fastai provides a layered deep learning library that offers both high-level components for rapid model building and low-level components for custom research approaches. It supports building image classifiers, segmentation models, text sentiment analyzers, recommendation systems, and tabular models.
Project overview
The library addresses the tension between ease of use and flexibility by providing a layered architecture built on PyTorch, allowing practitioners to build models in approximately 5 lines of code while retaining low-level access for new research.
Project type
Data Processing · Image & Vision
Use cases
Coding & Development
Deployment
Refer to project documentation
License
Apache-2.0
Best for
Software engineers and researchers who need to build deep learning models across text, image, or tabular modalities.
Users who want to train models using their own datasets and require a library that operates within a Python environment.
Key capabilities
Build an image classifier using around 5 lines of code.
Build an image segmentation model using around 5 lines of code.
Build a text sentiment model using around 5 lines of code.
Build a recommendation system using around 5 lines of code.
Build a tabular model using around 5 lines of code.
Limitations and risks
PyTorch must be installed prior to installing fastai.
Minimum hardware requirements, GPU specifics, and supported operating systems are not documented in the provided facts.
Getting started
Setup is documented as easy due to a one-line pip install. The first success path requires installing PyTorch, followed by running pip install fastai.
Alternatives and comparisons
Provides a ready-to-use PyTorch implementation of Vision Transformer and numerous related architectures for vision classification tasks.
A Python library for augmenting images to generate larger sets of training data for machine learning models.
A platform for image, video, and 3D annotation to build visual datasets for computer vision.
README: You can use fastai without any installation by using [Google Colab](https://colab.research.google.com/).
README: You can install fastai on your own machines with: `pip install fastai`. To ensure that you have the best available version of PyTorch on your machine, recommend [installing](https…
README: fastai is a deep learning library which provides practitioners with high-level components that can quickly and easily provide state-of-the-art results in standard deep learning do…
README: which shows how to use around 5 lines of code to build an image classifier, an image segmentation model, a text sentiment model, a recommendation system, and a tabular model.