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.
200Images
5Epochs
Data Phase
Collect and label your dataset. Use ImageClassifierCleaner to audit and remove mislabeled examples before training.
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.
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.
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
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.
