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https://github.com/FluxML/model-zoo

Please do not feed the models
https://github.com/FluxML/model-zoo

deep-learning flux julia machine-learning model-zoo

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Please do not feed the models

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README 

        



# Flux Model Zoo



This repository contains various demonstrations of the [Flux](http://fluxml.github.io/) machine learning library. Any of these may freely be used as a starting point for your own models.



The models are broadly categorised into the folders [vision](/vision) (e.g. large convolutional neural networks (CNNs)), [text](/text) (e.g. various recurrent neural networks (RNNs) and natural language processing (NLP) models), [games](/contrib/games) (Reinforcement Learning / RL). See the READMEs of respective models for more information.



## Usage



Each model comes with its own [Julia project](https://pkgdocs.julialang.org/v1/environments/#Using-someone-else's-project). To use this, open Julia in the project folder, and enter



```julia

using Pkg; Pkg.activate("."); Pkg.instantiate()

```



This will install all needed packages, at the exact versions when the model was last updated. Then you can run the model code with `include(".jl")`, or by running the model script line-by-line.



Models may also be run with NVIDIA GPU support, if you have a CUDA installed. Most models will have this capability by default, pointed at by calls to `gpu` in the model code.



### Gitpod Online IDE



Each model can be used in [Gitpod](https://www.gitpod.io/), just [open the repository by gitpod](https://gitpod.io/#https://github.com/FluxML/model-zoo)



* Based on [Gitpod's policies](https://www.gitpod.io/pricing/), free access is limited.

* All of your work will place in the Gitpod's cloud.

* It isn't an officially maintained feature.



## Contributing



We welcome contributions of new models and documentation. 



### Share a new model



If you want to share a new model, we suggest you follow these guidelines:



* Models should be in a folder with a project and manifest file to pin all relevant packages.

* Models should include a README(.md) to explain what the model is about, how to run it, and what results it achieves (if applicable).

* Models should ideally be CPU/GPU agnostic and not depend directly on GPU functionality.

* Please keep the code short, clean, and self-explanatory, with as little boilerplate as possible.



### Create or improve documentation



You can contribute in one of the following ways 



* Add or improve documentation to existing models: Write the following information:

  * Give a brief introduction to the model’s architecture and the goal it archives.

  * Describe the Flux API that the model demonstrates (high-level API, AD, custom operations, custom layers, etc.).

  * Add literature background for the model. More specifically, add articles, blog posts, videos, and any other resource that is helpful to better understand the model.

  * Mention the technique that is being demonstrated. Briefly describe the learning technique being demonstrated (Computer vision, regression, NLP, time series, etc.).

* Write in-depth tutorials for a model: You can further extend the documentation of a model and create a tutorial to explain in more detail the architecture, the training routine, use your own data, and so forth. After you write a tutorial, create a PR with it for the [Tutorials](https://fluxml.ai/tutorials/) section on the [FluxML](https://fluxml.ai/) website.



### Update a model



Each example lists the version of Flux for which it was most recently updated.

Bringing them up to the latest is a great way to learn!

Flux has a [NEWS page](https://github.com/FluxML/Flux.jl/blob/master/NEWS.md) listing important changes.

(For other packages, see their releses page: [MLUtils](https://github.com/JuliaML/MLUtils.jl/releases), [MLDatasets](https://github.com/JuliaML/MLDatasets.jl/releases), etc.)



To run the old examples, Flux v0.11 can be installed and run on [Julia 1.6, the LTS version](https://julialang.org/downloads/#long_term_support_release).

Flux v0.12 works on Julia 1.8.

Flux v0.14 is the latest right now, this and v0.13 are marked with ☀️; models upgraded to use  

explicit gradients (v0.13.9+ or v0.14) have a `+`.



## Examples in the Model Zoo



**Vision**

* MNIST

  * [Simple multi-layer perceptron](vision/mlp_mnist) ☀️ v0.13 +

  * [Simple ConvNet (LeNet)](vision/conv_mnist) ☀️ v0.14 +

  * [Variational Auto-Encoder](vision/vae_mnist) ☀️ v0.13 +

  * [Deep Convolutional Generative Adversarial Networks](vision/dcgan_mnist) ☀️ v0.13 +

  * [Conditional Deep Convolutional Generative Adversarial Networks](vision/cdcgan_mnist) ☀️ v0.13

  * [Score-Based Generative Modeling (Diffusion Model)](vision/diffusion_mnist) ☀️ v0.13

  * [Spatial Transformer](vision/spatial_transformer) ☀️ v0.13 +

* CIFAR10

  * [VGG 16/19](vision/vgg_cifar10) ☀️ v0.13 +

  * [ConvMixer "Patches are all you need?"](vision/convmixer_cifar10/) ☀️ v0.13



**Text**

* [CharRNN](text/char-rnn) ☀️ v0.13 +

* [Character-level language detection](text/lang-detection) ☀️ v0.13 +

* [Seq2Seq phoneme detection on CMUDict](text/phonemes) ⛅️ v0.11

* [Recursive net on IMDB sentiment treebank](text/treebank) ⛅️ v0.11



**Other** & contributed models

* [Logistic Regression Iris](other/iris/iris.jl) ☀️ v0.13 +

* [Autoregressive Model](other/autoregressive-process/) ☀️ v0.13 +

* [BitString Parity Challenge](other/bitstring-parity) ⛅️ v0.11

* [MLP on housing data](other/housing/) (low level API) ⛅️ v0.11

* [FizzBuzz](other/fizzbuzz/fizzbuzz.jl) ☀️ v0.13 +

* [Meta-Learning](contrib/meta-learning/MetaLearning.jl) ❄️ v0.7

* [Speech recognition](contrib/audio/speech-blstm) ❄️ v0.6



**Tutorials**

* [A 60 Minute Blitz](tutorials/60-minute-blitz/60-minute-blitz.jl) ⛅️ v0.11

* [DataLoader example with image data](tutorials/dataloader) ⛅️ v0.11

* [Transfer Learning](tutorials/transfer_learning/transfer_learning.jl) ☀️ v0.13 +



## Examples Elsewhere



**MLJFlux** is a bridge to [MLJ.jl](https://github.com/alan-turing-institute/MLJ.jl), a package for mostly non-neural-network machine learning. They have some examples of interest, which like the model zoo's examples, each include a local Project & Manifest file:



* [Iris](https://github.com/FluxML/MLJFlux.jl/tree/dev/examples/iris) ⛅️ v0.11

* [Boston](https://github.com/FluxML/MLJFlux.jl/tree/dev/examples/boston) ⛅️ v0.11

* [MNIST](https://github.com/FluxML/MLJFlux.jl/tree/dev/examples/mnist) ⛅️ v0.11