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README.md

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+# CMT.pytorch
+
+
+## Implementation of [CMT: Convolutional Neural Networks Meet Vision Transformers](https://arxiv.org/pdf/2107.06263.pdf)
+
+
+#### Set up
+```
+- python==3.6
+- cuda==10.0
+
+# other pytorch/timm version can also work
+
+pip install torch==1.7.0 torchvision==0.8.1;
+pip install timm==0.3.2;
+pip install torchprofile;
+
+# build apex
+
+cd /your_path_to/apex-master/;
+pip install -v --disable-pip-version-check --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" ./
+```
+
+### Data preparation
+
+Download and extract ImageNet train and val images from http://image-net.org/.
+The directory structure is:
+
+```
+│path/to/imagenet/
+├──train/
+│  ├── n01440764
+│  │   ├── n01440764_10026.JPEG
+│  │   ├── n01440764_10027.JPEG
+│  │   ├── ......
+│  ├── ......
+├──val/
+│  ├── n01440764
+│  │   ├── ILSVRC2012_val_00000293.JPEG
+│  │   ├── ILSVRC2012_val_00002138.JPEG
+│  │   ├── ......
+│  ├── ......
+```
+
+#### Training
+
+To train CMT-Tiny on ImageNet-1K on a single node with 8 gpus:
+
+```
+python -m torch.distributed.launch --nproc_per_node=8 train.py --data-path /your_path_to/imagenet/ --output_dir /your_path_to/output/ --model cmt_ti --batch-size 256 --apex-amp --input-size 160 --weight-decay 0.05 --drop-path 0.1 --epochs 800 --test_freq 100 --test_epoch 760 --warmup-lr 1e-7 --warmup-epochs 20 --lr 8e-4 --min-lr 1e-5 --no-model-ema
+```
+
+To train CMT-XS on ImageNet-1K on a single node with 8 gpus:
+```
+python -m torch.distributed.launch --nproc_per_node=8 train.py --data-path /your_path_to/imagenet/ --output_dir /your_path_to/output/ --model cmt_xs --batch-size 256 --apex-amp --input-size 192 --weight-decay 0.04 --drop-path 0.08 --epochs 400 --test_freq 100 --test_epoch 360 --warmup-lr 1e-6 --warmup-epochs 20 --lr 7e-4 --min-lr 2e-5 --model-ema-decay 0.9998
+```
+
+To train CMT-Small on ImageNet-1K on a single node with 8 gpus:
+
+```
+python -m torch.distributed.launch --nproc_per_node=8 train.py --data-path /your_path_to/imagenet/ --output_dir /your_path_to/output/ --model cmt_s --batch-size 128 --apex-amp --input-size 224 --weight-decay 0.05 --drop-path 0.1 --epochs 300 --test_freq 100 --test_epoch 260 --warmup-lr 1e-7 --warmup-epochs 20
+```
+
+To train CMT-Base on ImageNet-1K on a single node with 8 gpus:
+
+```
+python -m torch.distributed.launch --nproc_per_node=8 train.py --data-path /your_path_to/imagenet/ --output_dir /your_path_to/output/ --model cmt_b --batch-size 64 --apex-amp --input-size 256 --weight-decay 0.05 --drop-path 0.25 --epochs 300 --test_freq 100 --test_epoch 260 --warmup-lr 1e-6 --min-lr 2e-5 --warmup-epochs 20
+```
+
+## Acknowledgement
+This repo is based on [DeiT](https://github.com/facebookresearch/deit) and [pytorch-image-models](https://github.com/rwightman/pytorch-image-models).
+
+
+## Citation
+
+If you find this project useful in your research, please consider cite:
+
+```bibtex
+@article{guo2021cmt,
+  title={Cmt: Convolutional neural networks meet vision transformers},
+  author={Guo, Jianyuan and Han, Kai and Wu, Han and Xu, Chang and Tang, Yehui and Xu, Chunjing and Wang, Yunhe},
+  journal={arXiv preprint arXiv:2107.06263},
+  year={2021}
+}
+```
+
+## License
+
+[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)