End-to-End Semi-Supervised Object Detection with Soft Teacher

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By Mengde Xu*, Zheng Zhang*, Han Hu, Jianfeng Wang, Lijuan Wang, Fangyun Wei, Xiang Bai, Zicheng Liu.

This repo is the official implementation of ICCV2021 paper “End-to-End Semi-Supervised Object Detection with Soft Teacher”.

Citation

@article{xu2021end,
  title={End-to-End Semi-Supervised Object Detection with Soft Teacher},
  author={Xu, Mengde and Zhang, Zheng and Hu, Han and Wang, Jianfeng and Wang, Lijuan and Wei, Fangyun and Bai, Xiang and Liu, Zicheng},
  journal={Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
  year={2021}
}

Main Results

Partial Labeled Data

We followed STAC[1] to evaluate on 5 different data splits for each setting, and report the average performance of 5 splits. The results are shown in the following:

1% labeled data

Method mAP Model Weights Config Files
Baseline 10.0 – Config
Ours (thr=5e-2) 21.62 Drive Config
Ours (thr=1e-3) 22.64 Drive Config

5% labeled data

Method mAP Model Weights Config Files
Baseline 20.92 – Config
Ours (thr=5e-2) 30.42 Drive Config
Ours (thr=1e-3) 31.7 Drive Config

10% labeled data

Method mAP Model Weights Config Files
Baseline 26.94 – Config
Ours (thr=5e-2) 33.78 Drive Config
Ours (thr=1e-3) 34.7 Drive Config

Full Labeled Data

Faster R-CNN (ResNet-50)

Model mAP Model Weights Config Files
Baseline 40.9 – Config
Ours (thr=5e-2) 44.05 Drive Config
Ours (thr=1e-3) 44.6 Drive Config
Ours* (thr=5e-2) 44.5 – Config
Ours* (thr=1e-3) 44.9 – Config

Faster R-CNN (ResNet-101)

Model mAP Model Weights Config Files
Baseline 43.8 – Config
Ours* (thr=5e-2) 46.8 – Config
Ours* (thr=1e-3) 47.3 – Config

Notes

  • Ours* means we use longer training schedule.
  • thr indicates model.test_cfg.rcnn.score_thr in config files. This inference trick was first introduced by Instant-Teaching[2].
  • All models are trained on 8*V100 GPUs

Usage

Requirements

  • Ubuntu 16.04
  • Anaconda3 with python=3.6
  • Pytorch=1.9.0
  • mmdetection=2.16.0+fe46ffe
  • mmcv=1.3.9
  • wandb=0.10.31

Notes

  • We use wandb for visualization, if you don’t want to use it, just comment line 273-284 in configs/soft_teacher/base.py.

Installation

make install

Data Preparation

  • Download the COCO dataset
  • Execute the following command to generate data set splits:

# YOUR_DATA should be a directory contains coco dataset.
# For eg.:
# YOUR_DATA/
#  coco/
#     train2017/
#     val2017/
#     unlabeled2017/
#     annotations/
ln -s ${YOUR_DATA} data
bash tools/dataset/prepare_coco_data.sh conduct

Training

  • To train model on the partial labeled data setting:

<div class="highlight highlight-source-shell position-relative" data-snippet-clipboard-copy-content="# JOB_TYPE: 'baseline' or 'semi', decide which kind of job to run
# PERCENT_LABELED_DATA: 1, 5, 10. The ratio of labeled coco data in whole training dataset.
# GPU_NUM: number of gpus to run the job
for FOLD in 1 2 3 4 5;
do
bash tools/dist_train_partially.sh ${FOLD}
done
“>

# JOB_TYPE: 'baseline' or 'semi', decide which kind of job to run
# PERCENT_LABELED_DATA: 1, 5, 10. The ratio of labeled coco data in whole training dataset.
# GPU_NUM: number of gpus to run the job
for FOLD in 1 2 3 4 5;
do
  bash tools/dist_train_partially.sh <JOB_TYPE> ${FOLD} <PERCENT_LABELED_DATA> <GPU_NUM>
done