# mae **Repository Path**: chenyang918/mae ## Basic Information - **Project Name**: mae - **Description**: PyTorch implementation of MAE https//arxiv.org/abs/2111.06377 - **Primary Language**: Unknown - **License**: Not specified - **Default Branch**: main - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 1 - **Created**: 2022-01-13 - **Last Updated**: 2024-10-15 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README ## Masked Autoencoders: A PyTorch Implementation

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):

### 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:
ViT-Base ViT-Large ViT-Huge
pre-trained checkpoint download download download
md5 8cad7c b8b06e 9bdbb0
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):
ViT-B ViT-L ViT-H ViT-H448 prev best
ImageNet-1K (no external data) 83.6 85.9 86.9 87.8 87.1
following are evaluation of the same model weights (fine-tuned in original ImageNet-1K):
ImageNet-Corruption (error rate) 51.7 41.8 33.8 36.8 42.5
ImageNet-Adversarial 35.9 57.1 68.2 76.7 35.8
ImageNet-Rendition 48.3 59.9 64.4 66.5 48.7
ImageNet-Sketch 34.5 45.3 49.6 50.9 36.0
following are transfer learning by fine-tuning the pre-trained MAE on the target dataset:
iNaturalists 2017 70.5 75.7 79.3 83.4 75.4
iNaturalists 2018 75.4 80.1 83.0 86.8 81.2
iNaturalists 2019 80.5 83.4 85.7 88.3 84.1
Places205 63.9 65.8 65.9 66.8 66.0
Places365 57.9 59.4 59.8 60.3 58.0
### 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.