README.md

Cow Breed Classifier (Roboflow -> Hugging Face Space)

What to upload to the Space

• dataset.zip : export zip from Roboflow (pick YOLOv5 PyTorch or Pascal VOC when downloading).
  • Place dataset.zip in the Space root (where app.py sits) before committing.

OR

• model.pth : If you already trained a model elsewhere, upload model.pth (the saved dict with keys model_state and classes) to skip training.

How it works

• On first start:
  • If model.pth exists: the app loads it and serves predictions.
  • Else if dataset.zip exists: app extracts it, converts detection labels -> classification folders, and trains a small ResNet18 model for a few epochs, then saves model.pth.
• Use the UI to upload images and see top-3 breed predictions.

Notes & tips

• Training on CPU (Hugging Face free Spaces) can be slow. Keep NUM_EPOCHS=3 (default) small. You can increase by setting an environment variable in the Space settings.
• If training fails due to PyTorch issues in the Space build, train locally (or on Colab) and upload model.pth.