700
Subject: [PATCH 08/12] Improvements to multi-detection classification, changes
 to output file format

---
 README.md                                     |  86 ++--
 advanced_topics.md                            |  50 +-
 speciesnet/classifier.py                      | 143 +++---
 speciesnet/classifier_test.py                 |  57 ++-
 speciesnet/constants.py                       |  27 +-
 speciesnet/ensemble.py                        | 183 ++++++--
 speciesnet/ensemble_prediction_combiner.py    |  70 ++-
 .../ensemble_prediction_combiner_test.py      |  96 ++--
 speciesnet/ensemble_test.py                   | 426 +++++++++++-------
 speciesnet/multiprocessing.py                 | 345 +++++++++++---
 speciesnet/multiprocessing_test.py            | 184 ++++++++
 speciesnet/scripts/run_model.py               | 129 ++++--
 speciesnet/scripts/run_server.py              |   2 +-
 speciesnet/utils.py                           | 160 ++++++-
 speciesnet/utils_test.py                      |  98 ++--
 15 files changed, 1425 insertions(+), 631 deletions(-)

diff --git a/README.md b/README.md
index 1b99c40..c6068a8 100644
--- a/README.md
+++ b/README.md
@@ -26,15 +26,13 @@ An ensemble of AI models for classifying wildlife in camera trap images.
 
 Effective wildlife monitoring relies heavily on motion-triggered wildlife cameras, or “camera traps”, which generate vast quantities of image data. Manual processing of these images is a significant bottleneck. AI can accelerate that processing, helping conservation practitioners spend more time on conservation, and less time reviewing images.
 
-This repository hosts code for running an ensemble of two AI models: (1) an object detector that finds objects of interest in wildlife camera images, and (2) an image classifier that classifies those objects to the species level. This ensemble is used for species recognition in the [Wildlife Insights](https://www.wildlifeinsights.org/) platform.
+This repository hosts code for running a cascade of two AI models: (1) an object detector that finds objects of interest in wildlife camera images, and (2) an image classifier that classifies those objects to the species level. This approach is used for species recognition in the [Wildlife Insights](https://www.wildlifeinsights.org/) platform.
 
 The object detector used in this ensemble is [MegaDetector](https://github.com/agentmorris/MegaDetector), which finds animals, humans, and vehicles in camera trap images, but does not classify animals to species level.
 
 The species classifier ([SpeciesNet](https://www.kaggle.com/models/google/speciesnet)) was trained at Google using a large dataset of camera trap images and an [EfficientNet V2 M](https://arxiv.org/abs/2104.00298) architecture. It is designed to classify images into one of more than 2000 labels, covering diverse animal species, higher-level taxa (like "mammalia" or "felidae"), and non-animal classes ("blank", "vehicle"). SpeciesNet has been trained on a geographically diverse dataset of over 65M images, including curated images from the Wildlife Insights user community, as well as images from publicly-available repositories.
 
-The SpeciesNet ensemble combines these two models using a set of heuristics and, optionally, geographic information to assign each image to a single category.  See the "[ensemble decision-making](#ensemble-decision-making)" section for more information about how the ensemble combines information for each image to make a single prediction.
-
-The full details of the models and the ensemble process are discussed in this research paper:
+The full details of the species classifier are discussed in this paper:
 
 Gadot T, Istrate Ș, Kim H, Morris D, Beery S, Birch T, Ahumada J. [To crop or not to crop: Comparing whole-image and cropped classification on a large dataset of camera trap images](https://doi.org/10.1049/cvi2.12318). IET Computer Vision. 2024 Dec;18(8):1193-208.
 
@@ -94,9 +92,9 @@ If your images are from the USA, you can also specify a state name using the two
 
 ### Running SpeciesNet on multiple detections per image (or on videos)
 
-The `run_model` script described above uses [MegaDetector](https://github.com/agentmorris/MegaDetector) to find animals in each image, then runs the SpeciesNet classifier on just the highest-confidence detection in each image.  The goal of this script is to propose the single species that is most likely to be present in each image, and in most cases, processing every object detected in the image through the classifier would be slower, without changing the proposed species.
+The `run_model` script described above uses [MegaDetector](https://github.com/agentmorris/MegaDetector) to find animals, humans, and vehicles in each image, then runs the SpeciesNet classifier on every detection with a confidence above a threshold (default 0.1), so each detection gets its own classification.
 
-This is a problem, however, when you frequently have multi-species images, or images with both humans and domestic animals.  If this is a concern for your scenario, instead of using `run_model`, we recommend using [run_md_and_speciesnet](https://megadetector.readthedocs.io/en/latest/detection.html#run_md_and_speciesnet---CLI-interface), from the [MegaDetector Python package](https://megadetector.readthedocs.io/).  This looks like the following:
+If you want to run SpeciesNet on videos, or if you want to use other MegaDetector versions, you can also use [run_md_and_speciesnet](https://megadetector.readthedocs.io/en/latest/detection.html#run_md_and_speciesnet---CLI-interface), from the [MegaDetector Python package](https://megadetector.readthedocs.io/).  This looks like the following:
 
 ```bash
 pip install megadetector
@@ -175,16 +173,21 @@ If you use this model, please cite:
 
 ## Output format from run_model
 
-`run_model.py` produces output in .json format, containing an array called "predictions", with one element per image.  We provide a script to convert this format to the format used by [MegaDetector](https://github.com/agentmorris/MegaDetector), which can be imported into [Timelapse](https://timelapse.ucalgary.ca/), see [speciesnet_to_md.py](speciesnet/scripts/speciesnet_to_md.py).
+`run_model.py` produces output in .json format, containing an "info" section and an array called "predictions", with one element per image.  We provide a script to convert this format to the format used by [MegaDetector](https://github.com/agentmorris/MegaDetector), which can be imported into [Timelapse](https://timelapse.ucalgary.ca/), see [speciesnet_to_md.py](speciesnet/scripts/speciesnet_to_md.py).
+
 
-Each element always contains a field called "filepath"; the exact content of those elements will vary depending on which elements of the ensemble you ran.  If you didn't go out of your way to do something unusual, you ran the entire ensemble (i.e., both the detector and the classifier), so the "full ensemble" output format applies.  Output formats for other scenarios are described in the [advanced topics documentation](advanced_topics.md).
 
-### Full ensemble output format
+Each element in the "predictions" array contains the results for one image. Each element contains a list of detections, sorted in descending order of confidence.  Every detection with a confidence of at least `--detection_threshold_for_classification` (default 0.1) is classified, up to a maximum of `--max_classifications_per_image` detections per image (default: no maximum); when that maximum applies, the highest-confidence detections are the ones that get classified.  Detections below the threshold (or beyond the maximum) are still included in the output, just without classification fields.  If an image has no detections at or above the threshold, none of its detections are classified.
 
-In the full ensemble output, the "classifications_list" field contains raw classifier output, before geofencing is applied.  So even if you specify a country code, you may see taxa in the "classifications_list" field that are not found in the country you specified.  The "processed_classifications" field is the result of integrating the classification, detection, and geofencing information; if you specify a country code, the "processed_classifications" field should only contain taxa that are found in the country you specified.
+Each classified detection has a "raw_classifications" field and a "processed_classification" field.  The "raw_classifications" field contains the top k classes from the classifier, where k is controlled by `--max_classifications_per_detection` (default 2), before geofencing and taxonomic rollup are applied.  So even if you specify a country code, you may see taxa in the "raw_classifications" field that are not found in the country you specified.  The "processed_classification" field contains the single class that results from applying geofencing and taxonomic rollup (see [ensemble decision-making](#ensemble-decision-making)), along with some heuristics about the detection category and confidence. If you specify a country code, the "processed_classification" field should only contain taxa that are found in the country you specified.
+
+When running the whole-image classifier, the first element in each image's "detections" list is a single detection with category "0" ("whole_image_classification"), with a confidence of 1.0 and a bounding box covering the whole image.  This is the only detection that gets classified; it is followed by the (unclassified) detections from MegaDetector.  The highest-confidence MegaDetector detection is still used in the heuristics that determine the the processed classification for the whole image.
 
