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CoaT: Co-Scale Conv-Attentional Image Transformers
Introduction
This repository contains the official code and pretrained models for CoaT: Co-Scale Conv-Attentional Image Transformers. It introduces (1) a co-scale mechanism to realize fine-to-coarse, coarse-to-fine and cross-scale attention modeling and (2) an efficient conv-attention module to realize relative position encoding in the factorized attention.
<img src="./figures/model-acc.svg" alt="Model Accuracy" width="600" />For more details, please refer to CoaT: Co-Scale Conv-Attentional Image Transformers by Weijian Xu*, Yifan Xu*, Tyler Chang, and Zhuowen Tu.
Performance
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Classification (ImageNet dataset)
Name Acc@1 Acc@5 #Params CoaT-Lite Tiny 77.5 93.8 5.7M CoaT-Lite Mini 79.1 94.5 11M CoaT-Lite Small 81.9 95.5 20M CoaT-Lite Medium 83.6 96.7 45M CoaT Tiny 78.3 94.0 5.5M CoaT Mini 81.0 95.2 10M CoaT Small 82.1 96.1 22M -
Instance Segmentation (Mask R-CNN w/ FPN on COCO dataset)
Name Schedule Bbox AP Segm AP CoaT-Lite Mini 1x 41.4 38.0 CoaT-Lite Mini 3x 42.9 38.9 CoaT-Lite Small 1x 45.2 40.7 CoaT-Lite Small 3x 45.7 41.1 CoaT Mini 1x 45.1 40.6 CoaT Mini 3x 46.5 41.8 CoaT Small 1x 46.5 41.8 CoaT Small 3x 49.0 43.7 -
Object Detection (Deformable-DETR on COCO dataset)
Name AP AP50 AP75 APS APM APL CoaT-Lite Small 47.0 66.5 51.2 28.8 50.3 63.3 CoaT Small 48.4 68.5 52.4 30.1 51.8 63.8
Changelog
12/12/2021: Code and pre-trained checkpoints for Deformable-DETR with CoaT Small backbone are released. <br /> 12/07/2021: Training commands for CoaT-Lite Medium (384x384) are released. <br /> 12/06/2021: Pre-trained checkpoints for CoaT-Lite Medium (384x384) are released. <br /> 12/05/2021: Training scripts for CoaT Small and CoaT-Lite Medium are released. <br /> 09/27/2021: Code and pre-trained checkpoints for instance segmentation with MMDetection are released. <br /> 08/27/2021: Pre-trained checkpoints for CoaT Small and CoaT-Lite Medium are released. <br /> 05/19/2021: Pre-trained checkpoints for Mask R-CNN benchmark with CoaT-Lite Small backbone are released. <br /> 05/19/2021: Code and pre-trained checkpoints for Deformable-DETR with CoaT-Lite Small backbone are released. <br /> 05/11/2021: Pre-trained checkpoints for CoaT-Lite Small are released. <br /> 05/09/2021: Pre-trained checkpoints for Mask R-CNN benchmark with CoaT Mini backbone are released. <br /> 05/06/2021: Pre-trained checkpoints for CoaT Mini are released. <br /> 05/02/2021: Pre-trained checkpoints for CoaT Tiny are released. <br /> 04/25/2021: Code and pre-trained checkpoints for Mask R-CNN benchmark with CoaT-Lite Mini backbone are released. <br /> 04/23/2021: Pre-trained checkpoints for CoaT-Lite Mini are released. <br /> 04/22/2021: Code and pre-trained checkpoints for CoaT-Lite Tiny are released.
Usage
The following usage is provided for the classification task using CoaT model. For the other tasks, please follow the corresponding readme, such as instance segmentation and object detection.
Environment Preparation
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Set up a new conda environment and activate it.
# Create an environment with Python 3.8. conda create -n coat python==3.8 conda activate coat
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Install required packages.
# Install PyTorch 1.7.1 w/ CUDA 11.0. pip install torch==1.7.1+cu110 torchvision==0.8.2+cu110 torchaudio==0.7.2 -f https://download.pytorch.org/whl/torch_stable.html # Install timm 0.3.2. pip install timm==0.3.2 # Install einops. pip install einops
Code and Dataset Preparation
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Clone the repo.
git clone https://github.com/mlpc-ucsd/CoaT cd CoaT
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Download ImageNet dataset (ILSVRC 2012) and extract.
# Create dataset folder. mkdir -p ./data/ImageNet # Download the dataset (not shown here) and copy the files (assume the download path is in $DATASET_PATH). cp $DATASET_PATH/ILSVRC2012_img_train.tar $DATASET_PATH/ILSVRC2012_img_val.tar $DATASET_PATH/ILSVRC2012_devkit_t12.tar.gz ./data/ImageNet # Extract the dataset. python -c "from torchvision.datasets import ImageNet; ImageNet('./data/ImageNet', split='train')" python -c "from torchvision.datasets import ImageNet; ImageNet('./data/ImageNet', split='val')" # After the extraction, you should observe `train` and `val` folders under ./data/ImageNet.
