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+ # DETR (DEtection TRansformer) for Object Detection
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+
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+ ## Model Description
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+
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+ This model is a pre-trained DETR model for object detection. It uses a Transformer architecture to predict bounding boxes and class labels for each object in an image. It was trained on the COCO dataset and is capable of detecting a wide variety of objects in real-world images.
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+
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+ ## Model Details
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+
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+ - Model: `facebook/detr-resnet-50`
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+ - Framework: PyTorch
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+ - Task: Object Detection
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+ - Input: Image (H, W, C)
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+ - Output: Bounding boxes and class labels for detected objects
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+ - License: MIT
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+
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+ ## How to Use
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+
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+ ```python
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+ from transformers import DetrForObjectDetection, DetrImageProcessor
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+ from PIL import Image
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+ import torch
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+
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+ # Load the processor and model
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+ processor = DetrImageProcessor.from_pretrained("your-username/detr-object-detection")
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+ model = DetrForObjectDetection.from_pretrained("your-username/detr-object-detection")
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+
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+ # Prepare the image
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+ image = Image.open("path_to_image.jpg")
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+
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+ # Process the image
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+ inputs = processor(images=image, return_tensors="pt")
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+ outputs = model(**inputs)
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+
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+ # Post-process and display the results
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+ target_sizes = torch.tensor([image.size[::-1]])
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+ results = processor.post_process_object_detection(outputs, target_sizes=target_sizes, threshold=0.9)[0]
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+
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+ # Print and visualize detected objects
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+ for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
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+ box = [round(i, 2) for i in box.tolist()]
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+ print(f"Detected {model.config.id2label[label.item()]} with confidence {round(score.item(), 3)} at location {box}")