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README.md

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+---
+tags:
+- object-detection
+- vision
+- fasterrcnn
+- onnx
+license: apache-2.0
+---
+
+# Custom Faster R-CNN Model
+
+## Usage
+
+### PyTorch
+```python
+from inference import FasterRCNNInference
+from PIL import Image
+
+model = FasterRCNNInference(".")
+image = Image.open("your_image.jpg")
+results = model.predict([image])

config.json

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+{
+  "architectures": [
+    "FasterRCNNModel"
+  ],
+  "id2label": {
+    "0": "class_0",
+    "1": "class_1",
+    "2": "class_2",
+    "3": "class_3",
+    "4": "class_4",
+    "5": "class_5",
+    "6": "class_6",
+    "7": "class_7",
+    "8": "class_8",
+    "9": "class_9",
+    "10": "class_10",
+    "11": "class_11",
+    "12": "class_12",
+    "13": "class_13",
+    "14": "class_14",
+    "15": "class_15",
+    "16": "class_16",
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+    "19": "class_19",
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+    "78": "class_78",
+    "79": "class_79"
+  },
+  "label2id": {
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+    "class_8": 8,
+    "class_9": 9
+  },
+  "max_size": 1333,
+  "min_size": 800,
+  "model_type": "fasterrcnn",
+  "num_classes": 80,
+  "torch_dtype": "float32",
+  "transformers_version": "4.50.3"
+}

inference.py

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+from PIL import Image
+import torch
+import numpy as np
+from transformers import PreTrainedModel
+import onnxruntime
+
+class FasterRCNNInference:
+    def __init__(self, model_path):
+        # Load ONNX model
+        self.ort_session = onnxruntime.InferenceSession(f"{model_path}/model.onnx")
+        self.processor = torch.load(f"{model_path}/processor.bin")
+        
+    def predict(self, images, threshold=0.5):
+        # Preprocess
+        inputs = self.processor(images)
+        pixel_values = inputs["pixel_values"].numpy()
+        
+        # Run inference
+        outputs = self.ort_session.run(None, {"pixel_values": pixel_values})
+        
+        # Post-process
+        results = []
+        for boxes, scores, labels in zip(outputs[0], outputs[1], outputs[2]):
+            keep = scores > threshold
+            results.append({
+                "boxes": boxes[keep],
+                "scores": scores[keep],
+                "labels": labels[keep]
+            })
+        
+        return results

model.onnx

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+size 331069979

model.safetensors

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processor.bin

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