Add Google Gemini 3.5 VLM support

src/supervision/detection/core.py

@@ -977,6 +977,7 @@ def from_lmm(
         | Qwen2.5-VL          | `QWEN_2_5_VL`        | detection               | `resolution_wh`, `input_wh` | `classes`           |
         | Google Gemini 2.0   | `GOOGLE_GEMINI_2_0`  | detection               | `resolution_wh`             | `classes`           |
         | Google Gemini 2.5   | `GOOGLE_GEMINI_2_5`  | detection, segmentation | `resolution_wh`             | `classes`           |
+        | Google Gemini 3.5   | `GOOGLE_GEMINI_3_5`  | detection, segmentation | `resolution_wh`             | `classes`           |
         | Moondream           | `MOONDREAM`          | detection               | `resolution_wh`             |                     |
         | DeepSeek-VL2        | `DEEPSEEK_VL_2`      | detection               | `resolution_wh`             | `classes`           |
 
@@ -1425,6 +1426,7 @@ def from_lmm(
             LMM.DEEPSEEK_VL_2: VLM.DEEPSEEK_VL_2,
             LMM.GOOGLE_GEMINI_2_0: VLM.GOOGLE_GEMINI_2_0,
             LMM.GOOGLE_GEMINI_2_5: VLM.GOOGLE_GEMINI_2_5,
+            LMM.GOOGLE_GEMINI_3_5: VLM.GOOGLE_GEMINI_3_5,
         }
 
         if isinstance(lmm, LMM):
@@ -1464,6 +1466,7 @@ def from_vlm(
         | Qwen3-VL            | `QWEN_3_VL`          | detection               | `resolution_wh`,            | `classes`           |
         | Google Gemini 2.0   | `GOOGLE_GEMINI_2_0`  | detection               | `resolution_wh`             | `classes`           |
         | Google Gemini 2.5   | `GOOGLE_GEMINI_2_5`  | detection, segmentation | `resolution_wh`             | `classes`           |
+        | Google Gemini 3.5   | `GOOGLE_GEMINI_3_5`  | detection, segmentation | `resolution_wh`             | `classes`           |
         | Moondream           | `MOONDREAM`          | detection               | `resolution_wh`             |                     |
         | DeepSeek-VL2        | `DEEPSEEK_VL_2`      | detection               | `resolution_wh`             | `classes`           |
 
@@ -1931,7 +1934,7 @@ def from_vlm(
             xyxy = from_moondream(result, **kwargs)
             return cls(xyxy=xyxy)
 
-        if vlm == VLM.GOOGLE_GEMINI_2_5:
+        if vlm in (VLM.GOOGLE_GEMINI_2_5, VLM.GOOGLE_GEMINI_3_5):
             assert isinstance(result, str)
             gemini_result = from_google_gemini_2_5(result, **kwargs)
             data = {CLASS_NAME_DATA_FIELD: gemini_result[2]}

src/supervision/detection/vlm.py

@@ -33,6 +33,7 @@ class LMM(Enum):
         QWEN_3_VL: Qwen3-VL open vision-language model from Alibaba.
         GOOGLE_GEMINI_2_0: Google Gemini 2.0 vision-language model.
         GOOGLE_GEMINI_2_5: Google Gemini 2.5 vision-language model.
+        GOOGLE_GEMINI_3_5: Google Gemini 3.5 vision-language model.
         MOONDREAM: The Moondream vision-language model.
     """
 
@@ -43,6 +44,7 @@ class LMM(Enum):
     DEEPSEEK_VL_2 = "deepseek_vl_2"
     GOOGLE_GEMINI_2_0 = "gemini_2_0"
     GOOGLE_GEMINI_2_5 = "gemini_2_5"
+    GOOGLE_GEMINI_3_5 = "gemini_3_5"
     MOONDREAM = "moondream"
 
     @classmethod
@@ -80,6 +82,7 @@ class VLM(Enum):
         QWEN_3_VL: Qwen3-VL open vision-language model from Alibaba.
         GOOGLE_GEMINI_2_0: Google Gemini 2.0 vision-language model.
         GOOGLE_GEMINI_2_5: Google Gemini 2.5 vision-language model.
+        GOOGLE_GEMINI_3_5: Google Gemini 3.5 vision-language model.
         MOONDREAM: The Moondream vision-language model.
     """
 
