Update README.md

README.md

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----
-title: Classifier
-emoji: 📉
-colorFrom: pink
-colorTo: gray
-sdk: gradio
-sdk_version: 5.46.1
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# 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`.