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license: mit |
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# PlayerDirection SqueezeNet Model |
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## Overview |
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This model is trained for ice hockey player orientation detection, classifying cropped player images into one of eight orientations: Top, Top-Right, Right, Bottom-Right, Bottom, Bottom-Left, Left, and Top-Left. It is based on the SqueezeNet architecture and achieves an F1 score of **75%**. |
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## Model Details |
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- **Architecture**: SqueezeNet (modified for 8-class classification). |
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- **Training Configuration**: |
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- Learning rate: 1e-4 |
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- Batch size: 24 |
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- Epochs: 300 |
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- Weight decay: 1e-4 |
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- Dropout: 0.3 |
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- Early stopping: patience = 50 |
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- Augmentations: Color jitter (no rotation) |
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- **Performance**: |
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- Accuracy: ~75% |
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- F1 Score: ~75% |
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## Usage |
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1. Extract frames from a video using OpenCV. |
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2. Detect player bounding boxes with a YOLO model. |
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3. Crop player images, resize them to 224x224, and preprocess with the given PyTorch transformations: |
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- Resize to (224, 224) |
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- Normalize with mean=[0.485, 0.456, 0.406] and std=[0.229, 0.224, 0.225]. |
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4. Classify the direction of each cropped player image using the SqueezeNet model: |
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```python |
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with torch.no_grad(): |
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output = model(image_tensor) |
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direction_class = torch.argmax(output, dim=1).item() |
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