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https://github.com/facebookresearch/LeViT
LeViT a Vision Transformer in ConvNet's Clothing for Faster Inference
https://github.com/facebookresearch/LeViT
Last synced: 7 days ago
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LeViT a Vision Transformer in ConvNet's Clothing for Faster Inference
- Host: GitHub
- URL: https://github.com/facebookresearch/LeViT
- Owner: facebookresearch
- License: apache-2.0
- Archived: true
- Created: 2021-03-30T09:47:44.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2022-08-27T21:44:31.000Z (about 2 years ago)
- Last Synced: 2024-08-02T15:36:48.345Z (3 months ago)
- Language: Python
- Size: 271 KB
- Stars: 595
- Watchers: 12
- Forks: 71
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Contributing: .github/CONTRIBUTING.md
- License: LICENSE
- Code of conduct: .github/CODE_OF_CONDUCT.md
Awesome Lists containing this project
README
# LeViT: a Vision Transformer in ConvNet's Clothing for Faster Inference
This repository contains PyTorch evaluation code, training code and pretrained models for LeViT.
They obtain competitive tradeoffs in terms of speed / precision:
For details see [LeViT: a Vision Transformer in ConvNet's Clothing for Faster Inference](https://openaccess.thecvf.com/content/ICCV2021/html/Graham_LeViT_A_Vision_Transformer_in_ConvNets_Clothing_for_Faster_Inference_ICCV_2021_paper.html) by Benjamin Graham, Alaaeldin El-Nouby, Hugo Touvron, Pierre Stock, Armand Joulin, Hervé Jégou and Matthijs Douze.
If you use this code for a paper please cite:
```
@InProceedings{Graham_2021_ICCV,
author = {Graham, Benjamin and El-Nouby, Alaaeldin and Touvron, Hugo and Stock, Pierre and Joulin, Armand and Jegou, Herve and Douze, Matthijs},
title = {LeViT: A Vision Transformer in ConvNet's Clothing for Faster Inference},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2021},
pages = {12259-12269}
}
```
# Model Zoo
We provide baseline LeViT models trained with distllation on ImageNet 2012.
| name | acc@1 | acc@5 | #FLOPs | #params | url |
| ---------- | ----- | ----- | ------ | ------- | --------------------------------------------------------------------- |
| LeViT-128S | 76.6 | 92.9 | 305M | 7.8M | [model](https://dl.fbaipublicfiles.com/LeViT/LeViT-128S-96703c44.pth) |
| LeViT-128 | 78.6 | 94.0 | 406M | 9.2M | [model](https://dl.fbaipublicfiles.com/LeViT/LeViT-128-b88c2750.pth) |
| LeViT-192 | 80.0 | 94.7 | 658M | 11M | [model](https://dl.fbaipublicfiles.com/LeViT/LeViT-192-92712e41.pth) |
| LeViT-256 | 81.6 | 95.4 | 1120M | 19M | [model](https://dl.fbaipublicfiles.com/LeViT/LeViT-256-13b5763e.pth) |
| LeViT-384 | 82.6 | 96.0 | 2353M | 39M | [model](https://dl.fbaipublicfiles.com/LeViT/LeViT-384-9bdaf2e2.pth) |
# Usage
First, clone the repository locally:
```
git clone https://github.com/facebookresearch/levit.git
```
Then, install PyTorch 1.7.0+ and torchvision 0.8.1+ and [pytorch-image-models](https://github.com/rwightman/pytorch-image-models):
```
conda install -c pytorch pytorch torchvision
pip install timm
```
## Data preparation
Download and extract ImageNet train and val images from http://image-net.org/.
The directory structure is the standard layout for the torchvision [`datasets.ImageFolder`](https://pytorch.org/docs/stable/torchvision/datasets.html#imagefolder), and the training and validation data is expected to be in the `train/` folder and `val` folder respectively:
```
/path/to/imagenet/
train/
class1/
img1.jpeg
class2/
img2.jpeg
val/
class1/
img3.jpeg
class/2
img4.jpeg
```
## Evaluation
To evaluate a pre-trained LeViT-256 model on ImageNet val with a single GPU run:
```
python main.py --eval --model LeViT_256 --data-path /path/to/imagenet
```
This should give
```
* Acc@1 81.636 Acc@5 95.424 loss 0.750
```
## Training
To train LeViT-256 on ImageNet with hard distillation on a single node with 8 gpus run:
```
python -m torch.distributed.launch --nproc_per_node=8 --use_env main.py --model LeViT_256 --data-path /path/to/imagenet --output_dir /path/to/save
```
### Multinode training
Distributed training is available via Slurm and [submitit](https://github.com/facebookincubator/submitit):
```
pip install submitit
```
To train LeViT-256 model on ImageNet on one node with 8 gpus:
```
python run_with_submitit.py --model LeViT_256 --data-path /path/to/imagenet
```
# License
This repository is released under the Apache 2.0 license as found in the [LICENSE](LICENSE) file.
# Contributing
We actively welcome your pull requests! Please see [CONTRIBUTING.md](.github/CONTRIBUTING.md) and [CODE_OF_CONDUCT.md](.github/CODE_OF_CONDUCT.md) for more info.