@@ -90,6 +93,7 @@ class VLM(Enum):
     DEEPSEEK_VL_2 = "deepseek_vl_2"
     GOOGLE_GEMINI_2_0 = "gemini_2_0"
     GOOGLE_GEMINI_2_5 = "gemini_2_5"
+    GOOGLE_GEMINI_3_5 = "gemini_3_5"
     MOONDREAM = "moondream"
 
     @classmethod
@@ -120,6 +124,7 @@ def from_value(cls, value: VLM | str) -> VLM:
     VLM.DEEPSEEK_VL_2: str,
     VLM.GOOGLE_GEMINI_2_0: str,
     VLM.GOOGLE_GEMINI_2_5: str,
+    VLM.GOOGLE_GEMINI_3_5: str,
     VLM.MOONDREAM: dict,
 }
 
@@ -131,6 +136,7 @@ def from_value(cls, value: VLM | str) -> VLM:
     VLM.DEEPSEEK_VL_2: ["resolution_wh"],
     VLM.GOOGLE_GEMINI_2_0: ["resolution_wh"],
     VLM.GOOGLE_GEMINI_2_5: ["resolution_wh"],
+    VLM.GOOGLE_GEMINI_3_5: ["resolution_wh"],
     VLM.MOONDREAM: ["resolution_wh"],
 }
 
@@ -142,6 +148,7 @@ def from_value(cls, value: VLM | str) -> VLM:
     VLM.DEEPSEEK_VL_2: ["resolution_wh", "classes"],
     VLM.GOOGLE_GEMINI_2_0: ["resolution_wh", "classes"],
     VLM.GOOGLE_GEMINI_2_5: ["resolution_wh", "classes"],
+    VLM.GOOGLE_GEMINI_3_5: ["resolution_wh", "classes"],
     VLM.MOONDREAM: ["resolution_wh"],
 }
 

tests/detection/test_vlm.py

@@ -1297,3 +1297,62 @@ def test_from_deepseek_vl_2(
             detections.data[CLASS_NAME_DATA_FIELD],
             expected_detections.data[CLASS_NAME_DATA_FIELD],
         )
+
+
+@pytest.mark.parametrize(
+    ("result", "resolution_wh", "classes"),
+    [
+        ("random text", (1000, 1000), None),
+        ("```json\n[]\n```", (1000, 1000), None),
+        (
+            """```json
+            [
+                {"box_2d": [100, 200, 300, 400], "label": "cat", "confidence": 0.8}
+            ]
+            ```""",
+            (1000, 500),
+            None,
+        ),
+        (
+            """```json
+            [
+                {"box_2d": [10, 20, 110, 120], "label": "cat", "confidence": 0.8},
+                {"box_2d": [50, 100, 150, 200], "label": "dog", "confidence": 0.9}
+            ]
+            ```""",
+            (640, 480),
+            ["cat", "dog"],
+        ),
+    ],
+)
+def test_from_google_gemini_3_5_matches_2_5(
+    result: str,
+    resolution_wh: tuple[int, int],
+    classes: list[str] | None,
+):
+    detections_2_5 = Detections.from_vlm(
+        vlm=VLM.GOOGLE_GEMINI_2_5,
+        result=result,
+        resolution_wh=resolution_wh,
+        classes=classes,
+    )
+    detections_3_5 = Detections.from_vlm(
+        vlm=VLM.GOOGLE_GEMINI_3_5,
+        result=result,
+        resolution_wh=resolution_wh,
+        classes=classes,
+    )
+
+    assert len(detections_2_5) == len(detections_3_5)
+
+    if len(detections_2_5) == 0:
+        return
+
+    assert np.allclose(detections_2_5.xyxy, detections_3_5.xyxy)
+    assert np.array_equal(detections_2_5.class_id, detections_3_5.class_id)
+    assert np.array_equal(
+        detections_2_5.data[CLASS_NAME_DATA_FIELD],
+        detections_3_5.data[CLASS_NAME_DATA_FIELD],
+    )
+    if detections_2_5.confidence is not None:
+        assert np.allclose(detections_2_5.confidence, detections_3_5.confidence)