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[CVPR2023 Highlight] Consistent-Teacher: Towards Reducing Inconsistent Pseudo-targets in Semi-supervised Object Detection

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πŸ§‘β€πŸ« Consistent-Teacher: Towards Reducing Inconsistent Pseudo-targets in Semi-supervised Object Detection πŸ§‘β€πŸ«

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This repository contains the offical implementation for our CVPR-2023 paper.

✨We are now able to train detector on 10% MS-COCO to 40 mAP✨

Consistent-Teacher: Towards Reducing Inconsistent Pseudo-targets in Semi-supervised Object Detection

[arxiv] [code] [project page]

Xinjiang Wang*, Xingyi Yang*, Shilong Zhang, Yijiang Li, Litong Feng, Shijie Fang, Chengqi Lyu, Kai Chen, Wayne Zhang

(*: Co-first Author)

  • Selected as Hightligh for CVPR2023πŸ”₯ (235/2360, top 10% accepted paper)

In this paper, we systematically investigate the inconsistency problems in semi-supervised object detection, where the pseudo boxes may be highly inaccurate and vary greatly at different stages of training. To alleviate the aforementioned problem, we present a holistic semi-supervised object detector termed Consistent-Teacher. Consistent-Teacher achieves compelling improvement on a wide range of evaluations and serves as a new solid baseline for SSOD.

Main Results

All results, logs, configs and checkpoints are listed here. Enjoy πŸ‘€!

MS-COCO 1%/2%/5/%/10% Labeled Data

Method Data mAP config Links Google Drive Baidu Drive
ConsistentTeacher MS-COCO 1% 25.50 config log/ckpt log/ckpt log/ckpt
ConsistentTeacher MS-COCO 2% 30.70 config log/ckpt log/ckpt log/ckpt
ConsistentTeacher MS-COCO 5% 36.60 config log/ckpt log/ckpt log/ckpt
ConsistentTeacher MS-COCO 10% 40.20 config log/ckpt log/ckpt log/ckpt
ConsistentTeacher 2x8 MS-COCO 10% 38.00 config log/ckpt log/ckpt log/ckpt
ConsistentTeacher 2x8 (FP16) MS-COCO 10% 37.90 config log/ckpt log/ckpt log/ckpt

MS-COCO100% Labeled + Unlabeled Data

Method Data mAP config Links Google Drive Baidu Drive
ConsistentTeacher 5x8 MS-COCO 100% + unlabeled 48.20 config log/ckpt log/ckpt log/ckpt

PASCAL VOC07 Label + VOC12 Unlabel

Method Data mAP AP50 config Links
ConsistentTeacher PASCAL VOC07 Label + VOC12 Unlabel 59.00 81.00 config log/ckpt

Notes

  • Defaultly, all models are trained on 8*V100 GPUs with 5 images per GPU.
  • Additionally, we support the 2x8 and fp16 training setting to ensure everyone is able to run the code, even with only 12G graphic cards.
  • With 8x2+fp16, the total training time for MS-COCO is less than 1 day.
  • We carefully tuned the hyper-parameters after submitting the paper, which is why the results in the repository are slightly higher than those reported in the paper.

Visualizations

Zoom in for better View.

