fastai provides a deep learning library with high-level components for state-of-the-art results in standard domains and low-level components for building new approaches. It organizes around approachable, rapidly productive APIs that remain deeply hackable and configurable. The library supports vision, text, tabular, time-series analysis, and collaborative filtering through concise APIs with intelligent defaults, such as automatic transfer learning, layer freezing, and discriminative learning rates. A Learner class integrates architecture, optimizer, and data, selecting appropriate loss functions. The data block API handles getting source items, splitting sets, labeling, processing, and batching. Consistency across domains aids model development in varied applications.
Enables state-of-the-art vision model training with four lines of code
Allows implementing research papers in hours while matching paper performance
Provides clarity and development speed comparable to Keras with PyTorch customizability
Supports consistency across vision, text, tabular, and collaborative filtering domains
Requires PyTorch as underlying framework, adding dependency
Intelligent defaults may obscure low-level control for advanced customization
GPU batch transforms demand uniform image sizes after item-level processing
