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https://github.com/tmabraham/awesome-fastai

A curated list of awesome fastai projects/blog posts/tutorials/etc.
https://github.com/tmabraham/awesome-fastai

List: awesome-fastai

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A curated list of awesome fastai projects/blog posts/tutorials/etc.

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README 

        
# Awesome fastai [![Awesome](https://awesome.re/badge.svg)](https://awesome.re)

[](https://docs.fast.ai)

> A curated list of awesome projects and resources related [fastai](https://docs.fast.ai)



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)

- [Github Repositories](#github)

- [Kaggle Notebooks](#kaggle)

- [Articles](#articles)

- [Community](#community)

- [Other links](#other)



## Libraries



Libraries built with fastai.



### Computer Vision

- [UPIT](https://github.com/tmabraham/UPIT) - a fastai/PyTorch package for unpaired image-to-image translation

- [faststyle](https://github.com/lgvaz/faststyle) - aims to provide an easy and modular interface for image to image problems based on feature loss

- [IceVision](https://github.com/airctic/icevision) - an agnostic computer vision framework, pluggable to any training library 

- [SemTorch](https://github.com/WaterKnight1998/SemTorch) - brings state-of-the-art segmentation architectures into fastai



### Natural Language Processing

- [blurr](https://github.com/ohmeow/blurr/) - a library that integrates HuggingFace Transformers with version 2 of the fastai framework

- [fasthugs](https://github.com/morganmcg1/fasthugs) - use fastai-v2 with HuggingFace's pretrained transformers

- [AdaptNLP](https://github.com/Novetta/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](https://github.com/floleuerer/fastai_ulmfit) - a library for pretraining, fine-tuning and training a ULM-FiT classifier

- 

### Medical Imaging

- [fmi](https://github.com/asvcode/fmi) - additional functionality for use with fastai’s medical imaging module

- [faimed3d](https://github.com/kbressem/faimed3d) - extension to fastai for volumetric medical data



### Fastai extensions

- [fastinference](https://github.com/muellerzr/fastinference) - a collection of inference modules for fastai2

- [fastai_minima](https://github.com/muellerzr/fastai_minima) - minimal fastai code needed for working with pytorch

- [walkwithfastai](https://github.com/walkwithfastai/walkwithfastai.github.io) - fastai extensions and techniques



### Other libraries

- [fastaudio](https://github.com/fastaudio/fastaudio) - an audio module for fastai v2

- [tsai](https://github.com/timeseriesAI/tsai) - state-of-the-art deep learning library for time series and sequences.

- [FasterAI](https://github.com/nathanhubens/fasterai) - a library to make smaller and faster models with fastai

- [self_supervised](https://github.com/KeremTurgutlu/self_supervised) - implementation of popular SOTA self-supervised learning algorithms as fastai callbacks



## Github Repositories



Other Github repositories with useful fastai-related content.



- [fastbook](https://github.com/fastai/fastbook) - the Jupyter notebooks for the current edition of fastai book and course.

- [imagenette](https://github.com/fastai/imagenette) - a smaller subset of 10 easily classified classes from Imagenet, useful for quick prototyping.

- [FastAI.jl](https://github.com/FluxML/FastAI.jl) - a version of the fastai libary for the Julia language

- [EagerAI/fastai](https://github.com/EagerAI/fastai) - an R interface for fastai

- [Over9000](https://github.com/mgrankin/over9000) - different SOTA optimizers and fastai training script

- [fast_tabnet](https://github.com/mgrankin/fast_tabnet) - TabNet (SOTA neural network for tabular data) for fastai

- [ManifoldMixupV2](https://github.com/nestordemeure/ManifoldMixupV2) - Manifold-Mixup implementation for fastai v2

- [fastai-batch-size-finder](https://github.com/hal-314/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. 



- [Cassava classification - fastai starter](https://www.kaggle.com/tanlikesmath/cassava-classification-eda-fastai-starter)

- [Petfinder Pawpularity - fastai starter](https://www.kaggle.com/tanlikesmath/petfinder-pawpularity-eda-fastai-starter)

- [SETI ET Signal Detection - fastai starter](https://www.kaggle.com/tanlikesmath/seti-et-signal-detection-a-simple-cnn-starter)

- [Combined fastai tabular+vision notebook](https://www.kaggle.com/muellerzr/fastai2-tabular-vision-starter-kernel)

- [HuBMAP PyTorch/fast.ai starter](https://www.kaggle.com/iafoss/hubmap-pytorch-fast-ai-starter)

- [fastai v2 Pipeline tutorial](https://www.kaggle.com/jhoward/fastai-v2-pipeline-tutorial)

- fastai medical imaging tutorials by Jeremy Howard:

  - [Creating a metadata DataFrame](https://www.kaggle.com/jhoward/creating-a-metadata-dataframe-fastai)

  - [Some DICOM gotchas to be aware of](https://www.kaggle.com/jhoward/some-dicom-gotchas-to-be-aware-of-fastai)

  - [DON'T see like a radiologist!](https://www.kaggle.com/jhoward/don-t-see-like-a-radiologist-fastai)

  - [Cleaning the data for rapid prototyping](https://www.kaggle.com/jhoward/cleaning-the-data-for-rapid-prototyping-fastai)

  - [From prototyping to submission](https://www.kaggle.com/jhoward/from-prototyping-to-submission-fastai)



## Articles



Some of the best articles regarding fastai and deep learning.



- [fastai: A Layered API for Deep Learning](https://arxiv.org/abs/2002.04688)

- [Pytorch to fastai, Bridging the Gap by Zach Mueller](https://muellerzr.github.io/fastblog/2021/02/14/Pytorchtofastai.html)

- [fastai and the New DataBlock API by Zach Mueller](https://muellerzr.github.io/fastblog/datablock/2020/03/21/DataBlockAPI.html)

- [The Idea of a Transform by Zach Mueller](https://muellerzr.github.io/fastblog/datablock/2020/03/22/TransformFunctions.html)

- [Keypoint regression with heatmaps in fastai v2 by Elte Hupkes](https://elte.me/2021-03-10-keypoint-regression-fastai)

- [fastai models to torch.jit by Habib Bukhari](https://drhb.github.io/blog/fastai/2020/03/22/Fastai-Jit.html)

- [Speeding Up fastai2 Inference - And A Few Things Learned by Zach Mueller](https://forums.fast.ai/t/speeding-up-fastai2-inference-and-a-few-things-learned/66179)



## Community



Online communities to discuss fastai.



- [fast.ai forums](https://forums.fast.ai) - 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](https://discord.gg/XnWJpQYgFn) - 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](https://nbdev.fast.ai) - Create delightful python projects using Jupyter Notebooks. fastai v2 is built with nbdev.

- [nbverbose](https://github.com/muellerzr/nbverbose) - An inplace extension on fastai's nbdev library to support documenting inputs

- [nbagile](https://github.com/muellerzr/nbagile) - making nbdev compatible for agile frameworks and developments

- [fastpages](https://fastpages.fast.ai) - 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](https://course.fast.ai/) - the fastai deep learning course.

- [fastaidocs](https://www.cognitivefactory.fr/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.