Evaluate Pre-trained Checkpoint
We provide the CoaT checkpoints pre-trained on the ImageNet dataset.
Name | Acc@1 | Acc@5 | #Params | SHA-256 (first 8 chars) | URL |
---|---|---|---|---|---|
CoaT-Lite Tiny | 77.5 | 93.8 | 5.7M | e88e96b0 | model, log |
CoaT-Lite Mini | 79.1 | 94.5 | 11M | 6b4a8ae5 | model, log |
CoaT-Lite Small | 81.9 | 95.5 | 20M | 8d362f48 | model, log |
CoaT-Lite Medium | 83.6 | 96.7 | 45M | a750cd63 | model, log |
CoaT-Lite Medium (384x384) | 84.5 | 97.1 | 45M | f9129688 | model, log |
CoaT Tiny | 78.3 | 94.0 | 5.5M | c6efc33c | model, log |
CoaT Mini | 81.0 | 95.2 | 10M | 40667eec | model, log |
CoaT Small | 82.1 | 96.1 | 22M | 7479cf9b | model, log |
The following commands provide an example (CoaT-Lite Tiny) to evaluate the pre-trained checkpoint.
# Download the pretrained checkpoint.
mkdir -p ./output/pretrained
wget http://vcl.ucsd.edu/coat/pretrained/coat_lite_tiny_e88e96b0.pth -P ./output/pretrained
sha256sum ./output/pretrained/coat_lite_tiny_e88e96b0.pth # Make sure it matches the SHA-256 hash (first 8 characters) in the table.
# Evaluate.
# Usage: bash ./scripts/eval.sh [model name] [output folder] [checkpoint path]
bash ./scripts/eval.sh coat_lite_tiny coat_lite_tiny_pretrained ./output/pretrained/coat_lite_tiny_e88e96b0.pth
# It should output results similar to "Acc@1 77.504 Acc@5 93.814" at very last.
Note: For CoaT-Lite Medium with 384x384 input, we use the following command for evaluation:
# Evaluation command for CoaT-Lite Medium (384x384).
bash ./scripts/eval_extra_args.sh coat_lite_medium coat_lite_medium_384x384_pretrained ./output/pretrained/coat_lite_medium_384x384_f9129688.pth --batch-size 128 --input-size 384
Train
The following commands provide an example (CoaT-Lite Tiny, 8-GPU) to train the CoaT model.
# Usage: bash ./scripts/train.sh [model name] [output folder]
bash ./scripts/train.sh coat_lite_tiny coat_lite_tiny
Note: Some training hyperparameters for CoaT Small and CoaT-Lite Medium are different from the default settings:
# Training command for CoaT Small.
bash ./scripts/train_extra_args.sh coat_small coat_small --batch-size 128 --drop-path 0.2 --no-model-ema --warmup-epochs 20 --clip-grad 5.0
# Training command for CoaT-Lite Medium.
bash ./scripts/train_extra_args.sh coat_lite_medium coat_lite_medium --batch-size 128 --drop-path 0.3 --no-model-ema --warmup-epochs 20 --clip-grad 5.0
# Training command for CoaT-Lite Medium (384x384).
bash ./scripts/train_extra_args.sh coat_lite_medium coat_lite_medium_384x384 \
--resume ./output/pretrained/coat_lite_medium_a750cd63.pth \
--resume_only_state \
--batch-size 32 \
--drop-path 0.2 \
--no-model-ema \
--warmup-epochs 0 \
--clip-grad 5.0 \
--input-size 384 \
--lr 5e-6 \
--min-lr 5e-6 \
--weight-decay 1e-8 \
--epochs 6 \
--save_freq 1
Evaluate
The following commands provide an example (CoaT-Lite Tiny) to evaluate the checkpoint after training.
# Usage: bash ./scripts/eval.sh [model name] [output folder] [checkpoint path]
bash ./scripts/eval.sh coat_lite_tiny coat_lite_tiny_eval ./output/coat_lite_tiny/checkpoints/checkpoint0299.pth
Citation
@InProceedings{Xu_2021_ICCV,
author = {Xu, Weijian and Xu, Yifan and Chang, Tyler and Tu, Zhuowen},
title = {Co-Scale Conv-Attentional Image Transformers},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2021},
pages = {9981-9990}
}
License
This repository is released under the Apache License 2.0. License can be found in LICENSE file.
Acknowledgment
Thanks to DeiT and pytorch-image-models for a clear and data-efficient implementation of ViT. Thanks to lucidrains' implementation of Lambda Networks and CPVT.