Add object detection model for guitars and wristwatches with Gradio interface

.gitignore

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+.gradio/certificate.pem
+.venv
+

README.md

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 license: apache-2.0
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Guitar and Wristwatch Detection Model
+
+This Space demonstrates an object detection model trained to identify guitars and wristwatches in images. The model is based on YOLOv8 and has been optimized using OpenVINO.
+
+## Model Details
+- Architecture: YOLOv8
+- Format: OpenVINO
+- Classes: Acoustic Guitar, Wristwatch
+- Input: Images (JPG, PNG)
+- Output: Annotated images with bounding boxes and confidence scores
+
+## Usage
+1. Upload an image or use one of the example images
+2. The model will detect and highlight guitars and wristwatches in the image
+3. Results show bounding boxes with confidence scores
+
+## Examples
+The app includes test images demonstrating detection capabilities:
+- Acoustic guitars (acoustic_guitar_005.jpg, acoustic_guitar_006.jpg)
+- Analog watches (analog_watch_024.jpg, analog_watch_025.jpg)
+
+## Technical Details
+- Built with Gradio 5.6.0
+- Uses Ultralytics YOLO for inference
+- OpenVINO optimization for improved performance
+
+## License
+Apache 2.0
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+from ultralytics import YOLO
+from PIL import Image
+import gradio as gr
+from huggingface_hub import hf_hub_download
+import os
+
+def load_model(repo_id=None, local_path=None):
+    """
+    Load YOLO model either from local path or Hugging Face hub.
+    
+    Args:
+        repo_id (str, optional): Hugging Face repository ID
+        local_path (str, optional): Local path to model directory
+        
+    Returns:
+        YOLO: Loaded model
+    """
+    try:
+        if local_path:
+            # Load from local path - point to the directory containing the model
+            model = YOLO(local_path, task='detect')
+        else:
+            # Load from Hugging Face hub
+            model_path = hf_hub_download(repo_id=repo_id, filename='best_openvino_model')
+            model = YOLO(model_path, task='detect')
+        return model
+    except Exception as e:
+        raise Exception(f"Error loading model: {str(e)}")
+
+def predict(image, use_local=True):
+    """
+    Run inference on input image.
+    
+    Args:
+        image: Input image (PIL Image)
+        use_local (bool): Whether to use local model or Hugging Face model
+        
+    Returns:
+        PIL Image: Annotated image with detections
+    """
+    try:
+        # Choose model source based on environment
+        if use_local:
+            model_path = r'C:\Manikandan\NYP\ITI107\Assignment_draft\output_models\best_openvino_model'
+            model = load_model(local_path=model_path)
+        else:
+            # Use your Hugging Face model repository
+            model = load_model(repo_id="mail2kandan/guitar_watch_openvino_model")
+        
+        # Run inference
+        results = model.predict(image, conf=0.5, iou=0.6)
+        
+        # Get the plotted image with detections
+        img_bgr = results[0].plot()
+        
+        # Convert BGR to RGB for display
+        return Image.fromarray(img_bgr[..., ::-1])
+    
+    except Exception as e:
+        raise gr.Error(f"Prediction error: {str(e)}")
+
+# Create Gradio interface
+def create_interface(use_local=True):
+    return gr.Interface(
+        fn=lambda img: predict(img, use_local),
+        inputs=gr.Image(type="pil"),
+        outputs=gr.Image(type="pil"),
+        title="Guitar and Wristwatch Detection",
+        description="Upload an image to detect guitars and wristwatches.",
+        examples=[
+            ["testImages/acoustic_guitar_005.jpg"],
+            ["testImages/acoustic_guitar_006.jpg"],
+            ["testImages/analog_watch_024.jpg"],
+            ["testImages/analog_watch_025.jpg"]
+        ] if os.path.exists("testImages/acoustic_guitar_005.jpg") else None
+    )
+
+# Main execution
+if __name__ == "__main__":
+    # Determine if we're running on HF Spaces
+    is_huggingface = os.environ.get("SPACE_ID") is not None
+    
+    # Create and launch the interface
+    interface = create_interface(use_local=not is_huggingface)
+    interface.launch(share=True)

requirements.txt

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+gradio
+openvino
+opencv-python
+huggingface_hub
+numpy
+pillow
+ultralytics

upload_huggingface.py

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+import os
+from huggingface_hub import HfApi, create_repo
+
+model_path = r'C:\Manikandan\NYP\ITI107\Assignment_draft\output_models\best_openvino_model'
+model_name = 'mail2kandan/guitar_watch_openvino_model'
+
+# Initialize API
+api = HfApi()
+
+# Delete existing repository
+api.delete_repo(repo_id=model_name)
+
+# Create repository (replace with your Hugging Face username and model name)
+create_repo(model_name, exist_ok=True)
+
+# Upload files
+api.upload_file(
+    path_or_fileobj=os.path.join(model_path, 'best.bin'),
+    path_in_repo='best.bin',
+    repo_id=model_name,
+    commit_message="Upload best.bin"
+)
+api.upload_file(
+    path_or_fileobj=os.path.join(model_path, 'best.xml'),
+    path_in_repo='best.xml',
+    repo_id=model_name,
+    commit_message="Upload best.xml"
+)
+api.upload_file(
+    path_or_fileobj=os.path.join(model_path, 'metadata.yaml'),
+    path_in_repo='metadata.yaml',
+    repo_id=model_name,
+    commit_message="Upload metadata.yaml"
+)