 ```text
 {
+    "info": {
+        "format_version": str  => File format version, as a string of the form "20261015".
+    },
     "predictions": [
         {
             "filepath": str  => Image filepath.
@@ -193,33 +196,26 @@ In the full ensemble output, the "classifications_list" field contains raw class
             "admin1_region": str (optional)  => First-level administrative division (in ISO 3166-2 format) within the country above. If not provided in the request, it can be computed from (latitude, longitude) when those coordinates are specified. Included in the response only for some countries that are used in geofencing (e.g. "USA").
             "latitude": float (optional)  => Latitude where the image was taken, included only if (latitude, longitude) were present in the request.
             "longitude": float (optional)  => Longitude where the image was taken, included only if (latitude, longitude) were present in the request.
-            "classifications_list": [  => list (optional)  => List of top-5 classifications for each detection crop (or full image if there are no detections). Included only if "CLASSIFIER" is not part of the "failures" field.
+            "detections": [  => list (optional)  => List of detections with confidence scores > 0.01, in decreasing order of their confidence scores (regardless of category).
                 {
-                    "classes": list[str]  => List of top-5 classes predicted by the classifier, matching the decreasing order of their scores below.
-                    "scores": list[float]  => List of scores corresponding to top-5 classes predicted by the classifier, in decreasing order.
-                    "target_classes": list[str] (optional)  => List of target classes, only present if target classes are passed as arguments.
-                    "target_logits": list[float] (optional)  => Raw confidence scores (logits) of the target classes, only present if target classes are passed as arguments.
-                },
-                ...  => One classification entry per detection (or one entry for the full image if there are no detections).
-            ],
-            "detections": [  => list (optional)  => List of detections with confidence scores > 0.01, in decreasing order of their scores. Included only if "DETECTOR" is not part of the "failures" field.
-                {
-                    "category": str  => Detection class "1" (= animal), "2" (= human) or "3" (= vehicle) from MegaDetector's raw output.
-                    "label": str  => Detection class "animal", "human" or "vehicle", matching the "category" field above. Added for readability purposes.
-                    "conf": float  => Confidence score of the current detection.
+                    "category": str  => Detection class "0" (whole_image_classification), "1" (animal), "2" (human) or "3" (vehicle). Category "0" is used only by the whole-image classifier (see above); the other categories come from MegaDetector's raw output.
+                    "label": str  => Detection class "whole_image_classification", "animal", "human" or "vehicle", matching the "category" field above. Redundant with "category"; added for readability.
+                    "conf": float  => Confidence score of the current detection (always 1.0 for category "0").
                     "bbox": list[float]  => Bounding box coordinates, in (xmin, ymin, width, height) format, of the current detection. Coordinates are normalized to the [0.0, 1.0] range, relative to the image dimensions.
+                    "raw_classifications": {  => dict (optional)  => Raw classifier output for the current detection. Included only for classified detections (see above), and only if "CLASSIFIER" is not part of the "failures" field.
+                        "classes": list[str]  => List of top-k classes predicted by the classifier, matching the decreasing order of their scores below.
+                        "scores": list[float]  => List of scores corresponding to the top-k classes predicted by the classifier, in decreasing order.
+                        "target_classes": list[str] (optional)  => List of target classes, only present if target classes are passed as arguments.
+                        "target_logits": list[float] (optional)  => Raw confidence scores (logits) of the target classes, only present if target classes are passed as arguments.
+                    },
+                    "processed_classification": {  => dict (optional)  => Final classification of the SpeciesNet ensemble for the current detection.
+                        "class": str  => Final class predicted by the SpeciesNet ensemble for the current detection.
+                        "score": float  => Score of the final class.
+                        "source": str  => Internal component that produced the final class.
+                    }
                 },
                 ...  => A prediction can contain zero or multiple detections.
             ],
-            "processed_classifications": [  => list (optional)  => List of final predictions of the SpeciesNet ensemble, in decreasing order of their scores. Included only if "CLASSIFIER" and "DETECTOR" are not part of the "failures" field.
-                {
-                    "prediction": str  => Final prediction of the SpeciesNet ensemble for the current bounding box (or full image).
-                    "prediction_score": float  => Final prediction score of the SpeciesNet ensemble.
-                    "prediction_source": str  => Internal component that produced the final prediction. Used to collect information about which parts of the SpeciesNet ensemble fired.
-                    "bbox": list[float] | None  => Bounding box coordinates, in (xmin, ymin, width, height) format, associated with this prediction, or null if no detections were present.
-                },
-                ...  => One processed classification per detection (or one entry with bbox: null if there are no detections).
-            ],
             "model_version": str  => A string representing the version of the model that produced the current prediction.
         },
         ...  => A response will contain one prediction for each instance in the request.
@@ -227,6 +223,15 @@ In the full ensemble output, the "classifications_list" field contains raw class
 }
 ```
 
+### Output from individual components
+
+When you run only some of the components of the ensemble, the output file uses the format described above, but some fields are left out:
+
+- Detector-only output (`--detector_only`) contains only the "filepath", "failures", and "detections" fields for each image, and the detections have no "raw_classifications" or "processed_classification" fields.
+- Classifier-only output (`--classifier_only`) contains only the "filepath", "failures", and "detections" fields for each image, and classified detections have a "raw_classifications" field, but no "processed_classification" field.  When running the crop classifier (v4.0.3a), the detections are copied from the file passed via `--detections_json`.  When running the whole-image classifier (v4.0.3b), the only detection is the whole-image pseudo-detection (category "0"), since the detector was not run.  So that the ensemble step can be run later, the "raw_classifications" field in classifier-only output contains at least the top 5 classes, even if `--max_classifications_per_detection` is smaller than 5.
+- Ensemble-only output (`--ensemble_only`) is identical to the output you would get from running the full ensemble.
+
+
 ## Visualizing SpeciesNet output
 
 As per above, many users will work with SpeciesNet results in open-source tools like [Timelapse](https://timelapse.ucalgary.ca/), which support the file format used by [MegaDetector](https://github.com/agentmorris/MegaDetector) (the format is described [here](https://lila.science/megadetector-output-format)).  If you used `run_md_and_speciesnet` to run SpeciesNet, you already have output in this format.  If you used `run_model`, we provide a [speciesnet_to_md](speciesnet/scripts/speciesnet_to_md.py) script to convert to this format.  Tools like Timelapse are a good way to visualize and interact with your SpeciesNet results.
@@ -279,7 +284,7 @@ Both of these modules can also be called from Python code instead of from the co
 
 ## Ensemble decision-making
 
-As discussed above, `run_model` uses multiple steps to predict a single category for each image, combining the strengths of the detector and the classifier.  The ensembling strategy (i.e., the strategy used to combine the information from the detector and classifier) was primarily optimized for minimizing the human effort required to review collections of images.
+As discussed above, `run_model` uses multiple steps to predict a single category for each classified detection, combining the strengths of the detector and the classifier.
 
 The guiding principles of the ensembling strategy are:
 
@@ -289,34 +294,35 @@ The guiding principles of the ensembling strategy are:
 
 Here is a breakdown of the steps:
 
-1. **Input processing:** Raw images are preprocessed and passed to both the object detector (MegaDetector) and the image classifier. The type of preprocessing will depend on the selected model. For "always crop" models, images are first processed by the object detector and then cropped based on the detection bounding box before being fed to the classifier. For "full image" models, images are preprocessed independently for both models.
+1. **Input processing:** Raw images are preprocessed and passed to both the object detector (MegaDetector) and the image classifier. The type of preprocessing will depend on the selected model. For "always crop" models, images are first processed by the object detector, and then each detection that will be classified is cropped from the image and fed to the classifier. For "full image" models, images are preprocessed independently for both models, and the highest-confidence detection is used in the steps below.
 
 2. **Object detection:** The detector identifies potential objects (animals, humans, or vehicles) in the image, providing their bounding box coordinates and confidence scores.
 
 3. **Species classification:** The species classifier analyzes the (potentially cropped) image to identify the most likely species present. It provides a list of top-5 species classifications, each with a confidence score. The species classifier is a fully supervised model that classifies images into a fixed set of animal species, higher taxa, and non-animal labels.
 
-4. **Detection-based human/vehicle decisions:** If the detector is highly confident about the presence of a human or vehicle, that label will be returned as the final prediction regardless of what the classifier predicts. If the detection is less confident and the classifier also returns human or vehicle as a top-5 prediction, with a reasonable score, that top prediction will be returned. This step prevents high-confidence detector predictions from being overridden by lower-confidence classifier predictions.
+4. **Detection-based human/vehicle decisions:** If the detector is highly confident that the detection is a human or vehicle, that label will be returned as the final prediction regardless of what the classifier predicts. If the detection is less confident and the classifier also returns human or vehicle as a top-5 prediction, with a reasonable score, that top prediction will be returned. This step prevents high-confidence detector predictions from being overridden by lower-confidence classifier predictions.
 
-5. **Blank decisions:** If the classifier predicts "blank" with a high confidence score, and the detector has very low confidence about the presence of an animal (or is absent), that "blank" label is returned as a final prediction. Similarly, if a classification is "blank" with extra-high confidence (above 0.99), that label is returned as a final prediction regardless of the detector's output. This enables the model to filter out images with high confidence in being blank.
+5. **Blank decisions:** If the classifier predicts "blank" with a high confidence score, and the detector has very low confidence that the detection is an animal (or, for the whole-image classifier, there are no detections), that "blank" label is returned as a final prediction. Similarly, if a classification is "blank" with extra-high confidence (above 0.99), that label is returned as a final prediction regardless of the detector's output. This enables the model to filter out images with high confidence in being blank.
 
 6. **Geofencing:** If the most likely species is an animal and a location (country and optional admin1 region) is provided for the image, a geofencing rule is applied. If that species is explicitly disallowed for that region based on the available geofencing rules, the prediction will be rolled up (as explained below) to a higher taxa level on that allow list.
 
 7. **Label rollup:** If all of the previous steps do not yield a final prediction, a "rollup" is applied when there is a good classification score for an animal. "Rollup" is the process of propagating the classification predictions to the first matching ancestor in the taxonomy, provided there is a good score at that level. This means the model may assign classifications at the genus, family, order, class, or kingdom level, if those scores are higher than the score at the species level. This is a common strategy to handle long-tail distributions, common in wildlife datasets.
 
-8. **Detection-based animal decisions:**  If the detector has a reasonable confidence `animal` prediction, `animal` will be returned along with the detector confidence.
+8. **Detection-based animal decisions:**  If the detector has a reasonable confidence that the detection is an `animal`, `animal` will be returned along with the detector confidence.
 
 9. **Unknown:** If no other rule applies, the `unknown` class is returned as the final prediction, to avoid making low-confidence predictions.
 
-10. **Prediction source:** At each step of the prediction workflow, a `prediction_source` is stored. This will be included in the final results to help diagnose which parts of the overall SpeciesNet ensemble were actually used.
+10. **Prediction source:** At each step of the prediction workflow, a prediction source is stored. This will be included in the "source" field of each detection's "processed_classification" to help diagnose which parts of the overall SpeciesNet ensemble were actually used.
 
-The "geofencing" and "label rollup" steps are also used when running `run_md_and_speciesnet`; the other steps don't apply in this scenario, since the goal of `run_md_and_speciesnet` is to classify each detection, rather than to classify the whole image.
+The "geofencing" and "label rollup" steps are also used when running `run_md_and_speciesnet`; the other steps don't apply in this scenario.
 
 ## Fine-tuning SpeciesNet
 
 In some cases it may be helpful to fine-tune SpeciesNet on your own data, e.g. to create a complementary classifier for species-specific sex/age classification, or to adapt to local species that SpeciesNet doesn't handle well.  We don't have an "official" path to fine-tuning SpeciesNet, but we wrote a [tutorial](https://agentmorris.github.io/speciesnet-fine-tuning/) for DIY fine-tuning that may be helpful.
 
-Also, a shout out to the fine-tuned SpeciesNets we're aware of as of June 2026, let us know if we're missing any!
+Also, a shout out to the fine-tuned SpeciesNets we're aware of as of October 2026, let us know if we're missing any!
 
+* [Addax Data Science Canadian Subarctic](https://huggingface.co/Addax-Data-Science/ARC-ADS-v1) (fine-tuned SpeciesNet for 39 categories from Northern Manitoba)
 * [Addax Data Science Central Indian Wildlife](https://huggingface.co/Addax-Data-Science/IND-ADS-v1) (fine-tuned SpeciesNet for 40 categories from Central India)
 * [Addax Data Science Northern Territory Vertebrates](https://huggingface.co/Addax-Data-Science/ANT-ADS-v1) (fine-tuned SpeciesNet for 140 categories from NT, Australia)
 * [Addax Data Science Hawaii](https://huggingface.co/Addax-Data-Science/HWI-ADS-v1) (fine-tuned SpeciesNet for 13 taxa in Hawaii)
diff --git a/advanced_topics.md b/advanced_topics.md
index a25f205..85ebd48 100644
--- a/advanced_topics.md
+++ b/advanced_topics.md
@@ -7,7 +7,6 @@
 - [Alternative installation variants of the SpeciesNet Python package](#alternative-installation-variants)
 - [SpeciesNet model variants](#speciesnet-model-variants)
 - [Alternative input formats for run_model](#alternative-input-formats-for-run_model)
-- [Alternative output formats for run_model](#alternative-output-formats-for-run_model)
 - [Contributing code](#contributing-code)
 - [Build status](#build-status)
 
@@ -61,7 +60,7 @@ Depending on how you plan to run SpeciesNet, you may want to install additional
 
 There are two variants of the SpeciesNet classifier, which lend themselves to different ensemble strategies:
 
-- [v4.0.3a](model_cards/v4.0.1a.md) (default): Always-crop model, i.e. we run the detector first and crop the image to the top detection bounding box before feeding it to the species classifier.
+- [v4.0.3a](model_cards/v4.0.1a.md) (default): Always-crop model, i.e. we run the detector first, then run the classifier on the pixels within each detection above a confidence threshold.
 - [v4.0.3b](model_cards/v4.0.1b.md): Full-image model, i.e. we run both the detector and the species classifier on the full image, independently.
 
 Both links point to the model cards for the 4.0.1 models; model cards were not updated for the 4.0.3 release, which only included changes to geofencing rules and minor taxonomy updates.
@@ -103,53 +102,6 @@ admin1_region is currently only supported in the US, where valid values for admi
 
 Latitude and longitude are only used to determine admin1_region, so if you are specifying a state code, you don't need to specify latitude and longitude.
 
-## Alternative output formats for run_model
-
-The main README describes the typical output format for run_model, when you are running the full ensemble (detector and classifier).  This section describes the output format when you are running only the classifier or detector.
-
-### Classifier-only inference
-
-```text
-{
-    "predictions": [
-        {
-            "filepath": str  => Image filepath.
-            "failures": list[str] (optional)  => List of internal components that failed during prediction (in this case, only "CLASSIFIER" can be in that list). If absent, the prediction was successful.
-            "classifications": {  => dict (optional)  => Top-5 classifications. Included only if "CLASSIFIER" if not part of the "failures" field.
-                "classes": list[str]  => List of top-5 classes predicted by the classifier, matching the decreasing order of their scores below.
-                "scores": list[float]  => List of scores corresponding to top-5 classes predicted by the classifier, in decreasing order.
-                "target_classes": list[str] (optional)  => List of target classes, only present if target classes are passed as arguments.
-                "target_logits": list[float] (optional)  => Raw confidence scores (logits) of the target classes, only present if target classes are passed as arguments.
-            }
-        },
-        ...  => A response will contain one prediction for each instance in the request.
-    ]
-}
-```
-
-### Detector-only inference
-
-```text
-{
-    "predictions": [
-        {
-            "filepath": str  => Image filepath.
-            "failures": list[str] (optional)  => List of internal components that failed during prediction (in this case, only "DETECTOR" can be in that list). If absent, the prediction was successful.
-            "detections": [  => list (optional)  => List of detections with confidence scores > 0.01, in decreasing order of their scores. Included only if "DETECTOR" if not part of the "failures" field.
-                {
-                    "category": str  => Detection class "1" (= animal), "2" (= human) or "3" (= vehicle) from MegaDetector's raw output.
-                    "label": str  => Detection class "animal", "human" or "vehicle", matching the "category" field above. Added for readability purposes.
-                    "conf": float  => Confidence score of the current detection.
-                    "bbox": list[float]  => Bounding box coordinates, in (xmin, ymin, width, height) format, of the current detection. Coordinates are normalized to the [0.0, 1.0] range, relative to the image dimensions.
-                },
-                ...  => A prediction can contain zero or multiple detections.
-            ]
-        },
-        ...  => A response will contain one prediction for each instance in the request.
-    ]
-}
-```
-
 ## Contributing code
 
 If you're interested in contributing to our repo, rather than installing via pip, we recommend cloning the repo, creating a Python virtual environment for development, and installing the package locally using the following command:
diff --git a/speciesnet/classifier.py b/speciesnet/classifier.py
index 0621abb..fd23747 100644
--- a/speciesnet/classifier.py
+++ b/speciesnet/classifier.py
@@ -127,7 +127,7 @@ def preprocess(
         img: Optional[PIL.Image.Image],
         bboxes: Optional[list[BBox]] = None,
         resize: bool = True,
-    ) -> list[Optional[PreprocessedImage]]:
+    ) -> Optional[list[PreprocessedImage]]:
         """Preprocesses an image according to this classifier's needs.
 
         This method prepares an input image for classification. It handles
@@ -135,8 +135,10 @@ def preprocess(
         input size for the classifier model.
 
         In `always_crop` mode images are cropped according to the bounding boxes
-        provided. In `full_image` mode the top and bottom of the image are cropped
-        to prevent learning correlations between camera brand and species priors.
+        provided, producing one crop per bounding box (and no crops at all if no
+        bounding boxes are provided). In `full_image` mode, bounding boxes are ignored,
+        and the top and bottom of the image are cropped to prevent learning
+        correlations between camera brand and species priors, producing a single crop.
         See the paper for more details.
 
         Args:
@@ -149,52 +151,36 @@ def preprocess(
                 Whether to resize the image to some expected dimensions.
 
         Returns:
-            A list of preprocessed images, one for each bounding box.
+            A list of preprocessed images (one element per bounding box in `always_crop`
+            mode, a single element in `full_image` mode), or `None` if no image was provided.
         """
         if img is None:
-            return []
+            return None
 
         img_tensor = F.pil_to_tensor(img)  # HWC to CHW.
         img_tensor = F.convert_image_dtype(img_tensor, torch.float32)
 
         results = []
         if self.model_info.type_ == "always_crop":
-            if bboxes:
-                for bbox in bboxes:
-                    crop = F.crop(
-                        img_tensor,
-                        int(bbox.ymin * img.height),
-                        int(bbox.xmin * img.width),
-                        int(bbox.height * img.height),
-                        int(bbox.width * img.width),
-                    )
-                    if resize:
-                        crop = F.resize(
-                            crop,
-                            [
-                                SpeciesNetClassifier.IMG_SIZE,
-                                SpeciesNetClassifier.IMG_SIZE,
-                            ],
-                            antialias=False,
-                        )
-                    crop = F.convert_image_dtype(crop, torch.uint8)
-                    crop = crop.permute([1, 2, 0])  # CHW to HWC.
-                    results.append(
-                        PreprocessedImage(crop.numpy(), img.width, img.height)
-                    )
-            else:
-                # If no bounding boxes are provided but we must crop, we return
-                # an uncropped image.
+            for bbox in bboxes or []:
+                crop = F.crop(
+                    img_tensor,
+                    int(bbox.ymin * img.height),
+                    int(bbox.xmin * img.width),
+                    int(bbox.height * img.height),
+                    int(bbox.width * img.width),
+                )
                 if resize:
                     crop = F.resize(
-                        img_tensor,
-                        [SpeciesNetClassifier.IMG_SIZE, SpeciesNetClassifier.IMG_SIZE],
+                        crop,
+                        [
+                            SpeciesNetClassifier.IMG_SIZE,
+                            SpeciesNetClassifier.IMG_SIZE,
+                        ],
                         antialias=False,
                     )
-                else:
-                    crop = img_tensor
                 crop = F.convert_image_dtype(crop, torch.uint8)
-                crop = crop.permute([1, 2, 0])
+                crop = crop.permute([1, 2, 0])  # CHW to HWC.
                 results.append(PreprocessedImage(crop.numpy(), img.width, img.height))
 
         elif self.model_info.type_ == "full_image":
@@ -214,43 +200,43 @@ def preprocess(
 
             crop = F.convert_image_dtype(crop, torch.uint8)
             crop = crop.permute([1, 2, 0])  # CHW to HWC.
-
-            # Note: For full_image model, we just process the image once even if
-            # there are many bboxes. We duplicate the result so there's a 1-to-1
-            # match with the requested bboxes limit in downstream logic.
-            result_img = PreprocessedImage(crop.numpy(), img.width, img.height)
-            if bboxes:
-                results.extend([result_img for _ in bboxes])
-            else:
-                results.append(result_img)
+            results.append(PreprocessedImage(crop.numpy(), img.width, img.height))
 
         return results
 
     def predict(
-        self, filepath: str, imgs: list[Optional[PreprocessedImage]]
+        self,
+        filepath: str,
+        imgs: Optional[list[PreprocessedImage]],
+        num_classes: Optional[int] = 5,
     ) -> dict[str, Any]:
-        """Runs inference on a given preprocessed image (or crops for the image).
+        """Runs inference on the preprocessed crops of a given image.
 
         Args:
             filepath:
                 Location of image to run inference on. Used for reporting purposes only,
                 and not for loading the image.
             imgs:
-                List of preprocessed image crops to run inference on. If empty, a
-                failure message is reported back.
+                List of preprocessed image crops to run inference on. If `None`, a
+                failure message is reported back. If empty, there is nothing to
+                classify and an empty list of classifications is reported back.
+            num_classes:
+                Number of top classes to report for each crop. If `None`, all classes
+                are reported.
 
         Returns:
-            A dict containing either the top-5 classifications for the given image
-            crops (in decreasing order of confidence scores), or a failure message
+            A dict containing either the top classifications for each of the given
+            image crops (in decreasing order of confidence scores), or a failure message
             if no preprocessed image was provided.
         """
 
-        return self.batch_predict([filepath], [imgs])[0]
+        return self.batch_predict([filepath], [imgs], num_classes=num_classes)[0]
 
     def batch_predict(
         self,
         filepaths: list[str],
-        imgs_list: list[list[Optional[PreprocessedImage]]],
+        imgs_list: list[Optional[list[PreprocessedImage]]],
