[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.