A curated list of awesome projects and resources related fastai

Overview

Awesome fastai Awesome

A curated list of awesome projects and resources related fastai

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 domains, and provides researchers with low-level components that can be mixed and matched to build new approaches.

Note: Only resources that are related to fastai v2 are presented here.


Contents

Libraries

Libraries built with fastai.

Computer Vision

  • UPIT - a fastai/PyTorch package for unpaired image-to-image translation
  • faststyle - aims to provide an easy and modular interface for image to image problems based on feature loss
  • IceVision - an agnostic computer vision framework, pluggable to any training library
  • SemTorch - brings state-of-the-art segmentation architectures into fastai

Natural Language Processing

  • blurr - a library that integrates HuggingFace Transformers with version 2 of the fastai framework
  • fasthugs - use fastai-v2 with HuggingFace's pretrained transformers
  • AdaptNLP - a high-level framework and library for running, training, and deploying state-of-the-art NLP models for end-to-end tasks
  • fastai_ulmfit - a library for pretraining, fine-tuning and training a ULM-FiT classifier

Medical Imaging

  • fmi - additional functionality for use with fastai’s medical imaging module
  • faimed3d - extension to fastai for volumetric medical data

Fastai extensions

Other libraries

  • fastaudio - an audio module for fastai v2
  • tsai - state-of-the-art deep learning library for time series and sequences.
  • FasterAI - a library to make smaller and faster models with fastai
  • self_supervised - implementation of popular SOTA self-supervised learning algorithms as fastai callbacks

Github Repositories

Other Github repositories with useful fastai-related content.

  • fastbook - the Jupyter notebooks for the current edition of fastai book and course.
  • imagenette - a smaller subset of 10 easily classified classes from Imagenet, useful for quick prototyping.
  • FastAI.jl - a version of the fastai libary for the Julia language
  • EagerAI/fastai - an R interface for fastai
  • Over9000 - different SOTA optimizers and fastai training script
  • fast_tabnet - TabNet (SOTA neural network for tabular data) for fastai
  • ManifoldMixupV2 - Manifold-Mixup implementation for fastai v2
  • fastai-batch-size-finder - Implementation of OpenAI paper "An Empirical Model of Large-Batch Training" for fastai v2.

Kaggle Notebooks

Kaggle Notebooks that use fastai. Feel free to fork them and interactively try them out.

Articles

Some of the best articles regarding fastai and deep learning.

Community

Online communities to discuss fastai.

  • fast.ai forums - the OFFICIAL fast.ai forums. Any fastai questions should ideally be asked here. Also check if your questions have already been addressed here by a previous post using the search functionality. Finally, feel free to share your work and projects here.
  • fast.ai Discord server - the OFFICIAL fast.ai Discord server. Useful for more informal discussion of fastai-related content, setting up and participating in study groups, and fastai library development.

Other links

Other relevant links.

  • nbdev - Create delightful python projects using Jupyter Notebooks. fastai v2 is built with nbdev.
  • nbverbose - An inplace extension on fastai's nbdev library to support documenting inputs
  • nbagile - making nbdev compatible for agile frameworks and developments
  • fastpages - An easy to use blogging platform with extra features for Jupyter Notebooks. A great blogging platform for you to share your fastai journey!
  • fast.ai course - the fastai deep learning course.
  • fastaidocs - a great set of cheatsheets for the fastai v2 API.

Contribute

Contributions are welcome! Please submit a pull request with a single contribution only.

Owner
Tanishq Abraham
Tanishq Abraham
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Simple STAC Catalogs discovery tool.

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