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