+        num_classes: Optional[int] = 5,
     ) -> list[dict[str, Any]]:
         """Runs inference on a batch of preprocessed images.
 
@@ -259,33 +245,28 @@ def batch_predict(
                 List of image locations to run inference on. Used for reporting purposes
                 only, and not for loading the images.
             imgs_list:
-                List of lists of preprocessed image crops to run inference on. If an
-                image is `None`, a corresponding failure message is reported back.
+                List of lists of preprocessed image crops to run inference on, one list
+                per image. If a list is `None`, a corresponding failure message is
+                reported back. If a list is empty, an empty list of classifications is
+                reported back.
+            num_classes:
+                Number of top classes to report for each crop. If `None`, all classes
+                are reported.
 
         Returns:
             A list of dict results. Each dict result contains `classifications_list`
-            with the top-5 classifications for each corresponding crop image (in
-            decreasing order of confidence scores for each crop), or a failure
-            message if no preprocessed image was provided.
+            with the top classifications for each corresponding crop image, in the same
+            order as the crops (and in decreasing order of confidence scores for each
+            crop), or a failure message if no preprocessed image was provided.
         """
 
         predictions = {}
 
         inference_filepaths = []
-        inference_num_crops = []
         batch_arr = []
 
         for filepath, imgs in zip(filepaths, imgs_list):
-            if not imgs:
-                predictions[filepath] = {
-                    "filepath": filepath,
-                    "failures": [Failure.CLASSIFIER.name],
-                }
-                continue
-
-            valid_imgs = [img for img in imgs if img is not None]
-
-            if not valid_imgs:
+            if imgs is None:
                 predictions[filepath] = {
                     "filepath": filepath,
                     "failures": [Failure.CLASSIFIER.name],
@@ -294,15 +275,9 @@ def batch_predict(
 
             predictions[filepath] = {"filepath": filepath, "classifications_list": []}
 
-            if self.model_info.type_ == "full_image":
+            for img in imgs:
                 inference_filepaths.append(filepath)
-                inference_num_crops.append(len(valid_imgs))
-                batch_arr.append(valid_imgs[0].arr / 255.0)
-            else:
-                for img in valid_imgs:
-                    inference_filepaths.append(filepath)
-                    inference_num_crops.append(1)
-                    batch_arr.append(img.arr / 255.0)
+                batch_arr.append(img.arr / 255.0)
 
         if not batch_arr:
             return [predictions[fp] for fp in filepaths if fp in predictions]
@@ -327,12 +302,13 @@ def batch_predict(
                 torch.mps.empty_cache()
         logits = torch.cat(logits_list, dim=0)
         scores = torch.softmax(logits, dim=-1)
-        scores, indices = torch.topk(scores, k=5, dim=-1)
+        num_labels = scores.shape[-1]
+        k = num_labels if num_classes is None else min(num_classes, num_labels)
+        scores, indices = torch.topk(scores, k=k, dim=-1)
 
-        for file_idx, (filepath, num_crops, scores_arr, indices_arr) in enumerate(
+        for crop_idx, (filepath, scores_arr, indices_arr) in enumerate(
             zip(
                 inference_filepaths,
-                inference_num_crops,
                 scores.numpy(),
                 indices.numpy(),
             )
@@ -348,15 +324,12 @@ def batch_predict(
                     {
                         "target_classes": self.target_labels,
                         "target_logits": [
-                            float(logits[file_idx][idx]) for idx in self.target_idx
+                            float(logits[crop_idx][idx]) for idx in self.target_idx
                         ],
                     }
                 )
 
-            for _ in range(num_crops):
-                predictions[filepath]["classifications_list"].append(
-                    {k: list(v) for k, v in classification.items()}
-                )
+            predictions[filepath]["classifications_list"].append(classification)
 
         return [
             predictions[filepath] for filepath in filepaths if filepath in predictions
diff --git a/speciesnet/classifier_test.py b/speciesnet/classifier_test.py
index ada4be5..1377feb 100644
--- a/speciesnet/classifier_test.py
+++ b/speciesnet/classifier_test.py
@@ -99,23 +99,24 @@ def test_preprocess(  # pylint: disable=too-many-positional-arguments
         img_green_w480_h480,  # output
     ) -> None:
 
-        assert classifier.preprocess(None) == []
+        assert classifier.preprocess(None) is None
 
         if classifier.model_info.type_ == "always_crop":
 
-            preprocessed = classifier.preprocess(
+            # Without bounding boxes there is nothing to crop.
+            assert not classifier.preprocess(
                 img_red_green_blue_w100_h150_h700_h150,
                 resize=False,
-            )[0]
-            assert preprocessed
-            assert preprocessed.orig_width == 100
-            assert preprocessed.orig_height == 1000
-            assert preprocessed.arr.shape == (1000, 100, 3)
-            assert_array_equal(preprocessed.arr, img_red_green_blue_w100_h150_h700_h150)
+            )
+            assert not classifier.preprocess(
+                img_red_green_blue_w100_h150_h700_h150,
+                bboxes=[],
+                resize=False,
+            )
 
             preprocessed = classifier.preprocess(
                 img_red_green_blue_w100_h150_h700_h150,
-                bboxes=[],
+                bboxes=[BBox(0.0, 0.0, 1.0, 1.0)],
                 resize=False,
             )[0]
             assert preprocessed
@@ -180,11 +181,17 @@ def test_preprocess(  # pylint: disable=too-many-positional-arguments
 
         elif classifier.model_info.type_ == "full_image":
 
-            preprocessed = classifier.preprocess(
+            # Bounding boxes are ignored, and a single crop is produced.
+            preprocessed_list = classifier.preprocess(
                 img_red_green_blue_w100_h150_h700_h150,
-                bboxes=[BBox(10 / 100, 150 / 1000, 40 / 100, 100 / 1000)],
+                bboxes=[
+                    BBox(10 / 100, 150 / 1000, 40 / 100, 100 / 1000),
+                    BBox(0 / 100, 150 / 1000, 100 / 100, 700 / 1000),
+                ],
                 resize=False,
-            )[0]
+            )
+            assert len(preprocessed_list) == 1
+            preprocessed = preprocessed_list[0]
             assert preprocessed
             assert preprocessed.orig_width == 100
             assert preprocessed.orig_height == 1000
@@ -232,10 +239,15 @@ def test_preprocess(  # pylint: disable=too-many-positional-arguments
     def test_predict(self, classifier, img_green_w480_h480) -> None:
 
         filepath = "missing.jpg"
-        prediction = classifier.predict(filepath, [])
+        prediction = classifier.predict(filepath, None)
         assert prediction["filepath"] == filepath
         assert "failures" in prediction
 
+        # No crops means nothing to classify, which is not a failure.
+        filepath = "no_crops.jpg"
+        prediction = classifier.predict(filepath, [])
+        assert prediction == {"filepath": filepath, "classifications_list": []}
+
         filepath = "green.png"
         prediction = classifier.predict(
             filepath, [PreprocessedImage(np.asarray(img_green_w480_h480), 480, 480)]
@@ -256,18 +268,31 @@ def test_predict(self, classifier, img_green_w480_h480) -> None:
         ]
         prediction = classifier.predict(filepath, crops)
         assert len(prediction["classifications_list"]) == 2
+        for classifications in prediction["classifications_list"]:
+            assert len(classifications["classes"]) == 5
+            assert len(classifications["scores"]) == 5
+
+        # Test the number of reported classes.
+        prediction = classifier.predict(filepath, crops, num_classes=2)
+        for classifications in prediction["classifications_list"]:
+            assert len(classifications["classes"]) == 2
+            assert len(classifications["scores"]) == 2
+        prediction = classifier.predict(filepath, crops[:1], num_classes=None)
+        assert len(prediction["classifications_list"][0]["classes"]) == len(
+            classifier.labels
+        )
 
     @pytest.fixture(
         # Expected classifications.
         params=[
             (
                 "test_data/blank.jpg",
-                [],
+                [BBox(0.0, 0.0, 1.0, 1.0)],
                 BLANK,
             ),
             (
                 "test_data/blank2.jpg",
-                [],
+                [BBox(0.0, 0.0, 1.0, 1.0)],
                 BLANK,
             ),
             (
@@ -423,7 +448,7 @@ def test_target_species_batched_vs_non_batched(
             ),
             ("test_data/domestic_dog.jpg", [BBox(0.2377, 0.08398, 0.5161, 0.6497)]),
             ("test_data/human.jpg", [BBox(0.7115, 0.4976, 0.0664, 0.2424)]),
-            ("test_data/blank.jpg", []),
+            ("test_data/blank.jpg", [BBox(0.0, 0.0, 1.0, 1.0)]),
         ]
 
         # Preprocess all images
diff --git a/speciesnet/constants.py b/speciesnet/constants.py
index c416b87..9cb1602 100644
--- a/speciesnet/constants.py
+++ b/speciesnet/constants.py
@@ -16,7 +16,11 @@
 
 __all__ = [
     "Classification",
+    "DEFAULT_DETECTION_THRESHOLD_FOR_CLASSIFICATION",
+    "DEFAULT_MAX_CLASSIFICATIONS_PER_DETECTION",
     "Detection",
+    "FORMAT_VERSION",
+    "NUM_CLASSIFICATIONS_FOR_ENSEMBLE",
     "mps_inference_lock",
 ]
 
@@ -25,6 +29,21 @@
 import threading
 from typing import Iterator, Optional
 
+# Version of the output format written by SpeciesNet, reported in the "info" section of
+# output files.
+FORMAT_VERSION = "20261015"
+
+# Detections with a confidence score at or above this threshold are run through the
+# classifier (unless the maximum number of classifications per image is reached).
+DEFAULT_DETECTION_THRESHOLD_FOR_CLASSIFICATION = 0.1
+
+# Number of top classes to report in the raw classifications of each detection.
+DEFAULT_MAX_CLASSIFICATIONS_PER_DETECTION = 2
+
+# Number of top classes the ensemble uses to compute processed classifications,
+# independently of how many classes are reported in the output.
+NUM_CLASSIFICATIONS_FOR_ENSEMBLE = 5
+
 
 class Classification(str, enum.Enum):
     """Enum of common classification values.
@@ -44,6 +63,7 @@ class Classification(str, enum.Enum):
 class Detection(str, enum.Enum):
     """Enum of all possible detection values."""
 
+    WHOLE_IMAGE = "whole_image_classification"
     ANIMAL = "animal"
     HUMAN = "human"
     VEHICLE = "vehicle"
@@ -54,14 +74,17 @@ def from_category(cls, category: str) -> Optional["Detection"]:
 
         Args:
             category: Numeric category from the detector, provided as a string (e.g.
-            "1", "2", "3").
+            "0", "1", "2", "3"). Category "0" never comes from the detector itself; it
+            represents the single whole-image pseudo-detection used by whole-image
+            classifiers.
 
         Returns:
             Enum detection value corresponding to the given numeric category. If
-            category is not one of "1", "2" or "3", returns `None`.
+            category is not one of "0", "1", "2" or "3", returns `None`.
         """
 
         category_to_label = {
+            "0": Detection.WHOLE_IMAGE,
             "1": Detection.ANIMAL,
             "2": Detection.HUMAN,
             "3": Detection.VEHICLE,
diff --git a/speciesnet/ensemble.py b/speciesnet/ensemble.py
index 1f1687d..2aa07d0 100644
--- a/speciesnet/ensemble.py
+++ b/speciesnet/ensemble.py
@@ -19,6 +19,7 @@
 ]
 
 import json
+import math
 import time
 from typing import Any, Callable, Optional
 
@@ -26,11 +27,15 @@
 from humanfriendly import format_timespan
 
 from speciesnet.constants import Classification
+from speciesnet.constants import DEFAULT_MAX_CLASSIFICATIONS_PER_DETECTION
+from speciesnet.constants import Detection
 from speciesnet.constants import Failure
+from speciesnet.constants import NUM_CLASSIFICATIONS_FOR_ENSEMBLE
 from speciesnet.ensemble_prediction_combiner import combine_predictions
 from speciesnet.geofence_utils import geofence_animal_classification
 from speciesnet.geofence_utils import roll_up_labels_to_first_matching_level
 from speciesnet.utils import ModelInfo
+from speciesnet.utils import whole_image_detection
 
 # Handy type aliases.
 PredictionLabelType = str
@@ -38,6 +43,65 @@
 PredictionSourceType = str
 PredictionType = tuple[PredictionLabelType, PredictionScoreType, PredictionSourceType]
 
+_WHOLE_IMAGE_CATEGORY = whole_image_detection()["category"]
+
+
+def _top_classes(classifications: dict, num_classes: Optional[int]) -> dict:
+    """Returns a copy of a classification dict, limited to its top classes.
+
+    Args:
+        classifications:
+            Classification dict that includes "classes" and "scores" among its keys, in
+            decreasing order of scores. Other keys (e.g. target classes and logits) are
+            copied unchanged.
+        num_classes:
+            Number of top classes to keep. If `None`, all classes are kept.
+
+    Returns:
+        The limited classification dict.
+    """
+
+    result = dict(classifications)
+    if num_classes is not None:
+        result["classes"] = classifications["classes"][:num_classes]
+        result["scores"] = classifications["scores"][:num_classes]
+    return result
+
+
+def _check_matching_detections(
+    filepath: str, classified_detections: list[dict], detections: list[dict]
+) -> None:
+    """Checks that classifier results refer to the same detections as the detector.
+
+    Args:
+        filepath:
+            Image filepath, used for reporting purposes only.
+        classified_detections:
+            Detections attached to the classifier results for the image.
+        detections:
+            Detections from the detector results for the image.
+
+    Raises:
+        ValueError:
+            If the two lists of detections don't match.
+    """
+
+    matching = len(classified_detections) == len(detections) and all(
+        det1["category"] == det2["category"]
+        and len(det1["bbox"]) == len(det2["bbox"])
+        and all(
+            math.isclose(x1, x2, abs_tol=1e-4)
+            for x1, x2 in zip(det1["bbox"], det2["bbox"])
+        )
+        for det1, det2 in zip(classified_detections, detections)
+    )
+    if not matching:
+        raise ValueError(
+            f"Classifications for `{filepath}` were computed on different detections "
+            f"than the ones provided. Make sure classifications and detections come "
+            f"from the same detector output."
+        )
+
 
 def _load_taxonomy_from_file(taxonomy_file):
     """Loads the taxonomy .txt file into a dict."""
@@ -126,16 +190,24 @@ def combine(  # pylint: disable=too-many-positional-arguments
         detector_results: dict[str, dict],
         geolocation_results: dict[str, dict],
         partial_predictions: Optional[dict[str, dict]] = None,
-        max_classifications: Optional[int] = None,
+        max_classifications_per_detection: Optional[
+            int
+        ] = DEFAULT_MAX_CLASSIFICATIONS_PER_DETECTION,
     ) -> list[dict[str, Any]]:
         """Ensembles classifications and detections for a list of images.
 
+        Each classified detection is ensembled independently of all other detections,
+        producing one processed classification per classified detection.
+
         Args:
             filepaths:
                 List of filepaths to ensemble predictions for.
             classifier_results:
                 Dict of classifier results, with keys given by the filepaths to ensemble
-                predictions for.
+                predictions for. Each result provides either "failures" or a list of
+                "detections", where classified detections have a "raw_classifications"
+                field. For whole-image classifiers, that list contains a single
+                whole-image pseudo-detection.
             detector_results:
                 Dict of detector results, with keys given by the filepaths to ensemble
                 predictions for.
@@ -146,11 +218,19 @@ def combine(  # pylint: disable=too-many-positional-arguments
                 Dict of partial predictions from previous ensemblings, with keys given
                 by the filepaths for which predictions where already ensembled. Used to
                 skip re-ensembling for the matching filepaths.
-            max_classifications:
-                Optional maximum number of classifications to return per image.
+            max_classifications_per_detection:
+                Number of top classes to report in the raw classifications of each
+                detection. If `None`, all available classes are reported. Processed
+                classifications are always computed from the top
+                `NUM_CLASSIFICATIONS_FOR_ENSEMBLE` classes, regardless of this value.
 
         Returns:
             List of ensembled predictions.
+
+        Raises:
+            ValueError:
+                If the detections attached to the classifier results of an image don't
+                match the detections from the detector results for that image.
         """
 
         results = []
@@ -166,11 +246,9 @@ def combine(  # pylint: disable=too-many-positional-arguments
                 filepath in classifier_results
                 and "failures" not in classifier_results[filepath]
             ):
-                classifications_list = classifier_results[filepath][
-                    "classifications_list"
-                ]
+                classified_detections = classifier_results[filepath]["detections"]
             else:
-                classifications_list = None
+                classified_detections = None
                 failure |= Failure.CLASSIFIER
             if (
                 filepath in detector_results
@@ -186,7 +264,75 @@ def combine(  # pylint: disable=too-many-positional-arguments
                 geolocation = {}
                 failure |= Failure.GEOLOCATION
 
-            # Add as much raw information as possible to the prediction result.
+            # Assemble the output detections, and pair each classified detection with
+            # the detection to ensemble its classifications with.
+            output_detections: Optional[list[dict]] = None
+            pairs: list[tuple[int, dict]] = []
+
+            # Handle the whole-image classifier case.
+            if classified_detections and (
+                classified_detections[0]["category"] == _WHOLE_IMAGE_CATEGORY
+            ):
+                # Whole-image classifier: the pseudo-detection comes first, followed by
+                # the (unclassified) detector output. The pseudo-detection is ensembled
+                # with the top detection, like the full image always was.
+                output_detections = [dict(classified_detections[0])]
+                if detections is not None:
+                    output_detections += [dict(det) for det in detections]
+                    pairs.append(
+                        (
+                            0,
+                            (
+                                detections[0]
+                                if detections
+                                else {"label": Detection.ANIMAL, "conf": 0.0}
+                            ),
+                        )
+                    )
+            elif detections is not None:
+                if classified_detections is not None:
+                    _check_matching_detections(
+                        filepath, classified_detections, detections
+                    )
+                    output_detections = [dict(det) for det in classified_detections]
+                    pairs = [
+                        (idx, det)
+                        for idx, det in enumerate(output_detections)
+                        if "raw_classifications" in det
+                    ]
+                else:
+                    output_detections = [dict(det) for det in detections]
+
+            # Ensemble each classified detection.
+            if pairs:
+                processed_classifications = self.prediction_combiner(
+                    classifications_list=[
+                        _top_classes(
+                            output_detections[idx]["raw_classifications"],
+                            NUM_CLASSIFICATIONS_FOR_ENSEMBLE,
+                        )
+                        for idx, _ in pairs
+                    ],
+                    detections=[det for _, det in pairs],
+                    country=geolocation.get("country"),
+                    admin1_region=geolocation.get("admin1_region"),
+                    taxonomy_map=self.taxonomy_map,
+                    geofence_map=self.geofence_map,
+                    enable_geofence=self.enable_geofence,
+                    geofence_fn=geofence_animal_classification,
+                    roll_up_fn=roll_up_labels_to_first_matching_level,
+                )
+                for (idx, _), processed in zip(pairs, processed_classifications):
+                    output_detections[idx]["processed_classification"] = processed
+
+            # Only report the requested number of top classes.
+            for det in output_detections or []:
+                if "raw_classifications" in det:
+                    det["raw_classifications"] = _top_classes(
+                        det["raw_classifications"], max_classifications_per_detection
+                    )
+
+            # Assemble the prediction result.
             result = {
                 "filepath": filepath,
                 "failures": (
@@ -196,27 +342,10 @@ def combine(  # pylint: disable=too-many-positional-arguments
                 "admin1_region": geolocation.get("admin1_region"),
                 "latitude": geolocation.get("latitude"),
                 "longitude": geolocation.get("longitude"),
-                "classifications_list": classifications_list,
-                "detections": detections,
+                "detections": output_detections,
             }
             result = {key: value for key, value in result.items() if value is not None}
 
-            # Most importantly, ensemble everything into predictions.
-            if classifications_list is not None and detections is not None:
-                result["processed_classifications"] = self.prediction_combiner(
-                    classifications_list=classifications_list,
-                    detections=detections,
-                    country=geolocation.get("country"),
-                    admin1_region=geolocation.get("admin1_region"),
-                    taxonomy_map=self.taxonomy_map,
-                    geofence_map=self.geofence_map,
-                    enable_geofence=self.enable_geofence,
-                    geofence_fn=geofence_animal_classification,
-                    roll_up_fn=roll_up_labels_to_first_matching_level,
-                    max_classifications=max_classifications,
-                )
-
-            # Finally, report the model version.
             result["model_version"] = self.model_info.version
 
             results.append(result)
diff --git a/speciesnet/ensemble_prediction_combiner.py b/speciesnet/ensemble_prediction_combiner.py
index 45fb7b4..b870d42 100644
--- a/speciesnet/ensemble_prediction_combiner.py
+++ b/speciesnet/ensemble_prediction_combiner.py
@@ -36,23 +36,22 @@ def combine_predictions(
     enable_geofence: bool,
     geofence_fn: Callable,
     roll_up_fn: Callable,
-    max_classifications: Optional[int] = None,
 ) -> list[dict]:
-    """Ensembles classifications and detections from multiple bounding boxes.
+    """Ensembles each classification with its corresponding detection.
 
-    This operation leverages multiple heuristics to make the most of the classifier and
-    the detector predictions through a complex set of decisions. It introduces various
-    thresholds to identify humans, vehicles, blanks, animals at species level, animals
-    at higher taxonomy levels and even unknowns.
+    This operation leverages heuristics to make the most of the classifier and
+    the detector predictions.
+
+    Each element of `classifications_list` is ensembled with the element of
+    `detections` at the same position, independently of all other elements.
 
     Args:
         classifications_list:
-            List of dicts containing classification results for each bounding box.
+            List of dicts containing classification results, one per detection.
             "classes" and "scores" are expected to be provided among the dict keys.
         detections:
-            List of detection results, sorted in decreasing order of their confidence
-            score. Each detection is expected to be a dict providing "label" and "conf"
-            among its keys.
+            List of detection results, one per classification. Each detection is
+            expected to be a dict providing "label" and "conf" among its keys.
         country:
             Country (in ISO 3166-1 alpha-3 format) associated with predictions.
             Optional.
@@ -69,37 +68,33 @@ def combine_predictions(
             Callable to geofence animal classifications.
         roll_up_fn:
             Callable to roll up labels to the first matching level.
-        max_classifications:
-            Maximum number of predictions to return per image. Defaults to returning
-            all processed predictions.
 
     Returns:
-        A list of dicts describing the ensemble results.
+        A list of dicts describing the ensemble results, one per classification and in
+        the same order. Each dict includes "class", "score" and "source" keys.
+
+    Raises:
+        ValueError:
+            If `classifications_list` and `detections` have different lengths.
     """
-    effective_detections = (
-        detections
-        if detections
-        else [{"label": Detection.ANIMAL, "conf": 0.0, "bbox": None}]
-    )
+    if len(classifications_list) != len(detections):
+        raise ValueError(
+            f"Expected one detection per classification, got "
+            f"{len(classifications_list)} classifications and "
+            f"{len(detections)} detections."
+        )
+
     results = []
 
-    for i, det in enumerate(effective_detections):
+    for classifications, det in zip(classifications_list, detections):
         det_class = det["label"]
         det_score = det["conf"]
         prediction = None
         score = 0.0
         source = ""
 
-        if i < len(classifications_list) and classifications_list[i]:
-            classifications = classifications_list[i]
-            top_classification_class = classifications.get(
-                "classes", [Classification.UNKNOWN]
-            )[0]
-            top_classification_score = classifications.get("scores", [0.0])[0]
-        else:
-            classifications = {"classes": [Classification.UNKNOWN], "scores": [0.0]}
-            top_classification_class = Classification.UNKNOWN
-            top_classification_score = 0.0
+        top_classification_class = classifications["classes"][0]
+        top_classification_score = classifications["scores"][0]
 
         if det_class == Detection.HUMAN:
             # Threshold #1a: high-confidence HUMAN detections.
@@ -241,23 +236,14 @@ def combine_predictions(
 
         results.append(
             {
-                "prediction": (
+                "class": (
                     prediction.value
                     if isinstance(prediction, Classification)
