---
license: mit
---

# PlayerDirection SqueezeNet Model

## Overview
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%**.

## 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()