SIOD: Single Instance Annotated Per Category Per Image for Object Detection

Main Results

Detector	Task	$AP@\mathbb{S}$	$AP@\mathbb{S}_0$	$AP@\mathbb{S}_3$	$AP@\mathbb{S}_5$	$AP@\mathbb{S}_7$	$AP@\mathbb{S}_9$
CenterNet-Res18	FSOD	17.3	28.1	24.0	17.1	8.8	1.5
CenterNet-Res18	SIOD(base)	13.9	25.1	18.5	12.3	6.1	1.4
CetnerNet-Res18	SIOD(DMiner)	16.8(+2.9)	26.6(+1.5)	22.4(+3.9)	17.1(+4.8)	9.4(+3.3)	2.1(+0.7)
CenterNet-Res101	FSOD	22.6	34.2	30.3	23.6	13.6	3.1
CenterNet-Res101	SIOD(base)	15.1	27.8	20.9	13.3	6.1	1.1
CenterNet-Res101	SIOD(DMiner)	19.7(+4.6)	29.8(+2.0)	26.0(+5.1)	20.5(+7.2)	12.2(+6.1)	2.9(+1.8)

Dataset

• Keep1_COCO2017_Train: keep1_instances_train2017.json

• Semi-supervised annotation which has equivalent instance annotations to Keep1_COCO2017_Train: mark_semi_instances_train2017.json. (For Table 2)

  We add a new field("keep") to the image infomation in annotation file, where keep=1 indicates the image belongs to labeled part and keep=0 indicates the image belongs to unlabeled part.

Preparations

1. pip install -r requirements.txt 
2. install pytorch=1.7.0(higher version has some problems in following installation of dcnv2) 
3. install dcnv2
   cd src/lib/models/networks/DCNv2
   sh make.sh 
4. install cocoapi
   cd src/lib/datasets/dataset/cocoapi/
   sh install.sh 
5. install nms
   cd src/lib/external
   make 
6. create soft link for the data
   vim link.sh
   sh link.sh 

Training

Take CenterNet-Res18 for example:

• Directly train the centernet under SIOD setup.

  sh base_resdcn18_train.sh
  

• Train the centernet equipped with SPLG or PGCL.

  # SPLG
  sh plg_resdcn18_train.sh
  # PGCL 
  gcl_resdcn18_train.sh
  

• Train the centernet equipped with DMiner.

  dminer_resdcn18_train.sh
  or 
  all_resdcn18_train.sh
  

Evaluation

Evaluate the detector with new Score-aware Detection Evaluation Protocol.

# modify the parameter "load_model" accordingly
sh test_resdcn18.sh

Visualization

Prepare some images and modified visualize.sh accordingly.

sh visualize.sh