Create README.md

README.md

@@ -0,0 +1,41 @@
+# 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}")