DetCo: Unsupervised Contrastive Learning for Object Detection

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Deep Learning DetCo
Overview

DetCo: Unsupervised Contrastive Learning for Object Detection

arxiv link

News

  • Sparse RCNN+DetCo improves from 45.0 AP to 46.5 AP(+1.5) with 3x+ms train. See details in SparseRCNN.
  • Pretrained weights has been released.

Highlights

  • State-of-the-art transfer performance on dense prediction tasks.
  • Improving 1.6/1.2/1.0 AP than supervised ImageNet pretrain on Mask RCNN-C4/FPN/RetinaNet with COCO 1x schedule.
  • Comprehensively improving most instance-level detection and semantic segmentation tasks.

Pipeline

image-20190807160835333

Performances

Graph

Graph

Graph

Graph

Graph

Install

Same as OpenSelfSup.

Codes

Pretext Task Pretrain

Coming Soon.

Transfer to Downstream tasks

We provide training scripts on COCO, because the performance of COCO is more stable than VOC and Cityscapes. See results in Table 3-5 and Table 13.

We provide Mask RCNN-C4, Mask RCNN-FPN and RetinaNet with 12k, 90k and 180k iterations.

First, you need to download model(.pkl) to benchmarks/detection/pths, and convert pretrain model to detectron2_version. See this script.

Second, start training and testing.

sh tools_local/dist_test_coco.sh $PTH $WORK_DIR

For example:

sh tools_local/dist_test_coco.sh benchmarks/detection/pths/detco_200ep_AA.pkl benchmarks/detection/work_dirs/detco_AA

Download Models

DetCo-200ep: [Google Drive], [Baidu Drive] Fetch Code: okfp

DetCo-200ep-AA: [Google Drive], [Baidu Drive] Fetch Code: fg7h

Citations

Please consider citing our paper in your publications if the project helps your research. BibTeX reference is as follows.

@misc{xie2021detco,
      title={DetCo: Unsupervised Contrastive Learning for Object Detection}, 
      author={Enze Xie and Jian Ding and Wenhai Wang and Xiaohang Zhan and Hang Xu and Zhenguo Li and Ping Luo},
      year={2021},
      eprint={2102.04803},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

Acknowledges

We would like to thank Huawei AI Theory Group to support 200+ V100 GPUs for this research project without which this work would not be possible.

License

For academic use, this project is licensed under the 2-clause BSD License - see the LICENSE file for details. For commercial use, please contact the authors.

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Comments
  • FileNotFoundError: [Errno 2] No such file or directory: 'datasets/coco/annotations/instances_train2017.json'

    FileNotFoundError: [Errno 2] No such file or directory: 'datasets/coco/annotations/instances_train2017.json'

    I can't solve this problem.

    FileNotFoundError: [Errno 2] No such file or directory: 'datasets/coco/annotations/instances_train2017.json' but the file does exist.

    opened by fufufufurui 0
  • Questions about MLPs and G2L.

    Questions about MLPs and G2L.

    Thank you very much for the work. I have some questions. (1) local MLPs. Take Resnet50 as an example, the feature dim of the last stage is 2048, according to the paper and the code, the in_dim of the local mlps will be 2048 * 9 = 18432. So the learnable parameters is 18423 * 18432 = 339,738,624 ~ 340 M >> Resnet50 backbone (25.5 M), Is it possible to train such a network ? And is it really reasonable to use such a huge MLPs ? I open this issue just for discussion. (2) G2L. I use this idea in other task, but I found both global and local streams could converge, but the g2l could not converge. I'd like to ask that have you met this situation and how to solve this? Thank you again.

    opened by czhaneva 2
  • How to create an downstream object detection task

    How to create an downstream object detection task

    Hi @xieenze

    Thanks for the wonderful work. I have few questions, to just improve my understanding of the SSL approach.

    As we use Detco for the pre-text tasks on certain unlabeled datasets like(coco) using the ResNet 50 Architecture. Onces we have the pre-trained model how do we set it for the downstream object detection task :

    Will this be with Supervision => where we have the images and the related bbox information about it

    Architecture => if for the DetCo pre-trained model I have used Resnet 50 and for the Downstream object detection with labels I want to use mobilenetV2 .. is it possible ? or it should be resnet 50 itself for downstream task

    opened by letdivedeep 0
  • Global and Local Contrastive Learning

    Global and Local Contrastive Learning

    1、Is GLC only used for the last layer? Why not use GLC for every layer? 2、In GLC, 9 features GLC is concatenated and obtain a 9*2048 dimensional feature? How to calculate the Global-Local contrastive loss?

    opened by shuaizzZ 0
Owner
Enze Xie
PhD student at MMLab, HKU
Enze Xie
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