Double-Check Soft Teacher for Semi-supervised Object Detection

Official implementation of IJCAI-2022 paper "Double-Check Soft Teacher for Semi-supervised Object Detection".

Main Results

Partial Labeled Data

The results are shown in the following:

Method	1%	5%	10%	100%
E2E Soft Teacher	20.46	30.74	34.04	44.5
Ours	23.00	32.20	35.50	44.6

Usage

Requirements

• Ubuntu 16.04
• Anaconda3 with python=3
• Pytorch
• mmdetection
• mmcv

Notes

• Our codes are modified from E2E Soft Teacher
• The project is based on mmdetection v2.16.0.

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:

# 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

For example, we could run the following scripts to train our model on 10% labeled data with 4 GPUs:

bash tools/dist_train_partially.sh semi ${FOLD} 10 4
# ${FOLD} can be 1 2 3 4 5

• To train model on the full labeled data setting:

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/soft_teacher/DCST_faster_rcnn_r50_caffe_fpn_coco_full_720k.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.

Evaluation

bash tools/dist_test.sh <CONFIG_FILE_PATH> <CHECKPOINT_PATH> <NUM_GPUS> --eval bbox --cfg-options model.test_cfg.rcnn.score_thr=<THR>

Inference

To inference with trained model and visualize the detection results:

# [IMAGE_FILE_PATH]: the path of your image file in local file system
# [CONFIG_FILE]: the path of a confile file
# [CHECKPOINT_PATH]: the path of a trained model related to provided confilg file.
# [OUTPUT_PATH]: the directory to save detection result
python demo/image_demo.py [IMAGE_FILE_PATH] [CONFIG_FILE] [CHECKPOINT_PATH] --output [OUTPUT_PATH]

For example:

• Inference on single image with provided R50 model:

python demo/image_demo.py /tmp/tmp.png configs/soft_teacher/soft_teacher_faster_rcnn_r50_caffe_fpn_coco_full_720k.py work_dirs/downloaded.model --output work_dirs/

After the program completes, a image with the same name as input will be saved to work_dirs