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# Train a Custom Classifier with fast.ai

- [Made in Slatesource](https://slatesource.com/u/kairenner/train-a-custom-classifier-with-fast-ai-918)
- By [KaiRenner](https://slatesource.com/u/KaiRenner)
- Science & Technology
- Created on Mar 21, 2026

## 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.

**200** Images

**5** Epochs

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

0%

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.

[fast.ai documentation and practical deep learning course](https://docs.fast.ai/?utm_source=slatesource)