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