                     else prediction
                 ),
-                "prediction_score": score,
-                "prediction_source": source,
-                "bbox": det.get("bbox") if "bbox" in det else None,
+                "score": score,
+                "source": source,
             }
         )
 
-    results.sort(
-        key=lambda x: (
-            x["prediction_score"] if x["prediction_score"] is not None else 0.0
-        ),
-        reverse=True,
-    )
-    if max_classifications is not None and max_classifications > 0:
-        return results[:max_classifications]
     return results
diff --git a/speciesnet/ensemble_prediction_combiner_test.py b/speciesnet/ensemble_prediction_combiner_test.py
index 3d5f74b..80223ba 100644
--- a/speciesnet/ensemble_prediction_combiner_test.py
+++ b/speciesnet/ensemble_prediction_combiner_test.py
@@ -155,6 +155,7 @@ def combine_predictions_for_single_item(  # pylint: disable=too-many-positional-
     roll_up_fn,
 ):
     """Test helper transforming single-item permutations into the list format."""
+    assert len(detections) == 1
     results = combine_predictions(
         classifications_list=[classifications],
         detections=detections,
@@ -166,11 +167,12 @@ def combine_predictions_for_single_item(  # pylint: disable=too-many-positional-
         geofence_fn=geofence_fn,
         roll_up_fn=roll_up_fn,
     )
+    assert len(results) == 1
     # The tests expect a flat tuple of (label, score, source) to be asserted
     return (
-        results[0]["prediction"],
-        results[0]["prediction_score"],
-        results[0]["prediction_source"],
+        results[0]["class"],
+        results[0]["score"],
+        results[0]["source"],
     )
 
 
@@ -432,38 +434,27 @@ def test_combine_predictions_for_all_items(
             roll_up_fn=mock_roll_up_fn_return_none,
         )
 
-        assert len(results) == 3
-
-        # First is HUMAN
-        assert results[0]["prediction"] == Classification.HUMAN
-        assert results[0]["prediction_score"] == 0.95
-        assert results[0]["prediction_source"] == "detector"
-        assert results[0]["bbox"] == [0.1, 0.1, 0.2, 0.2]
-
-        # Second is LION (gets geofenced)
-        assert results[1]["prediction"] == "geofenced_label"
-        assert results[1]["prediction_score"] == 0.9
-        assert results[1]["prediction_source"] == "geofence_source"
-        assert results[1]["bbox"] == [0.5, 0.5, 0.6, 0.6]
-
-        # Third is VEHICLE
-        assert results[2]["prediction"] == Classification.VEHICLE
-        assert results[2]["prediction_score"] == 0.85
-        assert results[2]["prediction_source"] == "detector"
-        assert results[2]["bbox"] == [0.8, 0.8, 0.9, 0.9]
+        assert results == [
+            # HUMAN detection.
+            {"class": Classification.HUMAN, "score": 0.95, "source": "detector"},
+            # LION classification (gets geofenced).
+            {"class": "geofenced_label", "score": 0.9, "source": "geofence_source"},
+            # VEHICLE detection.
+            {"class": Classification.VEHICLE, "score": 0.85, "source": "detector"},
+        ]
 
-    def test_combine_predictions_max_classifications(
+    def test_combine_predictions_preserves_input_order(
         self, taxonomy_map, geofence_map
     ) -> None:
+        # The second item gets a higher score than the first one, but results must
+        # still match the order of the inputs.
         classifications_list = [
-            {"classes": [HUMAN], "scores": [0.7]},
-            {"classes": [LION], "scores": [0.9]},
-            {"classes": [VEHICLE], "scores": [0.8]},
+            {"classes": [HUMAN], "scores": [0.6]},
+            {"classes": [BLANK], "scores": [0.995]},
         ]
         detections = [
-            {"label": Detection.HUMAN, "conf": 0.95, "bbox": [0.1, 0.1, 0.2, 0.2]},
-            {"label": Detection.ANIMAL, "conf": 0.9, "bbox": [0.5, 0.5, 0.6, 0.6]},
-            {"label": Detection.VEHICLE, "conf": 0.85, "bbox": [0.8, 0.8, 0.9, 0.9]},
+            {"label": Detection.HUMAN, "conf": 0.75, "bbox": [0.1, 0.1, 0.2, 0.2]},
+            {"label": Detection.ANIMAL, "conf": 0.05, "bbox": [0.5, 0.5, 0.1, 0.1]},
         ]
 
         results = combine_predictions(
@@ -476,18 +467,16 @@ def test_combine_predictions_max_classifications(
             enable_geofence=True,
             geofence_fn=mock_geofence_fn,
             roll_up_fn=mock_roll_up_fn_return_none,
-            max_classifications=2,
         )
 
-        assert len(results) == 2
-        assert results[0]["prediction"] == Classification.HUMAN
-        assert results[1]["prediction"] == "geofenced_label"
+        assert results == [
+            {"class": Classification.HUMAN, "score": 0.75, "source": "detector"},
+            {"class": Classification.BLANK, "score": 0.995, "source": "classifier"},
+        ]
 
-    def test_combine_predictions_empty_detections(
-        self, taxonomy_map, geofence_map
-    ) -> None:
+    def test_combine_predictions_empty_inputs(self, taxonomy_map, geofence_map) -> None:
         results = combine_predictions(
-            classifications_list=[{"classes": [BLANK], "scores": [0.995]}],
+            classifications_list=[],
             detections=[],
             country=None,
             admin1_region=None,
@@ -498,9 +487,7 @@ def test_combine_predictions_empty_detections(
             roll_up_fn=mock_roll_up_fn_return_none,
         )
 
-        assert len(results) == 1
-        assert results[0]["prediction"] == Classification.BLANK
-        assert results[0]["bbox"] is None
+        assert not results
 
     def test_combine_predictions_mismatched_classification_count(
         self, taxonomy_map, geofence_map
@@ -509,20 +496,15 @@ def test_combine_predictions_mismatched_classification_count(
             {"label": Detection.HUMAN, "conf": 0.95, "bbox": [0.1, 0.1, 0.2, 0.2]},
             {"label": Detection.ANIMAL, "conf": 0.6, "bbox": [0.5, 0.5, 0.6, 0.6]},
         ]
-        results = combine_predictions(
-            classifications_list=[{"classes": [HUMAN], "scores": [0.7]}],
-            detections=detections,
-            country=None,
-            admin1_region=None,
-            taxonomy_map=taxonomy_map,
-            geofence_map=geofence_map,
-            enable_geofence=True,
-            geofence_fn=mock_geofence_fn,
-            roll_up_fn=mock_roll_up_fn_return_none,
-        )
-
-        assert len(results) == 2
-        assert results[0]["prediction"] == Classification.HUMAN
-        assert results[1]["prediction"] == Classification.ANIMAL
-        assert results[1]["prediction_source"] == "detector"
-        assert results[1]["bbox"] == [0.5, 0.5, 0.6, 0.6]
+        with pytest.raises(ValueError):
+            combine_predictions(
+                classifications_list=[{"classes": [HUMAN], "scores": [0.7]}],
+                detections=detections,
+                country=None,
+                admin1_region=None,
+                taxonomy_map=taxonomy_map,
+                geofence_map=geofence_map,
+                enable_geofence=True,
+                geofence_fn=mock_geofence_fn,
+                roll_up_fn=mock_roll_up_fn_return_none,
+            )
diff --git a/speciesnet/ensemble_test.py b/speciesnet/ensemble_test.py
index a989a29..757fbfd 100644
--- a/speciesnet/ensemble_test.py
+++ b/speciesnet/ensemble_test.py
@@ -22,7 +22,7 @@
 from speciesnet.constants import Classification
 from speciesnet.ensemble import SpeciesNetEnsemble
 from speciesnet.taxonomy_utils import get_ancestor_at_level
-from speciesnet.utils import load_rgb_image
+from speciesnet.utils import whole_image_detection
 
 # fmt: off
 # pylint: disable=line-too-long
@@ -147,16 +147,22 @@ def mock_ensemble_no_geofence(
     def mock_ensemble2(self, monkeypatch, ensemble) -> SpeciesNetEnsemble:
 
         def prediction_combiner_mock(
-            classifications_list: list[dict], *args, **kwargs
+            classifications_list: list[dict],
+            detections: list[dict],
+            *args,
+            **kwargs,
         ) -> list[dict]:
             del args  # Unused.
             del kwargs  # Unused.
+            assert len(classifications_list) == len(detections)
+            # Report the detection each classification was paired with as the source.
             return [
                 {
-                    "prediction": classifications_list[0]["classes"][0],
-                    "prediction_score": classifications_list[0]["scores"][0],
-                    "prediction_source": "mock",
+                    "class": classifications["classes"][0],
+                    "score": classifications["scores"][0],
+                    "source": "mock:" + det["label"] + ":" + str(det["conf"]),
                 }
+                for classifications, det in zip(classifications_list, detections)
             ]
 
         monkeypatch.setattr(
@@ -168,13 +174,28 @@ def prediction_combiner_mock(
 
     def test_combine(self, mock_ensemble2) -> None:
 
-        blank_img = load_rgb_image("test_data/blank.jpg")
-        blank2_img = load_rgb_image("test_data/blank2.jpg")
-        blank3_img = load_rgb_image("test_data/blank3.jpg")
-        assert blank_img and blank2_img and blank3_img
-
         expected_model_version = mock_ensemble2.model_info.version
 
+        human_det = {
+            "category": "2",
+            "label": "human",
+            "conf": 0.7,
+            "bbox": [0.1, 0.2, 0.3, 0.4],
+        }
+        animal_det = {
+            "category": "1",
+            "label": "animal",
+            "conf": 0.5,
+            "bbox": [0.0, 0.1, 0.2, 0.3],
+        }
+        low_conf_det = {
+            "category": "1",
+            "label": "animal",
+            "conf": 0.05,
+            "bbox": [0.5, 0.5, 0.1, 0.1],
+        }
+        whole_image_det = whole_image_detection()
+
         filepaths = [
             "a.jpg",
             "b.jpg",
@@ -182,6 +203,7 @@ def test_combine(self, mock_ensemble2) -> None:
             "d.jpg",
             "e.jpg",
             "f.jpg",
+            "g.jpg",
         ]
         classifier_results = {
             "b.jpg": {
@@ -190,37 +212,52 @@ def test_combine(self, mock_ensemble2) -> None:
             },
             "c.jpg": {
                 "filepath": "c.jpg",
-                "classifications_list": [
+                "detections": [
                     {
-                        "classes": ["X", "Y", "Z"],
-                        "scores": [0.5, 0.3, 0.2],
+                        **animal_det,
+                        "raw_classifications": {
+                            "classes": ["X", "Y", "Z"],
+                            "scores": [0.5, 0.3, 0.2],
+                        },
                     }
                 ],
             },
             "d.jpg": {
                 "filepath": "d.jpg",
-                "classifications_list": [
+                "detections": [
                     {
-                        "classes": ["R", "S", "T"],
-                        "scores": [0.7, 0.2, 0.1],
+                        **human_det,
+                        "raw_classifications": {
+                            "classes": ["R", "S", "T"],
+                            "scores": [0.7, 0.2, 0.1],
+                        },
                     }
                 ],
             },
             "e.jpg": {
                 "filepath": "e.jpg",
-                "classifications_list": [
+                "detections": [
                     {
-                        "classes": ["K", "L", "M"],
-                        "scores": [0.9, 0.1, 0.0],
-                    }
+                        **human_det,
+                        "raw_classifications": {
+                            "classes": ["K", "L", "M"],
+                            "scores": [0.9, 0.1, 0.0],
+                            "target_classes": ["T1"],
+                            "target_logits": [1.5],
+                        },
+                    },
+                    low_conf_det,
                 ],
             },
-            "f.jpg": {
-                "filepath": "f.jpg",
-                "classifications_list": [
+            "g.jpg": {
+                "filepath": "g.jpg",
+                "detections": [
                     {
-                        "classes": ["K", "L", "M"],
-                        "scores": [0.9, 0.1, 0.0],
+                        **whole_image_det,
+                        "raw_classifications": {
+                            "classes": ["W", "V", "U"],
+                            "scores": [0.6, 0.3, 0.1],
+                        },
                     }
                 ],
             },
@@ -232,41 +269,24 @@ def test_combine(self, mock_ensemble2) -> None:
             },
             "b.jpg": {
                 "filepath": "b.jpg",
-                "detections": [
-                    {
-                        "category": "1",
-                        "label": "animal",
-                        "conf": 0.5,
-                        "bbox": [0.0, 0.1, 0.2, 0.3],
-                    }
-                ],
+                "detections": [animal_det],
             },
             "d.jpg": {
                 "filepath": "d.jpg",
-                "detections": [
-                    {
-                        "category": "2",
-                        "label": "human",
-                        "conf": 0.7,
-                        "bbox": [0.1, 0.2, 0.3, 0.4],
-                    }
-                ],
+                "detections": [human_det],
             },
             "e.jpg": {
                 "filepath": "e.jpg",
-                "detections": [
-                    {
-                        "category": "2",
-                        "label": "human",
-                        "conf": 0.7,
-                        "bbox": [0.1, 0.2, 0.3, 0.4],
-                    }
-                ],
+                "detections": [human_det, low_conf_det],
             },
             "f.jpg": {
                 "filepath": "f.jpg",
                 "detections": [],
             },
+            "g.jpg": {
+                "filepath": "g.jpg",
+                "detections": [human_det, animal_det],
+            },
         }
         geolocation_results = {
             "a.jpg": {
@@ -284,24 +304,14 @@ def test_combine(self, mock_ensemble2) -> None:
             "f.jpg": {
                 "country": "COUNTRY_F",
             },
+            "g.jpg": {
+                "country": "COUNTRY_G",
+            },
         }
         partial_predictions = {
             "f.jpg": {
                 "filepath": "f.jpg",
-                "classifications_list": [
-                    {
-                        "classes": ["XYZ"],
-                        "scores": [0.8],
-                    }
-                ],
                 "detections": [],
-                "processed_classifications": [
-                    {
-                        "prediction": "XYZ",
-                        "prediction_score": 0.4,
-                        "prediction_source": "partial",
-                    }
-                ],
                 "model_version": expected_model_version,
             },
         }
@@ -323,50 +333,30 @@ def test_combine(self, mock_ensemble2) -> None:
                 "filepath": "b.jpg",
                 "failures": ["CLASSIFIER"],
                 "country": "COUNTRY_B",
-                "detections": [
-                    {
-                        "category": "1",
-                        "label": "animal",
-                        "conf": 0.5,
-                        "bbox": [0.0, 0.1, 0.2, 0.3],
-                    }
-                ],
+                "detections": [animal_det],
                 "model_version": expected_model_version,
             },
             {
                 "filepath": "c.jpg",
                 "failures": ["DETECTOR"],
                 "country": "COUNTRY_C",
-                "classifications_list": [
-                    {
-                        "classes": ["X", "Y", "Z"],
-                        "scores": [0.5, 0.3, 0.2],
-                    }
-                ],
                 "model_version": expected_model_version,
             },
             {
                 "filepath": "d.jpg",
                 "failures": ["GEOLOCATION"],
-                "classifications_list": [
-                    {
-                        "classes": ["R", "S", "T"],
-                        "scores": [0.7, 0.2, 0.1],
-                    }
-                ],
                 "detections": [
                     {
-                        "category": "2",
-                        "label": "human",
-                        "conf": 0.7,
-                        "bbox": [0.1, 0.2, 0.3, 0.4],
-                    }
-                ],
-                "processed_classifications": [
-                    {
-                        "prediction": "R",
-                        "prediction_score": 0.7,
-                        "prediction_source": "mock",
+                        **human_det,
+                        "raw_classifications": {
+                            "classes": ["R", "S"],
+                            "scores": [0.7, 0.2],
+                        },
+                        "processed_classification": {
+                            "class": "R",
+                            "score": 0.7,
+                            "source": "mock:human:0.7",
+                        },
                     }
                 ],
                 "model_version": expected_model_version,
@@ -374,94 +364,148 @@ def test_combine(self, mock_ensemble2) -> None:
             {
                 "filepath": "e.jpg",
                 "country": "COUNTRY_E",
-                "classifications_list": [
-                    {
-                        "classes": ["K", "L", "M"],
-                        "scores": [0.9, 0.1, 0.0],
-                    }
-                ],
                 "detections": [
                     {
-                        "category": "2",
-                        "label": "human",
-                        "conf": 0.7,
-                        "bbox": [0.1, 0.2, 0.3, 0.4],
-                    }
-                ],
-                "processed_classifications": [
-                    {
-                        "prediction": "K",
-                        "prediction_score": 0.9,
-                        "prediction_source": "mock",
-                    }
+                        **human_det,
+                        "raw_classifications": {
+                            "classes": ["K", "L"],
+                            "scores": [0.9, 0.1],
+                            "target_classes": ["T1"],
+                            "target_logits": [1.5],
+                        },
+                        "processed_classification": {
+                            "class": "K",
+                            "score": 0.9,
+                            "source": "mock:human:0.7",
+                        },
+                    },
+                    low_conf_det,
                 ],
                 "model_version": expected_model_version,
             },
             {
                 "filepath": "f.jpg",
-                "classifications_list": [
-                    {
-                        "classes": ["XYZ"],
-                        "scores": [0.8],
-                    }
-                ],
                 "detections": [],
-                "processed_classifications": [
+                "model_version": expected_model_version,
+            },
+            {
+                "filepath": "g.jpg",
+                "country": "COUNTRY_G",
+                "detections": [
                     {
-                        "prediction": "XYZ",
-                        "prediction_score": 0.4,
-                        "prediction_source": "partial",
-                    }
+                        **whole_image_det,
+                        "raw_classifications": {
+                            "classes": ["W", "V"],
+                            "scores": [0.6, 0.3],
+                        },
+                        # Ensembled with the top detection from the detector.
+                        "processed_classification": {
+                            "class": "W",
+                            "score": 0.6,
+                            "source": "mock:human:0.7",
+                        },
+                    },
+                    human_det,
+                    animal_det,
                 ],
                 "model_version": expected_model_version,
             },
         ]
 
-    def test_multi_combine(self, mock_ensemble2) -> None:
-        classifications_list = [
-            {
-                "classes": ["R", "S", "T"],
-                "scores": [0.7, 0.2, 0.1],
+    def test_combine_max_classifications_per_detection(self, mock_ensemble2) -> None:
+        classifier_results = {
+            "a.jpg": {
+                "filepath": "a.jpg",
+                "detections": [
+                    {
+                        "category": "1",
+                        "label": "animal",
+                        "conf": 0.9,
+                        "bbox": [0.1, 0.2, 0.3, 0.4],
+                        "raw_classifications": {
+                            "classes": ["A", "B", "C"],
+                            "scores": [0.6, 0.3, 0.1],
+                        },
+                    }
+                ],
             }
-        ]
-        detections = [
-            {
-                "category": "2",
-                "label": "human",
-                "conf": 0.7,
-                "bbox": [0.1, 0.2, 0.3, 0.4],
+        }
+        detector_results = {
+            "a.jpg": {
+                "filepath": "a.jpg",
+                "detections": [
+                    {
+                        "category": "1",
+                        "label": "animal",
+                        "conf": 0.9,
+                        "bbox": [0.1, 0.2, 0.3, 0.4],
+                    }
+                ],
             }
-        ]
+        }
+        geolocation_results = {"a.jpg": {"country": "USA"}}
 
-        result = mock_ensemble2.combine(
-            filepaths=["d.jpg"],
-            classifier_results={
-                "d.jpg": {"classifications_list": classifications_list}
-            },
-            detector_results={"d.jpg": {"detections": detections}},
-            geolocation_results={"d.jpg": {"country": "COUNTRY_D"}},
-        )[0]["processed_classifications"]
+        for max_classifications, expected_classes in [
+            (1, ["A"]),
+            (2, ["A", "B"]),
+            (None, ["A", "B", "C"]),
+        ]:
+            result = mock_ensemble2.combine(
+                filepaths=["a.jpg"],
+                classifier_results=classifier_results,
+                detector_results=detector_results,
+                geolocation_results=geolocation_results,
+                max_classifications_per_detection=max_classifications,
+            )[0]
+            raw = result["detections"][0]["raw_classifications"]
+            assert raw["classes"] == expected_classes
+            assert len(raw["scores"]) == len(expected_classes)
+
+        # Inputs must not be modified.
+        assert classifier_results["a.jpg"]["detections"][0]["raw_classifications"][
+            "classes"
+        ] == ["A", "B", "C"]
+
+    def test_combine_mismatched_detections(self, mock_ensemble2) -> None:
+        classifier_results = {
+            "a.jpg": {
+                "filepath": "a.jpg",
+                "detections": [
+                    {
+                        "category": "1",
+                        "label": "animal",
+                        "conf": 0.9,
+                        "bbox": [0.1, 0.2, 0.3, 0.4],
+                        "raw_classifications": {"classes": ["A"], "scores": [0.6]},
+                    }
+                ],
+            }
+        }
+        detector_results = {
+            "a.jpg": {
+                "filepath": "a.jpg",
+                "detections": [
+                    {
+                        "category": "1",
+                        "label": "animal",
+                        "conf": 0.9,
+                        "bbox": [0.5, 0.6, 0.1, 0.1],
+                    }
+                ],
+            }
+        }
+        with pytest.raises(ValueError):
+            mock_ensemble2.combine(
+                filepaths=["a.jpg"],
+                classifier_results=classifier_results,
+                detector_results=detector_results,
+                geolocation_results={"a.jpg": {"country": "USA"}},
+            )
 
-        assert len(result) == 1
-        assert result[0]["prediction"] == "R"
-        assert result[0]["prediction_score"] == 0.7
-        assert result[0]["prediction_source"] == "mock"
+    def test_combine_multiple_crops(self, ensemble) -> None:
+        """Test ensembling multiple classified detections using the real combiner."""
 
-    def test_multi_combine_multiple_crops(self, ensemble) -> None:
-        """Test ensembling multiple classifications and detections using real
-        combiner.
-        """
         filepath = "test_data/human_and_dog.jpg"
-        classifications_list = [
-            {
-                "classes": [HUMAN, Classification.BLANK],
-                "scores": [0.99, 0.01],
-            },
-            {
-                "classes": [DOMESTIC_DOG, Classification.BLANK],
-                "scores": [0.95, 0.05],
-            },
-        ]
         detections = [
             {
                 "category": "2",
@@ -475,23 +519,69 @@ def test_multi_combine_multiple_crops(self, ensemble) -> None:
                 "conf": 0.90,
                 "bbox": [0.5229, 0.5646, 0.1307, 0.0965],
             },
+            {
+                "category": "1",
+                "label": "animal",
+                "conf": 0.12,
+                "bbox": [0.1, 0.1, 0.05, 0.05],
+            },
+            {
+                "category": "1",
+                "label": "animal",
+                "conf": 0.02,
+                "bbox": [0.2, 0.2, 0.05, 0.05],
+            },
+        ]
+        raw_classifications = [
+            {
+                "classes": [HUMAN, Classification.BLANK, DOMESTIC_DOG],
+                "scores": [0.99, 0.005, 0.005],
+            },
+            {
+                "classes": [DOMESTIC_DOG, Classification.BLANK, HUMAN],
+                "scores": [0.95, 0.04, 0.01],
+            },
+            {
+                "classes": [Classification.BLANK, DOMESTIC_DOG, HUMAN],
+                "scores": [0.999, 0.0005, 0.0005],
+            },
         ]
+        classified_detections = [
+            {**det, "raw_classifications": raw}
+            for det, raw in zip(detections, raw_classifications)
+        ] + detections[len(raw_classifications) :]
+
         res = ensemble.combine(
             filepaths=[filepath],
             classifier_results={
-                filepath: {"classifications_list": classifications_list}
+                filepath: {"filepath": filepath, "detections": classified_detections}
             },
             detector_results={
                 filepath: {"filepath": filepath, "detections": detections}
             },
             geolocation_results={filepath: {"country": "USA"}},
         )[0]
-        preds = res["processed_classifications"]
-        assert len(preds) == 2
-        assert preds[0]["prediction"] == DOMESTIC_DOG
-        assert preds[0]["prediction_source"] == "classifier"
-        assert preds[1]["prediction"] == HUMAN
-        assert preds[1]["prediction_source"] == "detector"
+        output_detections = res["detections"]
+
+        # Detections keep their original order, each with its own classifications,
+        # even though the blank detection gets the highest processed score.
+        assert [det["bbox"] for det in output_detections] == [
+            det["bbox"] for det in detections
+        ]
+        processed = [det.get("processed_classification") for det in output_detections]
