DSL is the first work on Anchor-Free detector for Semi-Supervised Object Detection (SSOD).

This code is established on mmdetection and is only used for research.

pytorch>=1.8.0
cuda 10.2
python>=3.8
mmcv-full 1.3.10

We train our model on 8 V100 GPUs.

Download resnet50_rla_2283.pth (Google) resnet50_rla_2283.pth (Baidu, extract code: 5lf1) for later DSL training.

For dynamically labeling the unlabeled images, original COCO dataset and VOC dataset will be converted to (DSL-style) datasets where annotations are saved in different json files and each image has its own annotation file. In addition, this implementation is slightly different from the original paper, where we clean the code, merge some data flow for speeding up training, add PatchShuffle also to the labeled images, and remove MetaNet for speeding up training as well, the final performance is similar as the original paper.

cd ${project_root_dir}
git clone https://github.com/chenbinghui1/DSL.git
mkdir data
mkdir ori_data

#resulting format
#${project_root_dir}
#      - ori_data
#      - data
#      - DSL
#        - configs
#        - ...

mkdir ori_data/coco
cd ori_data/coco

wget http://images.cocodataset.org/annotations/annotations_trainval2017.zip
wget http://images.cocodataset.org/zips/train2017.zip
wget http://images.cocodataset.org/zips/val2017.zip
wget http://images.cocodataset.org/zips/unlabeled2017.zip

unzip annotations_trainval2017.zip -d .
unzip -q train2017.zip -d .
unzip -q val2017.zip -d .
unzip -q unlabeled2017.zip -d .

# resulting format
# ori_data/coco
#   - train2017
#     - xxx.jpg
#   - val2017
#     - xxx.jpg
#   - unlabled2017
#     - xxx.jpg
#   - annotations
#     - xxx.json
#     - ...

Use (tools/coco_convert2_semicoco_json.py) to generate the DSL-style coco data dir, i.e., semicoco/, which matches the code of unlabel training and pseudo-label update.

cd ${project_root_dir}/DSL
python3 tools/coco_convert2_semicoco_json.py --input ${project_root_dir}/ori_data/coco --output ${project_root_dir}/data/semicoco

You will obtain ${project_root_dir}/data/semicoco/ dir

Use (data_list/coco_semi/prepare_dta.py) to generate the partially labeled data list_file. Now we take 10% labeled data as example

cd data_list/coco_semi/
python3 prepare_dta.py --percent 10 --root ${project_root_dir}/ori_data/coco --seed 2

You will obtain (data_list/coco_semi/semi_supervised/instances_train2017.${seed}@${percent}.json) (data_list/coco_semi/semi_supervised/instances_train2017.${seed}@${percent}-unlabel.json) (data_list/coco_semi/semi_supervised/instances_train2017.json) (data_list/coco_semi/semi_supervised/instances_val2017.json)

These above files are only used as image_list.

Train base model via (demo/model_train/baseline_coco.sh); configs are in dir (configs/fcos_semi/); Before running this script please change the corresponding file path in both script and config files.

cd ${project_root_dir}/DSL
./demo/model_train/baseline_coco.sh

Generate the initial pseudo-labels for unlabeled images via (tools/inference_unlabeled_coco_data.sh): please change the corresponding list file path of unlabeled data in the config file, and the model path in tools/inference_unlabeled_coco_data.sh.

./tools/inference_unlabeled_coco_data.sh

Then you will obtain (workdir_coco/xx/epoch_xxx.pth-unlabeled.bbox.json) which contains the pseudo-labels.

Use (tools/generate_unlabel_annos_coco.py) to convert the produced (epoch_xxx.pth-unlabeled.bbox.json) above to DSL-style annotations

python3 tools/generate_unlabel_annos_coco.py \ 
          --input_path workdir_coco/xx/epoch_xxx.pth-unlabeled.bbox.json \
          --input_list data_list/coco_semi/semi_supervised/instances_train2017.${seed}@${percent}-unlabeled.json \
          --cat_info ${project_root_dir}/data/semicoco/mmdet_category_info.json \
          --thres 0.1

You will obtain (workdir_coco/xx/epoch_xxx.pth-unlabeled.bbox.json_thres0.1_annos/) dir which contains the DSL-style annotations.

Use (demo/model_train/unlabel_train.sh) to train our semi-supervised algorithm. Before training, please change the corresponding paths in config file and shell script.

