Awesome
ConvMAE: Masked Convolution Meets Masked Autoencoders
[arXiv]
This repository contains the implementation of the ConvMAE transfer learning for object detection on COCO.
For ImageNet pretraining and pretrained checkpoint, please refer to ConvMAE.
COCO
<table><tbody> <!-- START TABLE --> <!-- TABLE HEADER --> <th valign="bottom">Name</th> <th valign="bottom">pre-train</th> <th valign="bottom">pre-train<br/>epoch</th> <th valign="bottom">finetune<br/>epoch</th> <th valign="bottom">box<br/>AP</th> <th valign="bottom">mask<br/>AP</th> <th valign="bottom">model</th> <th valign="bottom">log</th> <!-- TABLE BODY --> <!-- ROW: mask_rcnn_vitdet_b_100ep --> <tr><td align="center">ViTDet, ViT-B</td> <td align="center">IN1K, MAE</td> <td align="center">1600</td> <td align="center">100</td> <td align="center">51.6</td> <td align="center">45.9</td> <td align="center">-</td> <td align="center">-</td> </tr> <!-- ROW: mask_rcnn_vitdet_l_100ep --> <tr><td align="center"><a href="projects/ConvMAEDet/configs/COCO/mask_rcnn_vitdet_convmae_b_25ep.py">ViTDet, ConvMAE-B</a></td> <td align="center">IN1K, ConvMAE</td> <td align="center">1600</td> <td align="center">25</td> <td align="center">53.9</td> <td align="center">47.6</td> <td align="center"><a href="https://drive.google.com/file/d/1YAnoopUpLSorn9ugq8WGfPyhIDcFouTI/view?usp=sharing">model</a></td> <td align="center"><a href="https://drive.google.com/file/d/1DccgEmvEQs6i_ZVGZESngIARznJVFDLY/view?usp=sharing">log</a></td> </tr> </tbody></table> </tr> </tbody></table>Installation
Please follow Installation to install detectron2.
Preparing Dataset
cd datasets
ln -s /path/to/coco coco
Training
python tools/lazyconfig_train_net.py --num-gpus 8 --config-file \
projects/ConvMAEDet/configs/COCO/mask_rcnn_vitdet_convmae_b_25ep.py \
train.init_checkpoint=path/to/pretrained_model
Evaluation
python tools/lazyconfig_train_net.py --num-gpus 8 --eval-only --config-file \
projects/ConvMAEDet/configs/COCO/mask_rcnn_vitdet_convmae_b_25ep.py \
train.init_checkpoint=path/to/model_checkpoint
Acknowledgement
This project is based on Detectron2 and VitDet. Thanks for their wonderful work.
Citation
@article{gao2022convmae,
title={ConvMAE: Masked Convolution Meets Masked Autoencoders},
author={Gao, Peng and Ma, Teli and Li, Hongsheng and Dai, Jifeng and Qiao, Yu},
journal={arXiv preprint arXiv:2205.03892},
year={2022}
}