+        assert processed[0]["class"] == HUMAN
+        assert processed[0]["source"] == "detector"
+        assert processed[1]["class"] == DOMESTIC_DOG
+        assert processed[1]["source"] == "classifier"
+        assert processed[2]["class"] == Classification.BLANK
+        assert processed[3] is None
+        assert "raw_classifications" not in output_detections[3]
+
+        # Raw classifications are limited to the default top-2 classes.
+        assert output_detections[0]["raw_classifications"]["classes"] == [
+            HUMAN,
+            Classification.BLANK,
+        ]
 
     def test_complete_taxonomy(self, ensemble) -> None:
 
diff --git a/speciesnet/multiprocessing.py b/speciesnet/multiprocessing.py
index a28d744..0e5dad9 100644
--- a/speciesnet/multiprocessing.py
+++ b/speciesnet/multiprocessing.py
@@ -37,24 +37,54 @@
 from tqdm import tqdm
 
 from speciesnet.classifier import SpeciesNetClassifier
+from speciesnet.constants import DEFAULT_DETECTION_THRESHOLD_FOR_CLASSIFICATION
+from speciesnet.constants import DEFAULT_MAX_CLASSIFICATIONS_PER_DETECTION
 from speciesnet.constants import Failure
+from speciesnet.constants import NUM_CLASSIFICATIONS_FOR_ENSEMBLE
 from speciesnet.detector import SpeciesNetDetector
 from speciesnet.ensemble import SpeciesNetEnsemble
 from speciesnet.ensemble_prediction_combiner import combine_predictions
 from speciesnet.geolocation import find_admin1_region
 from speciesnet.utils import BBox
+from speciesnet.utils import build_classifier_result
 from speciesnet.utils import load_partial_predictions
 from speciesnet.utils import load_rgb_image
+from speciesnet.utils import make_predictions_dict
 from speciesnet.utils import ModelInfo
 from speciesnet.utils import prepare_instances_dict
 from speciesnet.utils import PreprocessedImage
 from speciesnet.utils import save_predictions
+from speciesnet.utils import select_detections_for_classification
 
 # Handy type aliases.
 StrPath = Union[str, Path]
 DetectorInput = tuple[str, Optional[PreprocessedImage]]
-BBoxOutput = tuple[str, list[BBox]]
-ClassifierInput = tuple[str, list[Optional[PreprocessedImage]]]
+DetectionsOutput = tuple[str, Optional[list[dict]]]
+ClassifierInput = tuple[
+    str, Optional[list[PreprocessedImage]], Optional[list[dict]], list[int]
+]
+
+
+def _num_classes_to_compute(
+    max_classifications_per_detection: Optional[int],
+) -> Optional[int]:
+    """Returns how many top classes the classifier should report for each crop.
+
+    The ensemble always needs the top `NUM_CLASSIFICATIONS_FOR_ENSEMBLE` classes, even
+    if fewer classes are reported in the output.
+
+    Args:
+        max_classifications_per_detection:
+            Number of top classes to report for each detection, or `None` for all.
+
+    Returns:
+        Number of top classes to report, or `None` for all.
+    """
+
+    if max_classifications_per_detection is None:
+        return None
+    return max(max_classifications_per_detection, NUM_CLASSIFICATIONS_FOR_ENSEMBLE)
+
 
 # Register SpeciesNet model components with the SyncManager to be able to safely share
 # them between processes.
@@ -231,13 +261,14 @@ def _run_detector(
     detector: SpeciesNetDetector,
     input_queue: queue.Queue[DetectorInput],  # input
     results_dict: dict,  # output
-    bboxes_queue: Optional[queue.Queue[BBoxOutput]] = None,  # output
+    detections_queue: Optional[queue.Queue[DetectionsOutput]] = None,  # output
 ) -> None:
     """Runs detector inference.
 
     Takes a preprocessed image from the input queue and runs the detector model on it.
-    The raw output of the detector is stored in `results_dict` and the list of bounding
-    boxes is additionally stored in `bboxes_queue`.
+    The raw output of the detector is stored in `results_dict`, and the list of
+    detections (or `None` if the detector failed) is stored in
+    `detections_queue`.
 
     Args:
         detector:
@@ -246,59 +277,85 @@ def _run_detector(
             Input queue of preprocessed images.
         results_dict:
             Output dict for inference results.
-        bboxes_queue:
-            Output queue for bounding boxes identified during inference. Optional.
+        detections_queue:
+            Output queue for detections identified during inference. Optional.
     """
 
     filepath, img = input_queue.get()
     prediction = detector.predict(filepath, img)
     results_dict[filepath] = prediction
-    if bboxes_queue:
-        detections = prediction.get("detections", [])
-        bboxes_queue.put((filepath, [BBox(*det["bbox"]) for det in detections]))
+    if detections_queue:
+        detections_queue.put((filepath, prediction.get("detections")))
 
 
-def _prepare_classifier_input(
+def _prepare_classifier_input(  # pylint: disable=too-many-positional-arguments
     classifier: SpeciesNetClassifier,
-    bboxes_queue: queue.Queue[BBoxOutput],  # input
+    detections_queue: queue.Queue[DetectionsOutput],  # input
     classifier_queue: queue.Queue[ClassifierInput],  # output
+    whole_image: bool,
+    detection_threshold_for_classification: float,
+    max_classifications_per_image: Optional[int],
 ) -> None:
     """Prepares the input for classifier inference.
 
-    Takes bounding box information from `bboxes_queue` and uses it to load and
-    preprocess the image for classifier inference. It first loads the image,
-    preprocesses it by potentially cropping based on bboxes and finally outputs the
-    image into `classifier_queue` to be used by the classifier model.
+    Takes detections from `detections_queue`, selects which ones to classify, and uses
+    them to load and preprocess the image for classifier inference. It first loads the
+    image, preprocesses it by cropping the selected detections (or by preparing the
+    whole image for whole-image classifiers) and finally outputs the crops, along with
+    the detections and the indices of the selected detections, into `classifier_queue`
+    to be used by the classifier model.
 
     Args:
         classifier:
             SpeciesNetClassifier to use.
-        bboxes_queue:
-            Input queue of bounding boxes from detector inference.
+        detections_queue:
+            Input queue of detections from detector inference.
         classifier_queue:
             Output queue for preprocessed images for classifier inference.
+        whole_image:
+            Whether the classifier is a whole-image classifier.
+        detection_threshold_for_classification:
+            Minimum confidence score for a detection to be classified.
+        max_classifications_per_image:
+            Maximum number of detections to classify per image, or `None` for no limit.
     """
 
-    filepath, bboxes = bboxes_queue.get()
-    img = load_rgb_image(filepath)
+    filepath, detections = detections_queue.get()
+    if whole_image:
+        indices = []
+    else:
+        indices = select_detections_for_classification(
+            detections or [],
+            detection_threshold_for_classification,
+            max_classifications_per_image,
+        )
+        if not indices:
+            # Nothing to classify, so there's no need to load the image.
+            classifier_queue.put((filepath, [], detections, indices))
+            return
     try:
+        img = load_rgb_image(filepath)
+        bboxes = [BBox(*detections[idx]["bbox"]) for idx in indices]  # type: ignore
         imgs = classifier.preprocess(img, bboxes=bboxes)
-        classifier_queue.put((filepath, imgs))
+        classifier_queue.put((filepath, imgs, detections, indices))
     except:
-        classifier_queue.put((filepath, []))
+        classifier_queue.put((filepath, None, detections, indices))
         raise
 
 
-def _run_classifier(
+def _run_classifier(  # pylint: disable=too-many-positional-arguments
     classifier: SpeciesNetClassifier,
     input_queue: queue.Queue[ClassifierInput],  # input
     results_dict: dict,  # output
     batch_size: int,
+    whole_image: bool,
+    num_classes: Optional[int],
 ) -> None:
     """Runs classifier inference.
 
     Takes a batch of preprocessed images from the input queue and runs the classifier
-    model on it. The output of the classifier is stored in `results_dict`.
+    model on it. The output of the classifier is attached to the corresponding
+    detections and stored in `results_dict`.
 
     Args:
         classifier:
@@ -309,15 +366,22 @@ def _run_classifier(
             Output dict for inference results.
         batch_size:
             Batch size for inference.
-
+        whole_image:
+            Whether the classifier is a whole-image classifier.
+        num_classes:
+            Number of top classes to report for each crop, or `None` for all.
     """
 
     input_tuples = [input_queue.get() for _ in range(batch_size)]
     filepaths = [t[0] for t in input_tuples]
     imgs = [t[1] for t in input_tuples]
-    predictions = classifier.batch_predict(filepaths, imgs)
-    for filepath, prediction in zip(filepaths, predictions):
-        results_dict[filepath] = prediction
+    predictions = classifier.batch_predict(filepaths, imgs, num_classes=num_classes)
+    for (filepath, _, detections, indices), prediction in zip(
+        input_tuples, predictions
+    ):
+        results_dict[filepath] = build_classifier_result(
+            prediction, detections, indices, whole_image
+        )
 
 
 def _find_admin1_region(  # pylint: disable=too-many-positional-arguments
@@ -367,7 +431,9 @@ def _combine_results(  # pylint: disable=too-many-positional-arguments
     partial_predictions: dict[str, dict],
     predictions_json: Optional[StrPath] = None,
     save_lock: Optional[threading.Lock] = None,
-    max_classifications_per_image: Optional[int] = None,
+    max_classifications_per_detection: Optional[
+        int
+    ] = DEFAULT_MAX_CLASSIFICATIONS_PER_DETECTION,
 ) -> Optional[dict]:
     """Combines inference results from multiple jobs that ran independently.
 
@@ -400,6 +466,9 @@ def _combine_results(  # pylint: disable=too-many-positional-arguments
             Threading lock used to avoid race conditions when saving predictions to a
             file. Only needed when `predictions_json` is not `None`, otherwise it is
             ignored.
+        max_classifications_per_detection:
+            Number of top classes to report in the raw classifications of each
+            detection, or `None` for all.
 
     Returns:
         The predictions dict of ensembled inference results if `predictions_json` is set
@@ -412,9 +481,9 @@ def _combine_results(  # pylint: disable=too-many-positional-arguments
         detector_results=detector_results,
         geolocation_results=geolocation_results,
         partial_predictions=partial_predictions,
-        max_classifications=max_classifications_per_image,
+        max_classifications_per_detection=max_classifications_per_detection,
     )
-    predictions_dict = {"predictions": ensemble_results}
+    predictions_dict = make_predictions_dict(ensemble_results)
     if predictions_json:
         if save_lock:
             with save_lock:
@@ -475,7 +544,7 @@ def _merge_results(  # pylint: disable=too-many-positional-arguments
                 }
             )
 
-    predictions_dict = {"predictions": results}
+    predictions_dict = make_predictions_dict(results)
     if predictions_json:
         if save_lock:
             with save_lock:
@@ -602,6 +671,9 @@ def __init__(
             logging.info("Forcing a download of model %s.", model_name)
             _ = ModelInfo(model_name, force_model_download=True)
 
+        # Whether the classifier classifies whole images rather than detection crops.
+        self.whole_image_classifier = ModelInfo(model_name).type_ == "full_image"
+
         if multiprocessing:
             self.manager = SyncManager()
             self.manager.start()  # pylint: disable=consider-using-with
@@ -640,12 +712,18 @@ def __del__(self) -> None:
             if hasattr(self.manager, "shutdown"):
                 self.manager.shutdown()
 
-    def _predict_using_single_thread(
+    def _predict_using_single_thread(  # pylint: disable=too-many-positional-arguments
         self,
         instances_dict: dict,
         progress_bars: bool = False,
         predictions_json: Optional[StrPath] = None,
+        detection_threshold_for_classification: float = (
+            DEFAULT_DETECTION_THRESHOLD_FOR_CLASSIFICATION
+        ),
         max_classifications_per_image: Optional[int] = None,
+        max_classifications_per_detection: Optional[
+            int
+        ] = DEFAULT_MAX_CLASSIFICATIONS_PER_DETECTION,
     ) -> Optional[dict]:
         """Runs prediction using a single thread, processing each image one by one.
 
@@ -660,6 +738,14 @@ def _predict_using_single_thread(
                 Whether to show progress bars.
             predictions_json:
                 Path where to save the JSON output.
+            detection_threshold_for_classification:
+                Minimum confidence score for a detection to be classified.
+            max_classifications_per_image:
+                Maximum number of detections to classify per image, or `None` for no
+                limit.
+            max_classifications_per_detection:
+                Number of top classes to report for each classified detection, or `None`
+                for all.
 
         Returns:
              The predictions dict of ensembled inference results if `predictions_json`
@@ -690,6 +776,7 @@ def _predict_using_single_thread(
                 geolocation_results=geolocation_results,
                 partial_predictions=partial_predictions,
                 predictions_json=predictions_json,
+                max_classifications_per_detection=max_classifications_per_detection,
             )
         else:
             periodic_saver = None
@@ -711,6 +798,7 @@ def _predict_using_single_thread(
             total=num_instances_to_process,
             rlock=threading.RLock(),
         )
+        num_classes = _num_classes_to_compute(max_classifications_per_detection)
 
         # Process instances one by one.
         for instance in instances_to_process:
@@ -731,18 +819,28 @@ def _predict_using_single_thread(
             detector_results[filepath] = self.detector.predict(filepath, detector_input)
             progress.update("detector_predict")
 
-            # Preprocess image for classifier.
+            # Preprocess image for classifier, cropping the selected detections.
             detections = detector_results[filepath].get("detections", None)
-            if detections:
-                bboxes = [BBox(*det["bbox"]) for det in detections]
+            if self.whole_image_classifier:
+                indices = []
             else:
-                bboxes = []
+                indices = select_detections_for_classification(
+                    detections or [],
+                    detection_threshold_for_classification,
+                    max_classifications_per_image,
+                )
+            bboxes = [BBox(*detections[idx]["bbox"]) for idx in indices]  # type: ignore
             classifier_input = self.classifier.preprocess(img, bboxes=bboxes)
             progress.update("classifier_preprocess")
 
             # Run classifier.
-            classifier_results[filepath] = self.classifier.predict(
-                filepath, classifier_input
+            classifier_results[filepath] = build_classifier_result(
+                self.classifier.predict(
+                    filepath, classifier_input, num_classes=num_classes
+                ),
+                detections,
+                indices,
+                self.whole_image_classifier,
             )
             progress.update("classifier_predict")
 
@@ -775,7 +873,7 @@ def _predict_using_single_thread(
             partial_predictions=partial_predictions,
             predictions_json=predictions_json,
             save_lock=save_lock,
-            max_classifications_per_image=max_classifications_per_image,
+            max_classifications_per_detection=max_classifications_per_detection,
         )
 
     def _predict_using_worker_pools(  # pylint: disable=too-many-positional-arguments
@@ -789,7 +887,13 @@ def _predict_using_worker_pools(  # pylint: disable=too-many-positional-argument
         new_dict_fn: Optional[Callable] = None,
         new_queue_fn: Optional[Callable] = None,
         new_rlock_fn: Optional[Callable] = None,
+        detection_threshold_for_classification: float = (
+            DEFAULT_DETECTION_THRESHOLD_FOR_CLASSIFICATION
+        ),
         max_classifications_per_image: Optional[int] = None,
+        max_classifications_per_detection: Optional[
+            int
+        ] = DEFAULT_MAX_CLASSIFICATIONS_PER_DETECTION,
     ) -> Optional[dict]:
         """Runs prediction using worker pools (multi-threading or multiprocessing).
 
@@ -815,6 +919,14 @@ def _predict_using_worker_pools(  # pylint: disable=too-many-positional-argument
                 Callable that returns a queue.
              new_rlock_fn:
                 Callable that returns a thread/process lock.
+            detection_threshold_for_classification:
+                Minimum confidence score for a detection to be classified.
+            max_classifications_per_image:
+                Maximum number of detections to classify per image, or `None` for no
+                limit.
+            max_classifications_per_detection:
+                Number of top classes to report for each classified detection, or `None`
+                for all.
 
         Returns:
             The predictions dict of ensembled inference results if `predictions_json`
@@ -858,6 +970,7 @@ def _predict_using_worker_pools(  # pylint: disable=too-many-positional-argument
                 geolocation_results=geolocation_results,
                 partial_predictions=partial_predictions,
                 predictions_json=predictions_json,
+                max_classifications_per_detection=max_classifications_per_detection,
             )
         else:
             periodic_saver = None
@@ -892,7 +1005,9 @@ def _predict_using_worker_pools(  # pylint: disable=too-many-positional-argument
         detector_queue = new_queue_fn(
             max(2 * batch_size, 64)
         )  # Limited number of images to store in memory.
-        bboxes_queue = new_queue_fn()  # Unlimited number of bboxes to store in memory.
+        detections_queue = (
+            new_queue_fn()
+        )  # Unlimited number of detections to store in memory.
         classifier_queue = new_queue_fn(
             max(2 * batch_size, 64)
         )  # Limited number of images to store in memory.
@@ -911,7 +1026,14 @@ def _predict_using_worker_pools(  # pylint: disable=too-many-positional-argument
             # Preprocess image for classifier.
             common_pool.apply_async(
                 _prepare_classifier_input,
-                args=(self.classifier, bboxes_queue, classifier_queue),
+                args=(
+                    self.classifier,
+                    detections_queue,
+                    classifier_queue,
+                    self.whole_image_classifier,
+                    detection_threshold_for_classification,
+                    max_classifications_per_image,
+                ),
                 callback=lambda _: progress.update("classifier_preprocess"),
                 error_callback=_error_callback,
             )
@@ -935,12 +1057,18 @@ def _predict_using_worker_pools(  # pylint: disable=too-many-positional-argument
         for _ in range(num_instances_to_process):
             detector_pool.apply_async(
                 _run_detector,
-                args=(self.detector, detector_queue, detector_results, bboxes_queue),
+                args=(
+                    self.detector,
+                    detector_queue,
+                    detector_results,
+                    detections_queue,
+                ),
                 callback=lambda _: progress.update("detector_predict"),
                 error_callback=_error_callback,
             )
 
         # Run classifier inference asynchronously.
+        num_classes = _num_classes_to_compute(max_classifications_per_detection)
         for batch_idx in range(num_batches):
             classifier_pool.apply_async(
                 _run_classifier,
@@ -949,6 +1077,8 @@ def _predict_using_worker_pools(  # pylint: disable=too-many-positional-argument
                     classifier_queue,
                     classifier_results,
                     (batch_size if batch_idx < num_batches - 1 else last_batch_size),
+                    self.whole_image_classifier,
+                    num_classes,
                 ),
                 callback=lambda _: progress.update("classifier_predict"),
                 error_callback=_error_callback,
@@ -979,16 +1109,22 @@ def _predict_using_worker_pools(  # pylint: disable=too-many-positional-argument
             partial_predictions=partial_predictions,
             predictions_json=predictions_json,
             save_lock=save_lock,
-            max_classifications_per_image=max_classifications_per_image,
+            max_classifications_per_detection=max_classifications_per_detection,
         )
 
-    def _predict_using_thread_pools(
+    def _predict_using_thread_pools(  # pylint: disable=too-many-positional-arguments
         self,
         instances_dict: dict,
         batch_size: int = 8,
         progress_bars: bool = False,
         predictions_json: Optional[StrPath] = None,
+        detection_threshold_for_classification: float = (
+            DEFAULT_DETECTION_THRESHOLD_FOR_CLASSIFICATION
+        ),
         max_classifications_per_image: Optional[int] = None,
+        max_classifications_per_detection: Optional[
+            int
+        ] = DEFAULT_MAX_CLASSIFICATIONS_PER_DETECTION,
     ) -> Optional[dict]:
         return self._predict_using_worker_pools(
             instances_dict,
@@ -1000,16 +1136,26 @@ def _predict_using_thread_pools(
             new_dict_fn=dict,
             new_queue_fn=queue.Queue,
             new_rlock_fn=threading.RLock,
+            detection_threshold_for_classification=(
+                detection_threshold_for_classification
+            ),
             max_classifications_per_image=max_classifications_per_image,
+            max_classifications_per_detection=max_classifications_per_detection,
         )
 
-    def _predict_using_process_pools(
+    def _predict_using_process_pools(  # pylint: disable=too-many-positional-arguments
         self,
         instances_dict: dict,
         batch_size: int = 8,
         progress_bars: bool = False,
         predictions_json: Optional[StrPath] = None,
+        detection_threshold_for_classification: float = (
+            DEFAULT_DETECTION_THRESHOLD_FOR_CLASSIFICATION
+        ),
         max_classifications_per_image: Optional[int] = None,
+        max_classifications_per_detection: Optional[
+            int
+        ] = DEFAULT_MAX_CLASSIFICATIONS_PER_DETECTION,
     ) -> Optional[dict]:
         assert self.manager is not None
         return self._predict_using_worker_pools(
@@ -1022,7 +1168,11 @@ def _predict_using_process_pools(
             new_dict_fn=self.manager.dict,
             new_queue_fn=self.manager.Queue,
             new_rlock_fn=self.manager.RLock,
+            detection_threshold_for_classification=(
+                detection_threshold_for_classification
+            ),
             max_classifications_per_image=max_classifications_per_image,
+            max_classifications_per_detection=max_classifications_per_detection,
         )
 
     def _classify_using_worker_pools(  # pylint: disable=too-many-positional-arguments
@@ -1036,6 +1186,13 @@ def _classify_using_worker_pools(  # pylint: disable=too-many-positional-argumen
         new_dict_fn: Optional[Callable] = None,
         new_queue_fn: Optional[Callable] = None,
         new_rlock_fn: Optional[Callable] = None,
+        detection_threshold_for_classification: float = (
+            DEFAULT_DETECTION_THRESHOLD_FOR_CLASSIFICATION
+        ),
+        max_classifications_per_image: Optional[int] = None,
+        max_classifications_per_detection: Optional[
+            int
+        ] = DEFAULT_MAX_CLASSIFICATIONS_PER_DETECTION,
     ) -> Optional[dict]:
         assert new_pool_fn is not None
         assert new_dict_fn is not None
@@ -1092,7 +1249,9 @@ def _classify_using_worker_pools(  # pylint: disable=too-many-positional-argumen
         classifier_pool = new_pool_fn(
             1
         )  # One single worker to run classifier inference.
-        bboxes_queue = new_queue_fn()  # Unlimited number of bboxes to store in memory.
+        detections_queue = (
+            new_queue_fn()
+        )  # Unlimited number of detections to store in memory.
         classifier_queue = new_queue_fn(
             max(2 * batch_size, 64)
         )  # Limited number of images to store in memory.
@@ -1100,16 +1259,26 @@ def _classify_using_worker_pools(  # pylint: disable=too-many-positional-argumen
         # Preprocess images for classifier.
         for instance in instances_to_process:
             filepath = instance["filepath"]
-            detections = detections_dict.get(filepath, {}).get("detections", [])
-            bboxes_queue.put((filepath, [BBox(*det["bbox"]) for det in detections]))
+            detections = detections_dict.get(filepath, {}).get("detections")
+            detections_queue.put((filepath, detections))
             common_pool.apply_async(
                 _prepare_classifier_input,
-                args=(self.classifier, bboxes_queue, classifier_queue),
+                args=(
+                    self.classifier,
+                    detections_queue,
+                    classifier_queue,
+                    self.whole_image_classifier,
+                    detection_threshold_for_classification,
+                    max_classifications_per_image,
+                ),
                 callback=lambda _: progress.update("classifier_preprocess"),
                 error_callback=_error_callback,
             )
 
-        # Run classifier.
+        # Run classifier. Classifier-only output keeps at least the top classes needed
+        # by the ensemble, so that ensembling it later gives the same results as a full
+        # run.
+        num_classes = _num_classes_to_compute(max_classifications_per_detection)
         for batch_idx in range(num_batches):
             classifier_pool.apply_async(
                 _run_classifier,
@@ -1118,6 +1287,8 @@ def _classify_using_worker_pools(  # pylint: disable=too-many-positional-argumen
                     classifier_queue,
                     classifier_results,
                     (batch_size if batch_idx < num_batches - 1 else last_batch_size),
+                    self.whole_image_classifier,
+                    num_classes,
                 ),
                 callback=lambda _: progress.update("classifier_predict"),
                 error_callback=_error_callback,
@@ -1153,6 +1324,13 @@ def _classify_using_thread_pools(  # pylint: disable=too-many-positional-argumen
         batch_size: int = 8,
         progress_bars: bool = False,
         predictions_json: Optional[StrPath] = None,
+        detection_threshold_for_classification: float = (
+            DEFAULT_DETECTION_THRESHOLD_FOR_CLASSIFICATION
+        ),
+        max_classifications_per_image: Optional[int] = None,
+        max_classifications_per_detection: Optional[
+            int
+        ] = DEFAULT_MAX_CLASSIFICATIONS_PER_DETECTION,
     ) -> Optional[dict]:
         return self._classify_using_worker_pools(
             instances_dict,
@@ -1164,6 +1342,11 @@ def _classify_using_thread_pools(  # pylint: disable=too-many-positional-argumen
             new_dict_fn=dict,
             new_queue_fn=queue.Queue,
             new_rlock_fn=threading.RLock,
+            detection_threshold_for_classification=(
