base on PyTorch implementation of MAE https//arxiv.org/abs/2111.06377 ## Masked Autoencoders: A PyTorch Implementation <p align="center"> <img src="https://user-images.githubusercontent.com/11435359/146857310-f258c86c-fde6-48e8-9cee-badd2b21bd2c.png" width="480"> </p> This is a PyTorch/GPU re-implementation of the paper [Masked Autoencoders Are Scalable Vision Learners](https://arxiv.org/abs/2111.06377): ``` @Article{MaskedAutoencoders2021, author = {Kaiming He and Xinlei Chen and Saining Xie and Yanghao Li and Piotr Doll{\'a}r and Ross Girshick}, journal = {arXiv:2111.06377}, title = {Masked Autoencoders Are Scalable Vision Learners}, year = {2021}, } ``` * The original implementation was in TensorFlow+TPU. This re-implementation is in PyTorch+GPU. * This repo is a modification on the [DeiT repo](https://github.com/facebookresearch/deit). Installation and preparation follow that repo. * This repo is based on [`timm==0.3.2`](https://github.com/rwightman/pytorch-image-models), for which a [fix](https://github.com/rwightman/pytorch-image-models/issues/420#issuecomment-776459842) is needed to work with PyTorch 1.8.1+. ### Catalog - [x] Visualization demo - [x] Pre-trained checkpoints + fine-tuning code - [x] Pre-training code ### Visualization demo Run our interactive visualization demo using [Colab notebook](https://colab.research.google.com/github/facebookresearch/mae/blob/main/demo/mae_visualize.ipynb) (no GPU needed): <p align="center"> <img src="https://user-images.githubusercontent.com/11435359/147859292-77341c70-2ed8-4703-b153-f505dcb6f2f8.png" width="600"> </p> ### Fine-tuning with pre-trained checkpoints The following table provides the pre-trained checkpoints used in the paper, converted from TF/TPU to PT/GPU: <table><tbody> <!-- START TABLE --> <!-- TABLE HEADER --> <th valign="bottom"></th> <th valign="bottom">ViT-Base</th> <th valign="bottom">ViT-Large</th> <th valign="bottom">ViT-Huge</th> <!-- TABLE BODY --> <tr><td align="left">pre-trained checkpoint</td> <td align="center"><a href="https://dl.fbaipublicfiles.com/mae/pretrain/mae_pretrain_vit_base.pth">download</a></td> <td align="center"><a href="https://dl.fbaipublicfiles.com/mae/pretrain/mae_pretrain_vit_large.pth">download</a></td> <td align="center"><a href="https://dl.fbaipublicfiles.com/mae/pretrain/mae_pretrain_vit_huge.pth">download</a></td> </tr> <tr><td align="left">md5</td> <td align="center"><tt>8cad7c</tt></td> <td align="center"><tt>b8b06e</tt></td> <td align="center"><tt>9bdbb0</tt></td> </tr> </tbody></table> The fine-tuning instruction is in [FINETUNE.md](FINETUNE.md). By fine-tuning these pre-trained models, we rank #1 in these classification tasks (detailed in the paper): <table><tbody> <!-- START TABLE --> <!-- TABLE HEADER --> <th valign="bottom"></th> <th valign="bottom">ViT-B</th> <th valign="bottom">ViT-L</th> <th valign="bottom">ViT-H</th> <th valign="bottom">ViT-H₄₄₈</th> <td valign="bottom" style="color:#C0C0C0">prev best</td> <!-- TABLE BODY --> <tr><td align="left">ImageNet-1K (no external data)</td> <td align="center">83.6</td> <td align="center">85.9</td> <td align="center">86.9</td> <td align="center"><b>87.8</b></td> <td align="center" style="color:#C0C0C0">87.1</td> </tr> <td colspan="5"><font size="1"><em>following are evaluation of the same model weights (fine-tuned in original ImageNet-1K):</em></font></td> <tr> </tr> <tr><td align="left">ImageNet-Corruption (error rate) </td> <td align="center">51.7</td> <td align="center">41.8</td> <td align="center"><b>33.8</b></td> <td align="center">36.8</td> <td align="center" style="color:#C0C0C0">42.5</td> </tr> <tr><td align="left">ImageNet-Adversarial</td> <td align="center">35.9</td> <td align="center">57.1</td> <td align="center">68.2</td> <td align="center"><b>76.7</b></td> <td align="center" style="color:#C0C0C0">35.8</td> </tr> <tr><td align="left">ImageNet-Rendition</td> <td align="center">48.3</td> <td align="center">59.9</td> <td align="center">64.4</td> <td align="center"><b>66.5</b></td> <td align="center" style="color:#C0C0C0">48.7</td> </tr> <tr><td align="left">ImageNet-Sketch</td> <td align="center">34.5</td> <td align="center">45.3</td> <td align="center">49.6</td> <td align="center"><b>50.9</b></td> <td align="center" style="color:#C0C0C0">36.0</td> </tr> <td colspan="5"><font size="1"><em>following are transfer learning by fine-tuning the pre-trained MAE on the target dataset:</em></font></td> </tr> <tr><td align="left">iNaturalists 2017</td> <td align="center">70.5</td> <td align="center">75.7</td> <td align="center">79.3</td> <td align="center"><b>83.4</b></td> <td align="center" style="color:#C0C0C0">75.4</td> </tr> <tr><td align="left">iNaturalists 2018</td> <td align="center">75.4</td> <td align="center">80.1</td> <td align="center">83.0</td> <td align="center"><b>86.8</b></td> <td align="center" style="color:#C0C0C0">81.2</td> </tr> <tr><td align="left">iNaturalists 2019</td> <td align="center">80.5</td> <td align="center">83.4</td> <td align="center">85.7</td> <td align="center"><b>88.3</b></td> <td align="center" style="color:#C0C0C0">84.1</td> </tr> <tr><td align="left">Places205</td> <td align="center">63.9</td> <td align="center">65.8</td> <td align="center">65.9</td> <td align="center"><b>66.8</b></td> <td align="center" style="color:#C0C0C0">66.0</td> </tr> <tr><td align="left">Places365</td> <td align="center">57.9</td> <td align="center">59.4</td> <td align="center">59.8</td> <td align="center"><b>60.3</b></td> <td align="center" style="color:#C0C0C0">58.0</td> </tr> </tbody></table> ### Pre-training The pre-training instruction is in [PRETRAIN.md](PRETRAIN.md). ### License This project is under the CC-BY-NC 4.0 license. See [LICENSE](LICENSE) for details. ", Assign "at most 3 tags" to the expected json: {"id":"3737","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"