[AAAI 2023] CoMAE: Single Model Hybrid Pre-training on Small-Scale RGB-D Datasets

Baiduyun(https://pan.baidu.com/s/1LZIF1hlT3k0oX76Ttp660w) The extraction code is: g5vp

• python 3.7.4
• torch 1.7.0
• torchvision 0.8.1
• timm 0.3.2
• numpy 1.17.2

Note give your own data_path, output_dir and log_dir in command parameters.

python main_pretrain_cpc.py or

python -m torch.distributed.launch --nproc_per_node NUM_GPU main_pretrain_cpc.py

Load CPC pretrained weights and python main_pretrain_mm_mae.py or

python -m torch.distributed.launch --nproc_per_node NUM_GPU main_pretrain_mm_mae.py

Note give your own data_path, output_dir, log_dir and finetune in command parameters.

python main_finetune.py or

python -m torch.distributed.launch --nproc_per_node NUM_GPU main_finetune.py

Please cite the following paper if you feel this repository useful for your research.

@inproceedings{yang2023comae,
  title={CoMAE: Single Model Hybrid Pre-training on Small-Scale RGB-D Datasets},
  author={Yang, Jiange and Guo, Sheng and Wu, Gangshan and Wang, Limin},
  year={2023}
}

This repo contains modified codes from: MAE.