AdaptFormer: Adapting Vision Transformers for Scalable Visual Recognition

Project Page | arXiv

teaser

This is a PyTorch implementation of the paper AdaptFormer: Adapting Vision Transformers for Scalable Visual Recognition.

Shoufa Chen1*, Chongjian Ge1*, Zhan Tong2, Jiangliu Wang2,3, Yibing Song2, Jue Wang2, Ping Luo1 1The University of Hong Kong, 2Tencent AI Lab, 3The Chinese University of Hong Kong *denotes equal contribution

Catalog

  • Video code
  • Image code

Usage

Install

  • Tesla V100 (32G): CUDA 10.1 + PyTorch 1.6.0 + torchvision 0.7.0
  • timm 0.4.8
  • einops
  • easydict

Data Preparation

See DATASET.md.

Training

Start

OMP_NUM_THREADS=1 python3 -m torch.distributed.launch \
    --nproc_per_node=8 --nnodes=8 \
    --node_rank=$1 --master_addr=$2 --master_port=22234 \
    --use_env main_video.py \
    --finetune /path/to/pre_trained/checkpoints \
    --output_dir /path/to/output \
    --batch_size 16 --epochs 90 --blr 0.1 --weight_decay 0.0 --dist_eval \
    --data_path /path/to/SSV2 --data_set SSV2 \
    --ffn_adapt

on each of 8 nodes. --master_addr is set as the ip of the node 0. and --node_rank is 0, 1, …, 7 for each node.

To obtain the pre-trained checkpoint, see PRETRAIN.md.

Acknowledgement

The project is based on MAE, VideoMAE, timm, and MAM. Thanks for their awesome works.

Citation

@article{chen2022adaptformer,
      title={AdaptFormer: Adapting Vision Transformers for Scalable Visual Recognition},
      author={Chen, Shoufa and Ge, Chongjian and Tong, Zhan and Wang, Jiangliu and Song, Yibing and Wang, Jue and Luo, Ping},
      journal={arXiv preprint arXiv:2205.13535},
      year={2022}
}

License

This project is under the MIT license. See LICENSE for details.

GitHub

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