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Official Repository for "Mish: A Self Regularized Non-Monotonic Neural Activation Function" [BMVC 2020]
| Date | Stars |
|---|---|
| 2026-07-24 | 1300 |
| 2026-07-25 | 1300 |
| 2026-07-28 | 1300 |
| 2026-07-30 | 1300 |
| 2026-08-06 | 1300 |
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<h1 align="center">Mish: Self Regularized <br> Non-Monotonic Activation Function</h1>
<p align="center">
<a href="LICENSE" alt="License">
<img src="https://img.shields.io/badge/License-MIT-brightgreen.svg" /></a>
<a href="https://arxiv.org/abs/1908.08681v3" alt="ArXiv">
<img src="https://img.shields.io/badge/Paper-arXiv-blue.svg" /></a>
<a href="https://scholar.googleusercontent.com/scholar.bib?q=info:j0C1gbodjP4J:scholar.google.com/&output=citation&scisdr=CgX0hbDMEOzUo74J6TM:AAGBfm0AAAAAX1QM8TNcu4tND6FEofKsXzM3cs1uCAAW&scisig=AAGBfm0AAAAAX1QM8Y5elaJ1IW-BKOuU1zFTYNp-QaNQ&scisf=4&ct=citation&cd=-1&hl=en" alt="Cite">
<img src="https://img.shields.io/badge/Cite-BibTex-blue.svg" /></a>
<a href=" " alt="Citations">
<img src="https://img.shields.io/badge/Google Scholar-2989-lightgrey.svg" /></a>
<a href="https://www.bmvc2020-conference.com/conference/papers/paper_0928.html" alt="Publication">
<img src="https://img.shields.io/badge/BMVC-2020-red.svg" /></a>
<a href="https://console.paperspace.com/github/digantamisra98/Mish/blob/master/Layers_Acc.ipynb">
<img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Run on Gradient"/>
</a>
</p>
<p align="center">BMVC 2020 <a href="https://www.bmvc2020-conference.com/assets/papers/0928.pdf" target="_blank">(Official Paper)</a></p>
<br>
<br>
<details>
<summary>Notes: (Click to expand)</summary>
* A considerably faster version based on CUDA can be found here - [Mish CUDA](https://github.com/thomasbrandon/mish-cuda) (All credits to Thomas Brandon for the same)
* Memory Efficient Experimental version of Mish can be found [here](https://github.com/rwightman/gen-efficientnet-pytorch/blob/8795d3298d51ea5d993ab85a222dacffa8211f56/geffnet/activations/activations_autofn.py#L41)
* Faster variants for Mish and H-Mish by [Yashas Samaga](https://github.com/YashasSamaga) can be found here - [ConvolutionBuildingBlocks](https://github.com/YashasSamaga/ConvolutionBuildingBlocks)
* Alternative (experimental improved) variant of H-Mish developed by [Páll Haraldsson](https://github.com/PallHaraldsson) can be found here - [H-Mish](https://github.com/PallHaraldsson/H-Mish/blob/master/README.md) (Available in Julia)
* Variance based initialization method for Mish (experimental) by [Federico Andres Lois](https://twitter.com/federicolois) can be found here - [Mish_init](https://gist.github.com/redknightlois/b5d36fd2ae306cb8b3484c1e3bcce253)
</details>
<details>
<summary>Changelogs/ Updates: (Click to expand)</summary>
* [07/17] Mish added to [OpenVino](https://github.com/openvinotoolkit/openvino) - [Open-1187](https://github.com/openvinotoolkit/openvino/pull/1187), [Merged-1125](https://github.com/openvinotoolkit/openvino/pull/1125)
* [07/17] Mish added to [BetaML.jl](https://github.com/sylvaticus/BetaML.jl)
* [07/17] Loss Landscape exploration progress in collaboration with [Javier Ideami](https://ideami.com/ideami/) and [Ajay Uppili Arasanipalai](https://github.com/iyaja) <br>
* [07/17] Poster accepted for presentation at [DLRLSS](https://dlrlsummerschool.ca/) hosted by [MILA](https://mila.quebec/en/), [CIFAR](https://www.cifar.ca/), [Vector Institute](https://vectorinstitute.ai/) and [AMII](https://www.amii.ca/)
* [07/20] Mish added to [Google's AutoML](https://github.com/google/automl) - [502](https://github.com/google/automl/commit/28cf011689dacda90fe1ae6da59b92c0d3f2c9d9)
* [07/27] Mish paper accepted to [31st British Machine Vision Conference (BMVC), 2020](https://bmvc2020.github.io/index.html). ArXiv version to be updated soon.
* [08/13] New updated PyTorch benchmarks and pretrained models available on [PyTorch Benchmarks](https://github.com/digantamisra98/Mish/tree/master/PyTorch%20Benchmarks).
* [08/14] New updated [Arxiv](https://arxiv.org/abs/1908.08681v3) version of the paper is out.
* [08/18] Mish added to [Sony Nnabla](https://github.com/sony/nnablaExcerpt of 31,565 characters
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:bccece76189870f7, topic:computer-vision, topic:object-detection, topic:image-classification