:INFO
Custom Classifier Training with fast.ai
fast.ai wraps PyTorch in a high-level API that lets you train an image or text classifier in hours rather than days. The library handles data loaders, augmentation, and training loops with sensible defaults.


:COUNTER.half
200 Images
|
:COUNTER.half
5 Epochs

:PATH
Data Phase
Collect and label your dataset. Use ImageClassifierCleaner to audit and remove mislabeled examples before training.

:PATH
Train Phase
Load a pretrained ResNet or ViT backbone. Fine-tune with one cycle training. Use the learning rate finder before committing to a rate.

:PATH
Deploy Phase
Export the model as a pickle file. Wrap with a Gradio or FastAPI interface. Deploy to Hugging Face Spaces or a simple VPS.

:CHECKLIST
Classifier Training Checklist
[ ] Define classes and success criteria
[ ] Collect and label training data
[ ] Split into train and validation sets
[ ] Train baseline model with default settings
[ ] Review confusion matrix and error cases
[ ] Clean mislabeled examples and retrain
[ ] Export model and test inference
[ ] Deploy behind an API endpoint

:NOTE
fast.ai fine-tunes only the final layers on the first pass, then unfreezes all layers for a second training run with a lower learning rate. This two-pass approach consistently outperforms single-pass fine-tuning on small datasets.


:LINK
https://docs.fast.ai
fast.ai documentation and practical deep learning course