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README.md

@@ -1,13 +1,24 @@
 ---
-title: ObjectDetection With DETR
-emoji: 😻
+title: Object Detection with DETR
+emoji: 🔍
 colorFrom: purple
-colorTo: indigo
+colorTo: pink
 sdk: gradio
 sdk_version: 5.11.0
 app_file: app.py
 pinned: false
 license: mit
+tags:
+  - object-detection
+  - vision
+  - transformers
+  - detr
+  - deep-learning
+  - gradio
+datasets:
+  - coco
+models:
+  - facebook/detr-resnet-50
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import gradio as gr
+from transformers import DetrImageProcessor, DetrForObjectDetection
+from PIL import Image, ImageDraw
+
+# Load pre-trained model and image processor
+model_name = "facebook/detr-resnet-50"
+model = DetrForObjectDetection.from_pretrained(model_name)
+processor = DetrImageProcessor.from_pretrained(model_name)
+
+# Define function for object detection
+def detect_objects(image):
+    inputs = processor(images=image, return_tensors="pt")
+    outputs = model(**inputs)
+
+    # Get predictions
+    target_sizes = [image.size[::-1]]  # (height, width)
+    results = processor.post_process_object_detection(outputs, target_sizes=target_sizes, threshold=0.9)[0]
+
+    # Draw bounding boxes on the image
+    draw = ImageDraw.Draw(image)
+    for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
+        box = [round(i, 2) for i in box.tolist()]
+        draw.rectangle(box, outline="red", width=3)
+        label_name = model.config.id2label[label.item()]
+        draw.text((box[0], box[1]), f"{label_name} ({score:.2f})", fill="red")
+
+    return image
+
+# Create Gradio interface
+interface = gr.Interface(
+    fn=detect_objects,
+    inputs=gr.Image(type="pil"),
+    outputs=gr.Image(type="pil"),
+    title="Object Detection App",
+    description="Upload an image to detect objects using the DETR model."
+)
+
+# Launch the app
+if __name__ == "__main__":
+    interface.launch()

requirements.txt

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+torch
+gradio
+transformers
+pillow
+timm