+                detection_threshold_for_classification
+            ),
+            max_classifications_per_image=max_classifications_per_image,
+            max_classifications_per_detection=max_classifications_per_detection,
         )
 
     def _classify_using_process_pools(  # pylint: disable=too-many-positional-arguments
@@ -1173,6 +1356,13 @@ def _classify_using_process_pools(  # pylint: disable=too-many-positional-argume
         batch_size: int = 8,
         progress_bars: bool = False,
         predictions_json: Optional[StrPath] = None,
+        detection_threshold_for_classification: float = (
+            DEFAULT_DETECTION_THRESHOLD_FOR_CLASSIFICATION
+        ),
+        max_classifications_per_image: Optional[int] = None,
+        max_classifications_per_detection: Optional[
+            int
+        ] = DEFAULT_MAX_CLASSIFICATIONS_PER_DETECTION,
     ) -> Optional[dict]:
         assert self.manager is not None
         return self._classify_using_worker_pools(
@@ -1185,6 +1375,11 @@ def _classify_using_process_pools(  # pylint: disable=too-many-positional-argume
             new_dict_fn=self.manager.dict,
             new_queue_fn=self.manager.Queue,
             new_rlock_fn=self.manager.RLock,
+            detection_threshold_for_classification=(
+                detection_threshold_for_classification
+            ),
+            max_classifications_per_image=max_classifications_per_image,
+            max_classifications_per_detection=max_classifications_per_detection,
         )
 
     def _detect_using_worker_pools(  # pylint: disable=too-many-positional-arguments
@@ -1327,7 +1522,9 @@ def _ensemble_using_single_thread(  # pylint: disable=too-many-positional-argume
         detections_dict: Optional[dict] = None,
         progress_bars: bool = False,
         predictions_json: Optional[StrPath] = None,
-        max_classifications_per_image: Optional[int] = None,
+        max_classifications_per_detection: Optional[
+            int
+        ] = DEFAULT_MAX_CLASSIFICATIONS_PER_DETECTION,
     ) -> Optional[dict]:
         instances = instances_dict["instances"]
         filepaths = [instance["filepath"] for instance in instances]
@@ -1353,6 +1550,7 @@ def _ensemble_using_single_thread(  # pylint: disable=too-many-positional-argume
                 geolocation_results=geolocation_results,
                 partial_predictions=partial_predictions,
                 predictions_json=predictions_json,
+                max_classifications_per_detection=max_classifications_per_detection,
             )
         else:
             periodic_saver = None
@@ -1402,7 +1600,7 @@ def _ensemble_using_single_thread(  # pylint: disable=too-many-positional-argume
             partial_predictions=partial_predictions,
             predictions_json=predictions_json,
             save_lock=save_lock,
-            max_classifications_per_image=max_classifications_per_image,
+            max_classifications_per_detection=max_classifications_per_detection,
         )
 
     def predict(
@@ -1422,7 +1620,13 @@ def predict(
         batch_size: int = 8,
         progress_bars: bool = False,
         predictions_json: Optional[StrPath] = None,
+        detection_threshold_for_classification: float = (
+            DEFAULT_DETECTION_THRESHOLD_FOR_CLASSIFICATION
+        ),
         max_classifications_per_image: Optional[int] = None,
+        max_classifications_per_detection: Optional[
+            int
+        ] = DEFAULT_MAX_CLASSIFICATIONS_PER_DETECTION,
     ) -> Optional[dict]:
         instances_dict = prepare_instances_dict(
             instances_dict,
@@ -1434,12 +1638,19 @@ def predict(
             country,
             admin1_region,
         )
+        classification_kwargs = {
+            "detection_threshold_for_classification": (
+                detection_threshold_for_classification
+            ),
+            "max_classifications_per_image": max_classifications_per_image,
+            "max_classifications_per_detection": max_classifications_per_detection,
+        }
         if run_mode == "single_thread":
             return self._predict_using_single_thread(
                 instances_dict,
                 progress_bars=progress_bars,
                 predictions_json=predictions_json,
-                max_classifications_per_image=max_classifications_per_image,
+                **classification_kwargs,
             )
         elif run_mode == "multi_thread":
             return self._predict_using_thread_pools(
@@ -1447,7 +1658,7 @@ def predict(
                 batch_size=batch_size,
                 progress_bars=progress_bars,
                 predictions_json=predictions_json,
-                max_classifications_per_image=max_classifications_per_image,
+                **classification_kwargs,
             )
         elif run_mode == "multi_process":
             return self._predict_using_process_pools(
@@ -1455,7 +1666,7 @@ def predict(
                 batch_size=batch_size,
                 progress_bars=progress_bars,
                 predictions_json=predictions_json,
-                max_classifications_per_image=max_classifications_per_image,
+                **classification_kwargs,
             )
         else:
             raise ValueError(f"Unknown run mode: `{run_mode}`")
@@ -1476,6 +1687,13 @@ def classify(
         batch_size: int = 8,
         progress_bars: bool = False,
         predictions_json: Optional[StrPath] = None,
+        detection_threshold_for_classification: float = (
+            DEFAULT_DETECTION_THRESHOLD_FOR_CLASSIFICATION
+        ),
+        max_classifications_per_image: Optional[int] = None,
+        max_classifications_per_detection: Optional[
+            int
+        ] = DEFAULT_MAX_CLASSIFICATIONS_PER_DETECTION,
     ) -> Optional[dict]:
         instances_dict = prepare_instances_dict(
             instances_dict,
@@ -1487,6 +1705,13 @@ def classify(
             country,
             admin1_region,
         )
+        classification_kwargs = {
+            "detection_threshold_for_classification": (
+                detection_threshold_for_classification
+            ),
+            "max_classifications_per_image": max_classifications_per_image,
+            "max_classifications_per_detection": max_classifications_per_detection,
+        }
         if run_mode == "multi_thread":
             return self._classify_using_thread_pools(
                 instances_dict,
@@ -1494,6 +1719,7 @@ def classify(
                 batch_size=batch_size,
                 progress_bars=progress_bars,
                 predictions_json=predictions_json,
+                **classification_kwargs,
             )
         elif run_mode == "multi_process":
             return self._classify_using_process_pools(
@@ -1502,6 +1728,7 @@ def classify(
                 batch_size=batch_size,
                 progress_bars=progress_bars,
                 predictions_json=predictions_json,
+                **classification_kwargs,
             )
         else:
             raise ValueError(f"Unknown run mode: `{run_mode}`")
@@ -1561,7 +1788,9 @@ def ensemble_from_past_runs(
         detections_dict: Optional[dict] = None,
         progress_bars: bool = False,
         predictions_json: Optional[StrPath] = None,
-        max_classifications_per_image: Optional[int] = None,
+        max_classifications_per_detection: Optional[
+            int
+        ] = DEFAULT_MAX_CLASSIFICATIONS_PER_DETECTION,
     ) -> Optional[dict]:
         instances_dict = prepare_instances_dict(
             instances_dict,
@@ -1579,5 +1808,5 @@ def ensemble_from_past_runs(
             detections_dict=detections_dict,
             progress_bars=progress_bars,
             predictions_json=predictions_json,
-            max_classifications_per_image=max_classifications_per_image,
+            max_classifications_per_detection=max_classifications_per_detection,
         )
diff --git a/speciesnet/multiprocessing_test.py b/speciesnet/multiprocessing_test.py
index 3bcd7ce..96f332b 100644
--- a/speciesnet/multiprocessing_test.py
+++ b/speciesnet/multiprocessing_test.py
@@ -20,8 +20,20 @@
 
 import pytest
 
+from speciesnet.constants import FORMAT_VERSION
 from speciesnet.multiprocessing import SpeciesNet
 
+# fmt: off
+# pylint: disable=line-too-long
+
+HUMAN = "990ae9dd-7a59-4344-afcb-1b7b21368000;mammalia;primates;hominidae;homo;sapiens;human"
+DOMESTIC_DOG = "3d80f1d6-b1df-4966-9ff4-94053c7a902a;mammalia;carnivora;canidae;canis;familiaris;domestic dog"
+PLAINS_ZEBRA = "dd39bbd5-077c-482e-9d33-bd176116c870;mammalia;perissodactyla;equidae;equus;quagga;plains zebra"
+THOMSONS_GAZELLE = "dc5dbe17-a8ca-40a6-ac6a-3b6b1d63e6d6;mammalia;artiodactyla;bovidae;eudorcas;thomsonii;thomson's gazelle"
+
+# pylint: enable=line-too-long
+# fmt: on
+
 
 def assert_approx_objs(
     obj1: Any,
@@ -164,3 +176,175 @@ def test_detect(self, request, instances_dict, model) -> None:
         assert predictions_dict2
         assert_approx_objs(predictions_dict1, predictions_dict2, atol=1e-3)
         logging.info("Detections (%s): %s", request.node.name, predictions_dict1)
+
+
+def _predictions_by_filepath(predictions_dict: dict) -> dict[str, dict]:
+    return {p["filepath"]: p for p in predictions_dict["predictions"]}
+
+
+def _processed_classes(prediction: dict) -> list[Optional[str]]:
+    return [
+        (
+            det["processed_classification"]["class"]
+            if "processed_classification" in det
+            else None
+        )
+        for det in prediction.get("detections", [])
+    ]
+
+
+class TestEndToEnd:
+    """End-to-end tests checking the content of predictions."""
+
+    @pytest.fixture(scope="class")
+    def model(self, model_name: str) -> SpeciesNet:
+        return SpeciesNet(model_name)
+
+    @pytest.fixture(scope="class")
+    def predictions(self, model) -> dict[str, dict]:
+        with open("test_data/instances_with_errors.json", encoding="utf-8") as fp:
+            instances_dict = json.load(fp)
+        predictions_dict = model.predict(instances_dict=instances_dict)
+        assert predictions_dict
+        assert predictions_dict["info"] == {"format_version": FORMAT_VERSION}
+        return _predictions_by_filepath(predictions_dict)
+
+    def test_output_format(self, model, predictions) -> None:
+        for prediction in predictions.values():
+            assert not {
+                "classifications",
+                "classifications_list",
+                "prediction",
+                "prediction_score",
+                "prediction_source",
+                "processed_classifications",
+            } & set(prediction)
+            detections = prediction.get("detections", [])
+
+            if model.whole_image_classifier and detections:
+                # The whole-image pseudo-detection comes first, and is the only
+                # classified detection.
+                assert detections[0]["category"] == "0"
+                assert detections[0]["label"] == "whole_image_classification"
+                assert detections[0]["conf"] == 1.0
+                assert detections[0]["bbox"] == [0.0, 0.0, 1.0, 1.0]
+                assert all(det["category"] != "0" for det in detections[1:])
+                classified = [0]
+            else:
+                classified = [
+                    idx for idx, det in enumerate(detections) if det["conf"] >= 0.1
+                ]
+
+            # Detections are sorted by decreasing confidence.
+            confs = [det["conf"] for det in detections]
+            assert confs == sorted(confs, reverse=True)
+
+            if "failures" in prediction:
+                continue
+            for idx, det in enumerate(detections):
+                if idx in classified:
+                    raw = det["raw_classifications"]
+                    assert len(raw["classes"]) == 2
+                    assert len(raw["scores"]) == 2
+                    assert raw["scores"] == sorted(raw["scores"], reverse=True)
+                    assert set(det["processed_classification"]) == {
+                        "class",
+                        "score",
+                        "source",
+                    }
+                else:
+                    assert "raw_classifications" not in det
+                    assert "processed_classification" not in det
+
+    def test_multi_object_images(self, model, predictions) -> None:
+        if model.whole_image_classifier:
+            pytest.skip("Whole-image models do not classify individual detections.")
+
+        prediction = predictions["test_data/human_and_dog.jpg"]
+        assert _processed_classes(prediction) == [HUMAN, DOMESTIC_DOG]
+        assert [det["label"] for det in prediction["detections"]] == [
+            "human",
+            "animal",
+        ]
+
+        prediction = predictions["test_data/gazelles_and_zebra.jpg"]
+        assert _processed_classes(prediction) == [
+            THOMSONS_GAZELLE,
+            THOMSONS_GAZELLE,
+            PLAINS_ZEBRA,
+            None,  # Below the detection threshold for classification.
+            None,  # Below the detection threshold for classification.
+        ]
+
+    def test_no_classifiable_detections(self, model, predictions) -> None:
+        if model.whole_image_classifier:
+            pytest.skip("Whole-image models always classify the whole image.")
+
+        # No detection at or above the threshold, so nothing gets classified.
+        prediction = predictions["test_data/blank3.jpg"]
+        assert prediction["detections"]
+        assert _processed_classes(prediction) == [None] * len(prediction["detections"])
+
+    def test_classification_options(self, model) -> None:
+        filepath = "test_data/gazelles_and_zebra.jpg"
+        instances_dict = {"instances": [{"filepath": filepath, "country": "TZA"}]}
+
+        def _predict(**kwargs) -> dict:
+            predictions_dict = model.predict(instances_dict=instances_dict, **kwargs)
+            assert predictions_dict
+            return predictions_dict["predictions"][0]
+
+        def _num_classified(prediction: dict) -> int:
+            return sum("raw_classifications" in det for det in prediction["detections"])
+
+        default = _predict()
+        num_detections = len(default["detections"])
+        if model.whole_image_classifier:
+            assert _num_classified(default) == 1
+            assert _num_classified(_predict(max_classifications_per_image=0)) == 1
+        else:
+            assert _num_classified(default) == 3
+            assert (
+                _num_classified(_predict(detection_threshold_for_classification=0.0))
+                == num_detections
+            )
+            capped = _predict(max_classifications_per_image=1)
+            assert _num_classified(capped) == 1
+            assert "raw_classifications" in capped["detections"][0]
+
+        # The number of reported classes doesn't affect processed classifications.
+        for max_classifications in [1, 5, 10, None]:
+            prediction = _predict(max_classifications_per_detection=max_classifications)
+            det = prediction["detections"][0]
+            if max_classifications is None:
+                assert len(det["raw_classifications"]["classes"]) > 10
+            else:
+                assert len(det["raw_classifications"]["classes"]) == (
+                    max_classifications
+                )
+            assert _processed_classes(prediction) == _processed_classes(default)
+
+    def test_classify_then_ensemble(self, model) -> None:
+        with open("test_data/instances.json", encoding="utf-8") as fp:
+            instances_dict = json.load(fp)
+
+        detections_dict = model.detect(instances_dict=instances_dict)
+        classifications_dict = model.classify(
+            instances_dict=instances_dict,
+            detections_dict=_predictions_by_filepath(detections_dict),
+        )
+        assert detections_dict and classifications_dict
+
+        # Classifier-only output keeps enough classes to run the ensemble later.
+        for prediction in classifications_dict["predictions"]:
+            for det in prediction.get("detections", []):
+                if "raw_classifications" in det:
+                    assert len(det["raw_classifications"]["classes"]) == 5
+
+        ensemble_dict = model.ensemble_from_past_runs(
+            instances_dict=instances_dict,
+            classifications_dict=_predictions_by_filepath(classifications_dict),
+            detections_dict=_predictions_by_filepath(detections_dict),
+        )
+        predictions_dict = model.predict(instances_dict=instances_dict)
+        assert_approx_objs(ensemble_dict, predictions_dict, atol=1e-3)
diff --git a/speciesnet/scripts/run_model.py b/speciesnet/scripts/run_model.py
index 1b082d9..694967d 100644
--- a/speciesnet/scripts/run_model.py
+++ b/speciesnet/scripts/run_model.py
@@ -30,7 +30,8 @@
 from speciesnet import DEFAULT_MODEL
 from speciesnet import only_one_true
 from speciesnet import SpeciesNet
-from speciesnet.ensemble_prediction_combiner import PredictionType
+from speciesnet.constants import DEFAULT_DETECTION_THRESHOLD_FOR_CLASSIFICATION
+from speciesnet.constants import DEFAULT_MAX_CLASSIFICATIONS_PER_DETECTION
 from speciesnet.utils import load_partial_predictions
 from speciesnet.utils import prepare_instances_dict
 
@@ -156,28 +157,50 @@
     "--noforce_model_download (default) does not. Has no effect when --model refers "
     "to a local folder.",
 )
+_DETECTION_THRESHOLD_FOR_CLASSIFICATION = flags.DEFINE_float(
+    "detection_threshold_for_classification",
+    DEFAULT_DETECTION_THRESHOLD_FOR_CLASSIFICATION,
+    "Minimum detection confidence for a detection to be run through the classifier. "
+    "Detections below this threshold are still included in the output, without "
+    "classifications. Has no effect for whole-image classifiers.",
+)
 _MAX_CLASSIFICATIONS_PER_IMAGE = flags.DEFINE_integer(
     "max_classifications_per_image",
     None,
-    "Maximum number of classifications to return per image. "
-    "If not specified, returns all possible classifications.",
+    "Maximum number of detections to classify per image (the highest-confidence "
+    "detections above --detection_threshold_for_classification are classified). "
+    "If not specified (or < 0), all detections above the threshold are classified. Has "
+    "no effect for whole-image classifiers.",
+)
+_MAX_CLASSIFICATIONS_PER_DETECTION = flags.DEFINE_integer(
+    "max_classifications_per_detection",
+    DEFAULT_MAX_CLASSIFICATIONS_PER_DETECTION,
+    "Number of top classes to report in the raw classifications of each classified "
+    "detection. If < 0, all classes are reported. Processed classifications are always "
+    "computed from the top 5 classes, regardless of this value. In classifier-only "
+    "mode, at least the top 5 classes are reported, so that the ensemble can be run "
+    "later.",
 )
 
 
 def guess_predictions_source(
     predictions: dict[str, dict],
-) -> Literal["classifier", "detector", "ensemble", "unknown", "invalid"]:
+) -> Literal["classifier", "detector", "ensemble", "unknown"]:
     """Guesses which model component generated given predictions.
 
+    Ensemble results are recognized by their model version (which only the ensemble
+    reports) or by processed classifications, classifier results by raw
+    classifications, and detector results by detections without any classifications.
+    Classifier results in which no detection was classified are indistinguishable from
+    detector results.
+
     Args:
         predictions: Dict of predictions, keyed by filepaths.
 
     Returns:
         Returns "classifier", "detector" or "ensemble" when the corresponding component
-        was identified as the source of predictions. Returns "invalid" when predictions
-        contain both classifications and detections, but couldn't identify results from
-        the ensemble. Returns "unknown" when no prediction is recognizable (e.g. when
-        there are only failures).
+        was identified as the source of predictions. Returns "unknown" when no
+        prediction is recognizable (e.g. when there are only failures).
     """
 
     found_classifications = False
@@ -185,14 +208,15 @@ def guess_predictions_source(
     found_ensemble_results = False
 
     for prediction in predictions.values():
-        if "classifications" in prediction or "classifications_list" in prediction:
+        detections = prediction.get("detections") or []
+        if "model_version" in prediction or any(
+            "processed_classification" in det for det in detections
+        ):
+            found_ensemble_results = True
+        if any("raw_classifications" in det for det in detections):
             found_classifications = True
         if "detections" in prediction:
             found_detections = True
-        if "prediction" in prediction or "processed_classifications" in prediction:
-            found_ensemble_results = True
-        if found_classifications and found_detections and not found_ensemble_results:
-            return "invalid"
 
     if found_ensemble_results:
         return "ensemble"
@@ -205,7 +229,7 @@ def guess_predictions_source(
 
 def custom_combine_predictions_fn(
     *,
-    classifications: dict[str, list],
+    classifications_list: list[dict],
     detections: list[dict],
     country: Optional[str],
     admin1_region: Optional[str],
@@ -214,17 +238,16 @@ def custom_combine_predictions_fn(
     enable_geofence: bool,
     geofence_fn: Callable,
     roll_up_fn: Callable,
-) -> PredictionType:
+) -> list[dict]:
     """Ensembles classifications and detections in a custom way.
 
     Args:
-        classifications:
-            Dict of classification results. "classes" and "scores" are expected to be
-            provided among the dict keys.
+        classifications_list:
+            List of classification results, one per detection. "classes" and "scores"
+            are expected to be provided among the dict keys.
         detections:
-            List of detection results, sorted in decreasing order of their confidence
-            score. Each detection is expected to be a dict providing "label" and "conf"
-            among its keys.
+            List of detection results, one per classification. Each detection is
+            expected to be a dict providing "label" and "conf" among its keys.
         country:
             Country (in ISO 3166-1 alpha-3 format) associated with predictions.
             Optional.
@@ -243,7 +266,8 @@ def custom_combine_predictions_fn(
             Callable to roll up labels to the first matching level.
 
     Returns:
-        A tuple of  describing the ensemble result.
+        A list of dicts describing the ensemble results, one per classification and in
+        the same order, each providing "class", "score" and "source" keys.
     """
 
     del detections  # Unused.
@@ -256,11 +280,14 @@ def custom_combine_predictions_fn(
     del roll_up_fn  # Unused.
 
     # Always return the second classifier prediction.
-    return (
-        classifications["classes"][1],
-        classifications["scores"][1],
-        "custom_ensemble",
-    )
+    return [
+        {
+            "class": classifications["classes"][1],
+            "score": classifications["scores"][1],
+            "source": "custom_ensemble",
+        }
+        for classifications in classifications_list
+    ]
 
 
 def say_yes_to_continue(question: str, stop_message: str) -> bool:
@@ -399,6 +426,17 @@ def main(argv: list[str]) -> None:
         ):
             return
 
+    # Allow conversion of -1 to None for int-valued inputs
+    max_classifications_per_image = _MAX_CLASSIFICATIONS_PER_IMAGE.value
+    if max_classifications_per_image is not None and max_classifications_per_image < 0:
+        max_classifications_per_image = None
+    max_classifications_per_detection = _MAX_CLASSIFICATIONS_PER_DETECTION.value
+    if (
+        max_classifications_per_detection is not None
+        and max_classifications_per_detection < 0
+    ):
+        max_classifications_per_detection = None
+
     # If a list of target species is given, check that it exists
     if _TARGET_SPECIES_TXT.value is not None and not local_file_exists(
         _TARGET_SPECIES_TXT.value
@@ -432,24 +470,15 @@ def main(argv: list[str]) -> None:
         force_model_download=_FORCE_MODEL_DOWNLOAD.value,
     )
     if hasattr(model, "classifier") and not hasattr(model, "detector"):
-        if (
-            model.classifier.model_info.type_ == "always_crop"
-            and not local_file_exists(_DETECTIONS_JSON.value)
-        ):
-            if not say_yes_to_continue(
-                question=(
-                    "Classifier expects detections JSON. Continue without providing "
-                    "this file and run classifier on full images instead of crops?"
-                ),
-                stop_message=(
-                    f"Please provide detections via --{_DETECTIONS_JSON.name} and make "
-                    "sure that file exists."
-                ),
-            ):
-                return
-        elif (
-            model.classifier.model_info.type_ == "full_image" and _DETECTIONS_JSON.value
+        if not model.whole_image_classifier and not local_file_exists(
+            _DETECTIONS_JSON.value
         ):
+            raise RuntimeError(
+                "This classifier classifies detection crops, so it needs detections. "
+                f"Please provide detections via --{_DETECTIONS_JSON.name} and make "
+                "sure that file exists."
+            )
+        if model.whole_image_classifier and _DETECTIONS_JSON.value:
             if not say_yes_to_continue(
                 question=(
                     "Classifier doesn't expect detections JSON, yet such file was "
@@ -467,6 +496,11 @@ def main(argv: list[str]) -> None:
             batch_size=_BATCH_SIZE.value,
             progress_bars=_PROGRESS_BARS.value,
             predictions_json=_PREDICTIONS_JSON.value,
+            detection_threshold_for_classification=(
+                _DETECTION_THRESHOLD_FOR_CLASSIFICATION.value
+            ),
+            max_classifications_per_image=max_classifications_per_image,
+            max_classifications_per_detection=max_classifications_per_detection,
         )
     elif _DETECTOR_ONLY.value:
         predictions_dict = model.detect(
@@ -482,6 +516,7 @@ def main(argv: list[str]) -> None:
             detections_dict=detections_dict,
             progress_bars=_PROGRESS_BARS.value,
             predictions_json=_PREDICTIONS_JSON.value,
+            max_classifications_per_detection=max_classifications_per_detection,
         )
     else:
         predictions_dict = model.predict(
@@ -490,7 +525,11 @@ def main(argv: list[str]) -> None:
             batch_size=_BATCH_SIZE.value,
             progress_bars=_PROGRESS_BARS.value,
             predictions_json=_PREDICTIONS_JSON.value,
-            max_classifications_per_image=_MAX_CLASSIFICATIONS_PER_IMAGE.value,
+            detection_threshold_for_classification=(
+                _DETECTION_THRESHOLD_FOR_CLASSIFICATION.value
+            ),
+            max_classifications_per_image=max_classifications_per_image,
+            max_classifications_per_detection=max_classifications_per_detection,
         )
     if predictions_dict is not None:
         print(
diff --git a/speciesnet/scripts/run_server.py b/speciesnet/scripts/run_server.py
index 79c5d58..d74a856 100644
--- a/speciesnet/scripts/run_server.py
+++ b/speciesnet/scripts/run_server.py
@@ -126,7 +126,7 @@ def _propagate_extra_fields(
             for field in self.extra_fields:
                 if field in instance:
                     new_predictions[instance["filepath"]][field] = instance[field]
-        return {"predictions": list(new_predictions.values())}
+        return {**predictions_dict, "predictions": list(new_predictions.values())}
 
     def predict(self, instances_dict, context):
         del context  # Unused.