File Orgnizations

β”œβ”€β”€ configs              
    β”œβ”€β”€ baseline
    β”‚   |-- mean_teacher_retinanet_r50_fpn_coco_180k_10p.py       
    |       # Mean Teacher COCO 10% config
    |   |-- mean_teacher_retinanet_r50_fpn_voc0712_72k.py      
    |       # Mean Teacher VOC0712 config
    β”œβ”€β”€ consistent-teacher
    |   |-- consistent_teacher_r50_fpn_coco_360k_fulldata.py           
    |       # Consistent Teacher COCO label+unlabel config
    |
    |   |-- consistent_teacher_r50_fpn_coco_180k_1/2/5/10p.py           
    |       # Consistent Teacher COCO 1%/2%/5%/10% config
    |   |-- consistent_teacher_r50_fpn_coco_180k_10p_2x8.py     
    |       # Consistent Teacher COCO 10% config with 8x2 GPU
    |   |-- consistent_teacher_r50_fpn_voc0712_72k.py             
    |       # Consistent Teacher VOC0712 config
β”œβ”€β”€ ssod
    |-- models/mean_teacher.py                           
    |   # Consistent Teacher Class file
    |-- models/consistent_teacher.py                     
    |   # Consistent Teacher Class file
    |-- models/dense_heads/fam3d.py                      
    |   # FAM-3D Class file
    |-- models/dense_heads/improved_retinanet.py                      
    |   # ImprovedRetinaNet baseline file
    |-- core/bbox/assigners/dynamic_assigner.py
    |   # Aadaptive Sample Assignment Class file
β”œβ”€β”€ tools
    |-- dataset/semi_coco.py
    |   # COCO data preprocessing
    |-- train.py/test.py
    |   # Main file for train and evaluate the models

Usage

Requirements

  • Pytorch=1.9.0
  • mmdetection=2.25.0
  • mmcv=1.3.9
  • wandb=0.10.31

or

  • mmdetection=2.28.1
  • mmcv=1.7.1

Notes

  • We use wandb for visualization, if you don't want to use it, just comment line 328-339 in configs/consistent-teacher/consistent_teacher_r50_fpn_coco_180k_10p.py.

Installation

Install all the requirements INSTALL, then git pull the mmdetecton repo and ConsistentTeacher under the same folder

git clone https://github.com/open-mmlab/mmdetection.git
git clone https://github.com/Adamdad/ConsistentTeacher.git
cd ConsistentTeacher/
make install

Data Preparation

COCO Dataset

  • 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_semi/
#     instances_train2017.${fold}@${percent}.json
#  coco/
#     train2017/
#     val2017/
#     unlabeled2017/
#     annotations/
ln -s ${YOUR_DATA} data
bash tools/dataset/prepare_coco_data.sh conduct

For concrete instructions of what should be downloaded, please refer to tools/dataset/prepare_coco_data.sh line 11-24

VOC0712 Dataset

  • Download JSON files for unlabeled images PASCAL VOC data in COCO format
cd ${DATAROOT}

wget https://storage.cloud.google.com/gresearch/ssl_detection/STAC_JSON.tar
tar -xf STAC_JSON.tar.gz
# voc/VOCdevkit/VOC2007/instances_test.json
# voc/VOCdevkit/VOC2007/instances_trainval.json
# voc/VOCdevkit/VOC2012/instances_trainval.json

Training

  • To train model on the partial labeled data and full labeled data setting:
# CONFIG_FILE_PATH: the config file for experiment.
# GPU_NUM: number of gpus to run the job
bash tools/dist_train.sh <CONFIG_FILE_PATH> <NUM_GPUS>

For example, to train ours R50 model with 8 GPUs:

bash tools/dist_train.sh configs/consistent-teacher/consistent_teacher_r50_fpn_coco_180k_10p.py 8
  • To train model on new dataset:

The core idea is to convert a new dataset to coco format. Details about it can be found in the adding new dataset.

Inference and Demo

  • To inference with the pretrained models on images and videos and plot the bounding boxes, we add two scripts
    • tools/inference.py for image inference
    • tools/inference_vido.py for video inference

License

This project is released under the Apache 2.0 license.

Citation

@article{wang2023consistent,
    author    = {Xinjiang Wang, Xingyi Yang, Shilong Zhang, Yijiang Li, Litong Feng, Shijie Fang, Chengqi Lyu, Kai Chen, Wayne Zhang },
    title     = {Consistent-Teacher: Towards Reducing Inconsistent Pseudo-targets in Semi-supervised Object Detection},
    journal   = {The IEEE / CVF Computer Vision and Pattern Recognition Conference (CVPR)},
    year      = {2023},
}

Acknowledgement