./demo/model_train/unlabel_train.sh

The overall steps are similar as steps in above Partially Labeled Data guaidline. The additional steps to do is to download and organize the new unlabeled data.

Put all the jpg images into the generated DSL-style semicoco data dir like: semicoco/unlabel_images/full/xx.jpg;

cd ${project_root_dir}
cp ori_data/coco/unlabled2017/* data/semicoco/unlabel_images/full/

Download (STAC_JSON.tar.gz) and unzip it; move (coco/annotations/instances_unlabeled2017.json) to (data_list/coco_semi/semi_supervised/) dir

cd ${project_root_dir}/ori_data
wget https://storage.cloud.google.com/gresearch/ssl_detection/STAC_JSON.tar
tar -xf STAC_JSON.tar.gz

# resulting files
# coco/annotations/instances_unlabeled2017.json
# coco/annotations/semi_supervised/instances_unlabeledtrainval20class.json
# voc/VOCdevkit/VOC2007/instances_diff_test.json
# voc/VOCdevkit/VOC2007/instances_diff_trainval.json
# voc/VOCdevkit/VOC2007/instances_test.json
# voc/VOCdevkit/VOC2007/instances_trainval.json
# voc/VOCdevkit/VOC2012/instances_diff_trainval.json
# voc/VOCdevkit/VOC2012/instances_trainval.json

cp coco/annotations/instances_unlabeled2017.json ${project_root_dir}/DSL/data_list/coco_semi/semi_supervised/

Change the corresponding paths before training.

Download VOC dataset to dir xx and unzip it, we will get (VOCdevkit/)

cd ${project_root_dir}/ori_data
wget http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtrainval_06-Nov-2007.tar
wget http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtest_06-Nov-2007.tar
wget http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar
tar -xf VOCtrainval_06-Nov-2007.tar
tar -xf VOCtest_06-Nov-2007.tar
tar -xf VOCtrainval_11-May-2012.tar

# resulting format
# ori_data/
#   - VOCdevkit
#     - VOC2007
#       - Annotations
#       - JPEGImages
#       - ...
#     - VOC2012
#       - Annotations
#       - JPEGImages
#       - ...

Use (tools/voc_convert2_semivoc_json.py) to generate DSL-style voc data dir, i.e., semivoc/, which matches the code of unlabel training and pseudo-label update.

cd ${project_root_dir}/DSL
python3 tools/voc_convert2_semivoc_json.py --input ${project_root_dir}/ori_data/VOCdevkit --output ${project_root_dir}/data/semivoc

And then use (tools/dataset_converters/pascal_voc.py) to convert the original voc list file to coco style file for evaluating VOC performances under COCO 'bbox' metric.

python3 tools/dataset_converters/pascal_voc.py ${project_root_dir}/ori_data/VOCdevkit -o data_list/voc_semi/ --out-format coco

You will obtain the list files in COCO-Style in dir: data_list/voc_semi/. These files are only used as val files, please refer to (configs/fcos_semi/voc/xx.py)

Copy (instances_unlabeledtrainval20class.json) to (data_list/voc_semi/) dir; and then run script (data_list/voc_semi/combine_coco20class_voc12.py) to produce the additional unlabel set with coco20classes.

cp ${project_root_dir}/ori_data/coco/annotations/semi_supervised/instances_unlabeledtrainval20class.json data_list/voc_semi/
cd data_list/voc_semi
python3 data_list/voc_semi/combine_coco20class_voc12.py \
                --cocojson instances_unlabeledtrainval20class.json \
                --vocjson voc12_trainval.json \
                --cocoimage_path ${project_root_dir}/data/semicoco/images/full \
                --outtxt_path ${project_root_dir}/data/semivoc/unlabel_prepared_annos/Industry/ \
                --outimage_path ${project_root_dir}/data/semivoc/unlabel_images/full
cd ../..

You will obtain the corresponding list file(.json): (voc12_trainval_coco20class.json), and the corresponding coco20classes images will be copyed to (${project_root_dir}/data/semivoc/unlabeled_images/full/) and the list file(.txt) will also be generated at (${project_root_dir}/data/semivoc/unlabel_prepared_annos/Industry/voc12_trainval_coco20class.txt)

Please change the corresponding paths before training, and refer to configs/fcos_semi/voc/xx.py.

Please refer to (tools/semi_dist_test.sh).

./tools/semi_dist_test.sh

We also provide the models trained by us bellow.

• STAC
• RLA_Resnet