diff --git a/speciesnet/utils.py b/speciesnet/utils.py
index af3c3e3..1bc2093 100644
--- a/speciesnet/utils.py
+++ b/speciesnet/utils.py
@@ -41,8 +41,20 @@
 import PIL.ImageOps
 import requests
 
+from speciesnet.constants import Detection
+from speciesnet.constants import Failure
+from speciesnet.constants import FORMAT_VERSION
+
 StrPath = Union[str, Path]
 
+# Per-image fields that only appear in output formats older than FORMAT_VERSION.
+_LEGACY_PREDICTION_FIELDS = (
+    "classifications",
+    "classifications_list",
+    "prediction",
+    "processed_classifications",
+)
+
 PIL.ImageFile.LOAD_TRUNCATED_IMAGES = True
 
 # Supported extensions for image discovery.
@@ -456,7 +468,8 @@ def load_partial_predictions(
         RuntimeError:
             If the partial predictions contain a filepath not found in the list of
             instances to check. To fix this, make sure that the partial predictions
-            originated from the same list of instances to process.
+            originated from the same list of instances to process. Also raised if the
+            partial predictions use an output format that is no longer supported.
     """
 
     if not predictions_json:
@@ -479,6 +492,15 @@ def load_partial_predictions(
                 f"the work using the same set of instances."
             )
 
+        legacy_fields = [f for f in _LEGACY_PREDICTION_FIELDS if f in prediction]
+        if legacy_fields:
+            raise RuntimeError(
+                f"Predictions from `{predictions_json}` use an older output format "
+                f"(found field `{legacy_fields[0]}` for `{filepath}`), and can't be "
+                f"reused. Regenerate them with the current version of SpeciesNet, or "
+                f"use a different output location."
+            )
+
         if "failures" in prediction:
             continue
 
@@ -511,3 +533,139 @@ def save_predictions(predictions_dict: dict, output_json: StrPath) -> None:
     write_json(predictions_dict, output_json_tmp, num_decimals=4)
     logging.info("Moving `%s` to `%s`.", output_json_tmp, output_json)
     output_json_tmp.replace(output_json)  # Atomic operation.
+
+
+def make_predictions_dict(predictions: list[dict]) -> dict:
+    """Wraps a list of per-image predictions into a predictions dict.
+
+    Args:
+        predictions:
+            List of per-image predictions.
+
+    Returns:
+        A predictions dict, with an "info" section describing the output format and a
+        "predictions" section with the given predictions.
+    """
+
+    return {
+        "info": {"format_version": FORMAT_VERSION},
+        "predictions": predictions,
+    }
+
+
+def whole_image_detection() -> dict:
+    """Returns the pseudo-detection used by whole-image classifiers.
+
+    Whole-image classifiers classify the entire image rather than individual crops, so
+    their classifications are attached to a single pseudo-detection that covers the
+    whole image.
+
+    Returns:
+        A detection dict for category "0", with a confidence of 1.0 and a bounding box
+        covering the whole image.
+    """
+
+    return {
+        "category": "0",
+        "label": Detection.WHOLE_IMAGE.value,
+        "conf": 1.0,
+        "bbox": [0.0, 0.0, 1.0, 1.0],
+    }
+
+
+def select_detections_for_classification(
+    detections: list[dict],
+    detection_threshold: float,
+    max_classifications: Optional[int] = None,
+) -> list[int]:
+    """Selects which detections of an image should be run through the classifier.
+
+    Args:
+        detections:
+            List of detections for an image. Each detection is expected to be a dict
+            providing "conf" among its keys.
+        detection_threshold:
+            Minimum confidence score for a detection to be classified.
+        max_classifications:
+            Maximum number of detections to classify. If more detections are above the
+            threshold, the ones with the highest confidence scores are selected. If
+            `None` or negative, all detections above the threshold are selected.
+
+    Returns:
+        Indices (into `detections`) of the detections to classify, in increasing order.
+    """
+
+    indices = [
+        idx for idx, det in enumerate(detections) if det["conf"] >= detection_threshold
+    ]
+    if max_classifications is not None and max_classifications >= 0:
+        indices = sorted(indices, key=lambda idx: detections[idx]["conf"], reverse=True)
+        indices = sorted(indices[:max_classifications])
+    return indices
+
+
+def build_classifier_result(
+    prediction: dict,
+    detections: Optional[list[dict]],
+    classified_indices: list[int],
+    whole_image: bool,
+) -> dict:
+    """Attaches raw classifier output to the detections it belongs to.
+
+    Args:
+        prediction:
+            Raw classifier prediction for an image, providing either "failures" or a
+            "classifications_list" with one classification per classified crop.
+        detections:
+            Detections for the image, or `None` if no detections are available. Ignored
+            for whole-image classifiers.
+        classified_indices:
+            Indices (into `detections`) of the detections that were cropped and
+            classified, matching the order of "classifications_list". Ignored for
+            whole-image classifiers.
+        whole_image:
+            Whether the classifier is a whole-image classifier, in which case the single
+            classification is attached to a whole-image pseudo-detection.
+
+    Returns:
+        A classifier result dict for the image, providing either "failures" or
+        "detections", where classified detections have a "raw_classifications" field.
+
+    Raises:
+        ValueError:
+            If the number of classifications doesn't match the number of classified
+            detections.
+    """
+
+    filepath = prediction["filepath"]
+    if "failures" in prediction:
+        return prediction
+    classifications_list = prediction["classifications_list"]
+
+    if whole_image:
+        if len(classifications_list) != 1:
+            raise ValueError(
+                f"Expected a single whole-image classification for `{filepath}`, got "
+                f"{len(classifications_list)}."
+            )
+        return {
+            "filepath": filepath,
+            "detections": [
+                {
+                    **whole_image_detection(),
+                    "raw_classifications": classifications_list[0],
+                }
+            ],
+        }
+
+    if detections is None:
+        return {"filepath": filepath, "failures": [Failure.CLASSIFIER.name]}
+    if len(classifications_list) != len(classified_indices):
+        raise ValueError(
+            f"Expected {len(classified_indices)} classifications for `{filepath}` (one "
+            f"per classified detection), got {len(classifications_list)}."
+        )
+    result_detections = [dict(det) for det in detections]
+    for idx, classifications in zip(classified_indices, classifications_list):
+        result_detections[idx]["raw_classifications"] = classifications
+    return {"filepath": filepath, "detections": result_detections}
diff --git a/speciesnet/utils_test.py b/speciesnet/utils_test.py
index 9601404..35c9680 100644
--- a/speciesnet/utils_test.py
+++ b/speciesnet/utils_test.py
@@ -326,6 +326,7 @@ def test_no_loading(self, tmp_path) -> None:
     @pytest.fixture
     def valid_predictions_json(self, tmp_path) -> Generator[Path, None, None]:
         json_content = {
+            "info": {"format_version": "20261015"},
             "predictions": [
                 {
                     "filepath": "a.jpg",
@@ -351,65 +352,58 @@ def valid_predictions_json(self, tmp_path) -> Generator[Path, None, None]:
                     "filepath": "c.jpg",
                     "failures": ["DETECTOR"],
                     "country": "COUNTRY_C",
-                    "classifications": {
-                        "classes": ["X", "Y", "Z"],
-                        "scores": [0.5, 0.3, 0.2],
-                    },
                     "model_version": "0.0.0",
                 },
                 {
                     "filepath": "d.jpg",
                     "failures": ["GEOLOCATION"],
-                    "classifications": {
-                        "classes": ["R", "S", "T"],
-                        "scores": [0.7, 0.2, 0.1],
-                    },
                     "detections": [
                         {
                             "category": "2",
                             "label": "human",
                             "conf": 0.7,
                             "bbox": [0.1, 0.2, 0.3, 0.4],
+                            "raw_classifications": {
+                                "classes": ["R", "S"],
+                                "scores": [0.7, 0.2],
+                            },
+                            "processed_classification": {
+                                "class": "R",
+                                "score": 0.7,
+                                "source": "test",
+                            },
                         }
                     ],
-                    "prediction": "R",
-                    "prediction_score": 0.7,
-                    "prediction_source": "test",
                     "model_version": "0.0.0",
                 },
                 {
                     "filepath": "e.jpg",
                     "country": "COUNTRY_E",
-                    "classifications": {
-                        "classes": ["K", "L", "M"],
-                        "scores": [0.9, 0.1, 0.0],
-                    },
                     "detections": [
                         {
                             "category": "2",
                             "label": "human",
                             "conf": 0.7,
                             "bbox": [0.1, 0.2, 0.3, 0.4],
+                            "raw_classifications": {
+                                "classes": ["K", "L"],
+                                "scores": [0.9, 0.1],
+                            },
+                            "processed_classification": {
+                                "class": "K",
+                                "score": 0.9,
+                                "source": "test",
+                            },
                         }
                     ],
-                    "prediction": "K",
-                    "prediction_score": 0.9,
-                    "prediction_source": "test",
                     "model_version": "0.0.0",
                 },
                 {
                     "filepath": "f.jpg",
-                    "classifications": {
-                        "classes": ["XYZ"],
-                        "scores": [0.8],
-                    },
                     "detections": [],
-                    "prediction": "XYZ",
-                    "prediction_score": 0.4,
-                    "prediction_source": "test",
                     "model_version": "0.0.0",
                 },
-            ]
+            ],
         }
 
         filepath = tmp_path / "valid_predictions.json"
@@ -435,33 +429,28 @@ def test_successful_loading(self, valid_predictions_json) -> None:
             "e.jpg": {
                 "filepath": "e.jpg",
                 "country": "COUNTRY_E",
-                "classifications": {
-                    "classes": ["K", "L", "M"],
-                    "scores": [0.9, 0.1, 0.0],
-                },
                 "detections": [
                     {
                         "category": "2",
                         "label": "human",
                         "conf": 0.7,
                         "bbox": [0.1, 0.2, 0.3, 0.4],
+                        "raw_classifications": {
+                            "classes": ["K", "L"],
+                            "scores": [0.9, 0.1],
+                        },
+                        "processed_classification": {
+                            "class": "K",
+                            "score": 0.9,
+                            "source": "test",
+                        },
                     }
                 ],
-                "prediction": "K",
-                "prediction_score": 0.9,
-                "prediction_source": "test",
                 "model_version": "0.0.0",
             },
             "f.jpg": {
                 "filepath": "f.jpg",
-                "classifications": {
-                    "classes": ["XYZ"],
-                    "scores": [0.8],
-                },
                 "detections": [],
-                "prediction": "XYZ",
-                "prediction_score": 0.4,
-                "prediction_source": "test",
                 "model_version": "0.0.0",
             },
         }
@@ -479,6 +468,35 @@ def test_successful_loading(self, valid_predictions_json) -> None:
                 [{"filepath": "a.jpg"}],
             )
 
+    @pytest.fixture
+    def legacy_predictions_json(self, tmp_path) -> Generator[Path, None, None]:
+        json_content = {
+            "predictions": [
+                {
+                    "filepath": "a.jpg",
+                    "classifications": {
+                        "classes": ["K", "L", "M"],
+                        "scores": [0.9, 0.1, 0.0],
+                    },
+                    "detections": [],
+                    "prediction": "K",
+                    "prediction_score": 0.9,
+                    "prediction_source": "test",
+                    "model_version": "0.0.0",
+                },
+            ]
+        }
+
+        filepath = tmp_path / "legacy_predictions.json"
+        with open(filepath, mode="w", encoding="utf-8") as fp:
+            json.dump(json_content, fp)
+        yield filepath
+        filepath.unlink()
+
+    def test_legacy_format_loading(self, legacy_predictions_json) -> None:
+        with pytest.raises(RuntimeError, match="older output format"):
+            load_partial_predictions(legacy_predictions_json, [{"filepath": "a.jpg"}])
+
     @pytest.fixture
     def invalid_predictions_json(self, tmp_path) -> Generator[Path, None, None]:
         filepath = tmp_path / "invalid_predictions.json"

From cbf284a7be9487adb25c89b5f962a7b01758d020 Mon Sep 17 00:00:00 2001
From: Dan Morris 
Date: Thu, 15 Oct 2026 17:52:05 -0700
Subject: [PATCH 09/12] lint fixes

---
 README.md                | 5 -----
 speciesnet/classifier.py | 3 ++-
 speciesnet/ensemble.py   | 2 +-
 3 files changed, 3 insertions(+), 7 deletions(-)

diff --git a/README.md b/README.md
index c6068a8..b4fa370 100644
--- a/README.md
+++ b/README.md
@@ -175,8 +175,6 @@ If you use this model, please cite:
 
 `run_model.py` produces output in .json format, containing an "info" section and an array called "predictions", with one element per image.  We provide a script to convert this format to the format used by [MegaDetector](https://github.com/agentmorris/MegaDetector), which can be imported into [Timelapse](https://timelapse.ucalgary.ca/), see [speciesnet_to_md.py](speciesnet/scripts/speciesnet_to_md.py).
 
-
-
 Each element in the "predictions" array contains the results for one image. Each element contains a list of detections, sorted in descending order of confidence.  Every detection with a confidence of at least `--detection_threshold_for_classification` (default 0.1) is classified, up to a maximum of `--max_classifications_per_image` detections per image (default: no maximum); when that maximum applies, the highest-confidence detections are the ones that get classified.  Detections below the threshold (or beyond the maximum) are still included in the output, just without classification fields.  If an image has no detections at or above the threshold, none of its detections are classified.
 
 Each classified detection has a "raw_classifications" field and a "processed_classification" field.  The "raw_classifications" field contains the top k classes from the classifier, where k is controlled by `--max_classifications_per_detection` (default 2), before geofencing and taxonomic rollup are applied.  So even if you specify a country code, you may see taxa in the "raw_classifications" field that are not found in the country you specified.  The "processed_classification" field contains the single class that results from applying geofencing and taxonomic rollup (see [ensemble decision-making](#ensemble-decision-making)), along with some heuristics about the detection category and confidence. If you specify a country code, the "processed_classification" field should only contain taxa that are found in the country you specified.
@@ -231,7 +229,6 @@ When you run only some of the components of the ensemble, the output file uses t
 - Classifier-only output (`--classifier_only`) contains only the "filepath", "failures", and "detections" fields for each image, and classified detections have a "raw_classifications" field, but no "processed_classification" field.  When running the crop classifier (v4.0.3a), the detections are copied from the file passed via `--detections_json`.  When running the whole-image classifier (v4.0.3b), the only detection is the whole-image pseudo-detection (category "0"), since the detector was not run.  So that the ensemble step can be run later, the "raw_classifications" field in classifier-only output contains at least the top 5 classes, even if `--max_classifications_per_detection` is smaller than 5.
 - Ensemble-only output (`--ensemble_only`) is identical to the output you would get from running the full ensemble.
 
-
 ## Visualizing SpeciesNet output
 
 As per above, many users will work with SpeciesNet results in open-source tools like [Timelapse](https://timelapse.ucalgary.ca/), which support the file format used by [MegaDetector](https://github.com/agentmorris/MegaDetector) (the format is described [here](https://lila.science/megadetector-output-format)).  If you used `run_md_and_speciesnet` to run SpeciesNet, you already have output in this format.  If you used `run_model`, we provide a [speciesnet_to_md](speciesnet/scripts/speciesnet_to_md.py) script to convert to this format.  Tools like Timelapse are a good way to visualize and interact with your SpeciesNet results.
@@ -281,7 +278,6 @@ python -m megadetector.postprocessing.postprocess_batch_results --help
 
 Both of these modules can also be called from Python code instead of from the command line.
 
-
 ## Ensemble decision-making
 
 As discussed above, `run_model` uses multiple steps to predict a single category for each classified detection, combining the strengths of the detector and the classifier.
@@ -350,4 +346,3 @@ It would be unfortunate if this whole README about camera trap images didn't sho
 ![giant armadillo](https://github.com/google/cameratrapai/raw/main/images/sample_image_oct.jpg)
 
 Image credit University of Minnesota, from the [Orinoquía Camera Traps](https://lila.science/datasets/orinoquia-camera-traps/) dataset.
-
diff --git a/speciesnet/classifier.py b/speciesnet/classifier.py
index fd23747..b5479f3 100644
--- a/speciesnet/classifier.py
+++ b/speciesnet/classifier.py
@@ -152,7 +152,8 @@ def preprocess(
 
         Returns:
             A list of preprocessed images (one element per bounding box in `always_crop`
-            mode, a single element in `full_image` mode), or `None` if no image was provided.
+            mode, a single element in `full_image` mode), or `None` if no image was
+            provided.
         """
         if img is None:
             return None
diff --git a/speciesnet/ensemble.py b/speciesnet/ensemble.py
index 2aa07d0..9596281 100644
--- a/speciesnet/ensemble.py
+++ b/speciesnet/ensemble.py
@@ -304,7 +304,7 @@ def combine(  # pylint: disable=too-many-positional-arguments
                     output_detections = [dict(det) for det in detections]
 
             # Ensemble each classified detection.
-            if pairs:
+            if pairs and output_detections is not None:
                 processed_classifications = self.prediction_combiner(
                     classifications_list=[
                         _top_classes(

From a1508480ab41c9a66074f48effbf7470ff511743 Mon Sep 17 00:00:00 2001
From: Dan Morris 
Date: Thu, 15 Oct 2026 19:29:25 -0700
Subject: [PATCH 10/12] disable GCA

---
 .gemini/config.yaml | 2 ++
 1 file changed, 2 insertions(+)
 create mode 100644 .gemini/config.yaml

diff --git a/.gemini/config.yaml b/.gemini/config.yaml
new file mode 100644
index 0000000..850168f
--- /dev/null
+++ b/.gemini/config.yaml
@@ -0,0 +1,2 @@
+code_review:
+  disable: true
\ No newline at end of file

From cc1d8b1496da0f1e6ef354523aa75a3ae84e5926 Mon Sep 17 00:00:00 2001
From: Dan Morris 
Date: Fri, 16 Oct 2026 08:47:12 -0700
Subject: [PATCH 11/12] Deleted speciesnet_to_md, referring to upstream version
 in README

---
 README.md                              |  21 +-
 speciesnet/scripts/speciesnet_to_md.py | 426 -------------------------
 2 files changed, 15 insertions(+), 432 deletions(-)
 delete mode 100644 speciesnet/scripts/speciesnet_to_md.py

diff --git a/README.md b/README.md
index b4fa370..f05e1cd 100644
--- a/README.md
+++ b/README.md
@@ -173,9 +173,9 @@ If you use this model, please cite:
 
 ## Output format from run_model
 
-`run_model.py` produces output in .json format, containing an "info" section and an array called "predictions", with one element per image.  We provide a script to convert this format to the format used by [MegaDetector](https://github.com/agentmorris/MegaDetector), which can be imported into [Timelapse](https://timelapse.ucalgary.ca/), see [speciesnet_to_md.py](speciesnet/scripts/speciesnet_to_md.py).
+`run_model.py` produces output in .json format, containing an "info" section and an array called "predictions", with one element per image.  We provide a script to convert this format to the [MegaDetector output format](https://lila.science/megadetector-output-format), which can be imported into image review tools like [Timelapse](https://timelapse.ucalgary.ca/); see the "[converting output to MegaDetector format](#converting-output-to-megadetector-format)" section below.
 
-Each element in the "predictions" array contains the results for one image. Each element contains a list of detections, sorted in descending order of confidence.  Every detection with a confidence of at least `--detection_threshold_for_classification` (default 0.1) is classified, up to a maximum of `--max_classifications_per_image` detections per image (default: no maximum); when that maximum applies, the highest-confidence detections are the ones that get classified.  Detections below the threshold (or beyond the maximum) are still included in the output, just without classification fields.  If an image has no detections at or above the threshold, none of its detections are classified.
+In the `run_model` output format, each element in the "predictions" array contains the results for one image. Each element contains a list of detections, sorted in descending order of confidence.  Every detection with a confidence of at least `--detection_threshold_for_classification` (default 0.1) is classified, up to a maximum of `--max_classifications_per_image` detections per image (default: no maximum); when that maximum applies, the highest-confidence detections are the ones that get classified.  Detections below the threshold (or beyond the maximum) are still included in the output, just without classification fields.  If an image has no detections at or above the threshold, none of its detections are classified.
 
 Each classified detection has a "raw_classifications" field and a "processed_classification" field.  The "raw_classifications" field contains the top k classes from the classifier, where k is controlled by `--max_classifications_per_detection` (default 2), before geofencing and taxonomic rollup are applied.  So even if you specify a country code, you may see taxa in the "raw_classifications" field that are not found in the country you specified.  The "processed_classification" field contains the single class that results from applying geofencing and taxonomic rollup (see [ensemble decision-making](#ensemble-decision-making)), along with some heuristics about the detection category and confidence. If you specify a country code, the "processed_classification" field should only contain taxa that are found in the country you specified.
 
@@ -229,11 +229,20 @@ When you run only some of the components of the ensemble, the output file uses t
 - Classifier-only output (`--classifier_only`) contains only the "filepath", "failures", and "detections" fields for each image, and classified detections have a "raw_classifications" field, but no "processed_classification" field.  When running the crop classifier (v4.0.3a), the detections are copied from the file passed via `--detections_json`.  When running the whole-image classifier (v4.0.3b), the only detection is the whole-image pseudo-detection (category "0"), since the detector was not run.  So that the ensemble step can be run later, the "raw_classifications" field in classifier-only output contains at least the top 5 classes, even if `--max_classifications_per_detection` is smaller than 5.
 - Ensemble-only output (`--ensemble_only`) is identical to the output you would get from running the full ensemble.
 
-## Visualizing SpeciesNet output
+## Converting output to MegaDetector format
+
+As per above, many users will work with SpeciesNet results in open-source tools like [Timelapse](https://timelapse.ucalgary.ca/) that support the file format used by [MegaDetector](https://github.com/agentmorris/MegaDetector) (the format is described [here](https://lila.science/megadetector-output-format)).  If you used `run_md_and_speciesnet` to run SpeciesNet, you already have output in this format.  If you used `run_model`, you can convert the `run_model` .json format to the MegaDetector format like this:
+
+```bash
+pip install megadetector-utils
+python -m megadetector.detection.run_md_and_speciesnet "c:\your\speciesnet\output\file.json" "c:\path\where\you\want\the\converted\file.json" --base_folder "c:\your\image\folder"
+```
 
-As per above, many users will work with SpeciesNet results in open-source tools like [Timelapse](https://timelapse.ucalgary.ca/), which support the file format used by [MegaDetector](https://github.com/agentmorris/MegaDetector) (the format is described [here](https://lila.science/megadetector-output-format)).  If you used `run_md_and_speciesnet` to run SpeciesNet, you already have output in this format.  If you used `run_model`, we provide a [speciesnet_to_md](speciesnet/scripts/speciesnet_to_md.py) script to convert to this format.  Tools like Timelapse are a good way to visualize and interact with your SpeciesNet results.
+Replace the filenames with real filenames, specifically (1) the path to the .json file you created with `run_model`, (2) the path where you want the converted .json file, and (3) the folder where your images live.
+
+## Visualizing SpeciesNet output
 
-If you want to use the command line or Python code to visualize SpeciesNet results, we recommend using the visualization tools provided in the [megadetector-utils Python package](https://pypi.org/project/megadetector-utils/).  For example, if you just ran either of these commands:
+As per above, tools like [Timelapse](https://timelapse.ucalgary.ca/) that load AI results are a good way to visualize and manipulate SpeciesNet results.  However, if you want to use the command line or Python code to visualize SpeciesNet results, we recommend using the visualization tools provided in the [megadetector-utils Python package](https://pypi.org/project/megadetector-utils/).  For example, if you just ran either of these commands:
 
 `python -m speciesnet.scripts.run_model --folders "c:\your\image\folder" --predictions_json "c:\your\output\file.json"`
 
@@ -246,7 +255,7 @@ pip install megadetector-utils
 python -m megadetector.visualization.visualize_detector_output "c:\your\output\file.json" "c:\folder\where\you\want\visualized\output"
 ```
 
-That will produce a folder of images with SpeciesNet results visualized on each image.  A typical use of this script would also use the --sample argument (to render a random subset of images, if what you want is to quickly grok how SpeciesNet did on a large dataset), and often the --html_output_file argument, to wrap the results in an HTML page that makes it quick to scroll through them.  Putting those together will give you pages like these:
+That will produce a folder of images with SpeciesNet results visualized on each image.  A typical use of this script would also use the `--sample` argument (to render a random subset of images, if what you want is to quickly grok how SpeciesNet did on a large dataset), and often the `--html_output_file` argument, to wrap the results in an HTML page that makes it quick to scroll through them.  Putting those together will give you pages like these:
 
 * [Fun preview page for Caltech Camera Traps](https://lila.science/public/speciesnet-previews/speciesnet-visualization-examples/caltech-camera-traps/)
 * [Fun preview page for Idaho Camera Traps](https://lila.science/public/speciesnet-previews/speciesnet-visualization-examples/idaho-camera-traps/)
diff --git a/speciesnet/scripts/speciesnet_to_md.py b/speciesnet/scripts/speciesnet_to_md.py
deleted file mode 100644
index 8badf85..0000000
--- a/speciesnet/scripts/speciesnet_to_md.py
+++ /dev/null
@@ -1,426 +0,0 @@
-# Copyright 2024 Google LLC
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     https://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""Script to convert SpeciesNet .json format to MegaDetector/Timelapse .json format."""
-
-import argparse
-import json
-import sys
-
-blank_prediction_string = "f1856211-cfb7-4a5b-9158-c0f72fd09ee6;;;;;;blank"
-no_cv_result_prediction_string = "f2efdae9-efb8-48fb-8a91-eccf79ab4ffb;no cv result;no cv result;no cv result;no cv result;no cv result;no cv result"
-rodent_prediction_string = (
-    "90d950db-2106-4bd9-a4c1-777604c3eada;mammalia;rodentia;;;;rodent"
-)
-mammal_prediction_string = "f2d233e3-80e3-433d-9687-e29ecc7a467a;mammalia;;;;;mammal"
-animal_prediction_string = "1f689929-883d-4dae-958c-3d57ab5b6c16;;;;;;animal"
-human_prediction_string = "990ae9dd-7a59-4344-afcb-1b7b21368000;mammalia;primates;hominidae;homo;sapiens;human"
-
-
-def invert_dictionary(d):
-    """
-    Creates a new dictionary that maps d.values() to d.keys().  Does not check
-    uniqueness.
-
-    Args:
-        d (dict): dictionary to invert
-
-    Returns:
-        dict: inverted copy of [d]
-    """
-
-    return {v: k for k, v in d.items()}
-
-
-def sort_list_of_dicts_by_key(L, k, reverse=False):
-    """
-    Sorts the list of dictionaries [L] by the key [k].
-
-    Args:
-        L (list): list of dictionaries to sort
-        k (object, typically str): the sort key
-        reverse (bool, optional): whether to sort in reverse (descending) order
-
-    Returns:
-        dict: sorted copy of [d]
-    """
-    return sorted(L, key=lambda d: d[k], reverse=reverse)
-
-
-def is_list_sorted(L, reverse=False):
-    """
-    Returns True if the list L appears to be sorted, otherwise False.
-
-    Calling is_list_sorted(L,reverse=True) is the same as calling
-    is_list_sorted(L.reverse(),reverse=False).
-
-    Args:
-        L (list): list to evaluate
-        reverse (bool, optional): whether to reverse the list before evaluating sort status
-
-    Returns:
-        bool: True if the list L appears to be sorted, otherwise False
-    """
-
-    if reverse:
-        return all(L[i] >= L[i + 1] for i in range(len(L) - 1))
-    else:
-        return all(L[i] <= L[i + 1] for i in range(len(L) - 1))
-
-
-def get_common_name_from_prediction_string(s):
-    """
-    Extract the common name from the seven-token prediction string [s], or generate
-    a reasonable one (e.g. "vulpes genus").  Prediction strings look like:
-
-    '90d950db-2106-4bd9-a4c1-777604c3eada;mammalia;rodentia;;;;rodent'
-
-    Args:
-        s (str): the string for which we should extract a common name
-
-    Returns:
-        str: the extracted common name
-    """
-
-    s = s.strip()
-    tokens = s.split(";")
-    assert len(tokens) == 7, "Invalid prediction string: {}".format(s)
-    tokens = [token.strip() for token in tokens]
-
-    common_name = None
-
-    i_token = 6
-
-    while i_token >= 0:
-
-        token = tokens[i_token]
-
-        if len(token) > 0:
-
-            # Non-empty common name
-            if i_token == 6:
-                common_name = token
-            # Non-empty species name
-            elif i_token == 5:
-                # If a species name is present, a genus name should always be present
-                if len(tokens[4]) == 0:
-                    print("Warning: invalid genus/species combination {}".format(s))
-                    common_name = tokens[5]
-                else:
-                    # Create binomial name
-                    common_name = tokens[4] + " " + tokens[5]
-            # Non-empty genus name
-            elif i_token == 4:
-                # Not a typo, this is a convention, genus-level names end with "species"
-                common_name = tokens[4] + " species"
-            # Non-empty family name
-            elif i_token == 3:
-                common_name = tokens[3] + " family"
-            # Non-empty order name
-            elif i_token == 2:
-                common_name = tokens[2] + " order"
-            # Non-empty class name
-            elif i_token == 1:
-                common_name = tokens[1] + " class"
-            # Non-empty ID
-            elif i_token == 0:
-                common_name = tokens[0] + " category"
-            break
-
-        # ...if this token is non-empty
-
-        i_token -= 1
-
-    # ...for each token
-
-    if common_name is None:
-        assert s == ";;;;;;"
-        common_name = "empty_prediction_string"
-
-    return common_name
-
-
-def generate_md_results_from_predictions_json(
-    predictions_json_file,
-    md_results_file,
-    base_folder=None,
-    convert_homo_species_to_human=True,
-):
-    """
-    Generate an MD-formatted .json file from a predictions.json file.  Typically,
-    MD results files use relative paths, and predictions.json files use absolute paths, so
-    this function optionally removes the leading string [base_folder] from all file names.
-
-    Currently just applies the top classification category to every detection.  If the top classification
-    is "blank", writes an empty detection list.
-
-    speciesnet_to_md.py is a command-line driver for this function.
-
-    Args:
-        predictions_json_file (str): path to a predictions.json file
-        md_results_file (str): path to which we should write an MD-formatted .json file
-        base_folder (str, optional): leading string to remove from each path in the predictions.json file
-        convert_homo_species_to_human (bool, optional): the ensemble often rolls human
-            predictions up to "homo species", which isn't wrong, but looks odd.  This forces
-            these back to "homo sapiens".
-    """
-
-    # Read predictions file
-    with open(predictions_json_file, "r") as f:
-        predictions = json.load(f)
-    predictions = predictions["predictions"]
-    assert isinstance(predictions, list)
-
-    # Convert backslashes to forward slashes in both filenames and the base folder string
-    for im in predictions:
-        im["filepath"] = im["filepath"].replace("\\", "/")
-    if base_folder is not None:
-        base_folder = base_folder.replace("\\", "/")
-
-    detection_category_id_to_name = {}
-    classification_category_name_to_id = {}
-
-    # Keep track of detections that don't have an assigned detection category; these
-    # are fake detections we create for non-blank images with non-empty detection lists.
-    # We need to go back later and give them a legitimate detection category ID.
-    all_unknown_detections = []
-
-    # Create the output images list
-    images_out = []
-
-    # im_in = predictions[0]
-    for im_in in predictions:
-
-        # blank_prediction_string
-        im_out = {}
-
-        fn = im_in["filepath"]
-        if base_folder is not None:
-            if fn.startswith(base_folder):
-                fn = fn.replace(base_folder, "", 1)
-            elif fn.startswith(base_folder + "/"):
-                fn = fn.replace(base_folder + "/", "", 1)
-
-        im_out["file"] = fn
-
-        if "failures" in im_in:
-
-            im_out["failure"] = str(im_in["failures"])
-            im_out["detections"] = None
-
-        else:
-
-            im_out["detections"] = []
-
-            if "detections" in im_in:
-
-                if len(im_in["detections"]) == 0:
-                    im_out["detections"] = []
-                else:
-                    # det_in = im_in['detections'][0]
-                    for det_in in im_in["detections"]:
-                        det_out = {}
-                        if det_in["category"] in detection_category_id_to_name:
-                            assert (
-                                detection_category_id_to_name[det_in["category"]]
-                                == det_in["label"]
-                            )
-                        else:
-                            detection_category_id_to_name[det_in["category"]] = det_in[
-                                "label"
-                            ]
-                        det_out = {}
-                        for s in ["category", "conf", "bbox"]:
-                            det_out[s] = det_in[s]
-                        im_out["detections"].append(det_out)
-
-            # ...if detections are present
-
-            class_to_assign = None
-            class_confidence = None
-
-            if "classifications" in im_in:
-
-                classifications = im_in["classifications"]
-                assert len(classifications["scores"]) == len(classifications["classes"])
-                assert is_list_sorted(classifications["scores"], reverse=True)
-                class_to_assign = classifications["classes"][0]
-                class_confidence = classifications["scores"][0]
-
-            if "prediction" in im_in:
-
-                class_to_assign = im_in["prediction"]
-                if convert_homo_species_to_human and (
-                    ("hominidae;homo" in class_to_assign)
-                    or ("homo species") in class_to_assign
-                ):
-                    class_to_assign = human_prediction_string
-                class_confidence = im_in["prediction_score"]
-
-            if class_to_assign is not None:
-
-                if class_to_assign == blank_prediction_string:
-
-                    # This is a scenario that's not captured well by the MD format: a blank prediction
-                    # with detections present.  But, for now, don't do anything special here, just making
-                    # a note of this.
-                    if len(im_out["detections"]) > 0:
-                        pass
-
-                else:
-
-                    assert not class_to_assign.endswith("blank")
-
-                    # This is a scenario that's not captured well by the MD format: no detections present,
-                    # but a non-blank prediction.  For now, create a fake detection to handle this prediction.
-                    if len(im_out["detections"]) == 0:
-
-                        print(
-                            "Warning: creating fake detection for non-blank whole-image classification"
-                        )
-                        det_out = {}
-                        all_unknown_detections.append(det_out)
-
-                        # We will change this to a string-int later
-                        det_out["category"] = "unknown"
-                        det_out["conf"] = class_confidence
-                        det_out["bbox"] = [0, 0, 1, 1]
-                        im_out["detections"].append(det_out)
-
-                # ...if this is/isn't a blank classification
-
-                # Attach that classification to each detection
-
-                # Create a new category ID if necessary
-                if class_to_assign in classification_category_name_to_id:
-                    classification_category_id = classification_category_name_to_id[
-                        class_to_assign
-                    ]
-                else:
-                    classification_category_id = str(
-                        len(classification_category_name_to_id)
-                    )
-                    classification_category_name_to_id[class_to_assign] = (
-                        classification_category_id
-                    )
-
-                for det in im_out["detections"]:
-                    det["classifications"] = []
-                    det["classifications"].append(
-                        [classification_category_id, class_confidence]
-                    )
-
-            # ...if we have some type of classification for this image
-
-        # ...if this is/isn't a failure
-
-        images_out.append(im_out)
-
-    # ...for each image
-
-    # Fix the 'unknown' category
-
-    if len(all_unknown_detections) > 0:
-
-        max_detection_category_id = max(
-            [int(x) for x in detection_category_id_to_name.keys()]
-        )
-        unknown_category_id = str(max_detection_category_id + 1)
-        detection_category_id_to_name[unknown_category_id] = "unknown"
-
-        for det in all_unknown_detections:
-            assert det["category"] == "unknown"
-            det["category"] = unknown_category_id
-
-    # Sort by filename
-
-    images_out = sort_list_of_dicts_by_key(images_out, "file")
-
-    # Prepare friendly classification names
-
-    classification_category_descriptions = invert_dictionary(
-        classification_category_name_to_id
-    )
-    classification_categories_out = {}
-    for category_id in classification_category_descriptions.keys():
-        category_name = get_common_name_from_prediction_string(
-            classification_category_descriptions[category_id]
-        )
-        classification_categories_out[category_id] = category_name
-
-    # Prepare the output dict
-
-    detection_categories_out = detection_category_id_to_name
-    info = {}
-    info["format_version"] = 1.4
-    info["detector"] = "converted_from_predictions_json"
-
-    output_dict = {}
-    output_dict["info"] = info
-    output_dict["detection_categories"] = detection_categories_out
-    output_dict["classification_categories"] = classification_categories_out
-    output_dict["classification_category_descriptions"] = (
-        classification_category_descriptions
-    )
-    output_dict["images"] = images_out
-
-    with open(md_results_file, "w") as f:
-        json.dump(output_dict, f, indent=1)
-
-    # TODO: ideally we would validate the output, but this requires a lot more imports,
-    # so deferring this.
-    """
-    validation_options = ValidateBatchResultsOptions()
-    validation_options.raise_errors = True
-    _ = validate_batch_results(md_results_file, options=validation_options)
-    """
-
-
-def main():
-
-    parser = argparse.ArgumentParser()
-    parser.add_argument(
-        "predictions_json_file",
-        action="store",
-        type=str,
-        help=".json file to convert from SpeciesNet predictions.json format to MD format",
-    )
-    parser.add_argument(
-        "md_results_file",
-        action="store",
-        type=str,
-        help="output file to write in MD format",
-    )
-    parser.add_argument(
-        "--base_folder",
-        action="store",
-        type=str,
-        default=None,
-        help="leading string to remove from each path in the predictions.json "
-        + "file (to convert from absolute to relative paths)",
-    )
-
-    if len(sys.argv[1:]) == 0:
-        parser.print_help()
-        parser.exit()
-
-    args = parser.parse_args()
-
-    generate_md_results_from_predictions_json(
-        args.predictions_json_file, args.md_results_file, args.base_folder
-    )
-
-
-if __name__ == "__main__":
-    main()

From 817d26de4630df8b3443604beafb768427278cdd Mon Sep 17 00:00:00 2001
From: Dan Morris 
Date: Fri, 16 Oct 2026 09:49:27 -0700
Subject: [PATCH 12/12] package version bump

---
 pyproject.toml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pyproject.toml b/pyproject.toml
index 01fbdf5..325832e 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "speciesnet"
-version = "5.0.5"
+version = "5.0.6"
 authors = [
     {name = "Google Camera Traps AI team", email = "cameratraps@google.com"},
 ]

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