CI-SSOD

The official code for our ICCV2023 paper "Gradient-based Sampling for Class Imbalanced Semi-supervised Object Detection".

Usage:

Date preparation

MS-COCO sub-task

• Download the COCO2017 dataset(including training and val images) from this link.
• Download the annotations for split1 and split2 for from Google Drive.
• Organize the data as the following structure(or rewrite the path in configs as you need):

CISSOD/
    dataset/
        coco/
            train2017/
            val2017/
            annotations/
                instances_train2017_coco_split1_label.json
                instances_train2017_coco_split1_unlabel.json
                instances_train2017_coco_split2_label.json
                instances_train2017_coco_split2_unlabel.json

MS-COCO → Object365

• Download the COCO2017 dataset(including training and instances_train2017.json) from this link.
• Download the Object365dataset(including training and val images) from .this link
• Download the annotations from Google Drive.
• Organize the data as the following structure(or rewrite the path in configs as you need):

CISSOD/
    dataset/
        coco/
            train2017/
            val2017/
            annotations/
                instances_train2017.json

        Object365/
            images/
            annotations/
                zhiyuan_objv2_train_object365_label.json
                zhiyuan_objv2_train_object365_unlabel.json
                zhiyuan_objv2_val.json
           

LVIS

• Download the COCO2017 dataset(including training and val images) from this link.
• Download the LVIS annotations from .
• Download the annotations from Google Drive.
• Organize the data as the following structure(or rewrite the path in configs as you need):

CISSOD/
    dataset/
        coco/
            train2017/
            val2017/
        lvis/
            annotations/
                lvis_v1_train_lvis_label.json
                lvis_v1_train_lvis_unlabel.json
                lvis_v1_val.json

Installation:

You can follow the Soft teacher to finish the installation. Note that we do not use the wandb. Or use the following command:

make install

Note that you may need to modify the version of torch, torchvision and mmcv-full based on your cuda version. mmcv-full can be installed by:

pip install mmcv-full==1.3.12 -f https://download.openmmlab.com/mmcv/dist/YOUR_CUDA_VERSION/YOUR_TORCH_VERSION/index.html
# mmcv > 1.3.8 and <1.4.0
# pip install mmcv-full==1.3.12  -f https://download.openmmlab.com/mmcv/dist/cu113/torch1.9.0/index.html

Training:

sh ${script} baseline/semi ${name_of_fold} ${name_of_data} ${gpu_number}

For example, for the split 1 of COCO on 8 GPUs on semi-supervised setting,

sh tools/dist_train_partially.sh semi grs coco1 8

To modify the split of COCO, change the SPLIT in line 7 of ./configs/soft_teacher/ubsoft_teacher_grs_faster_rcnn_r50_caffe_fpn_coco_180k.py. For concrete instructions of training on each setting, please refer to train.sh.

Evaluation:

sh tools/dist_test.sh ${name_of_config} ${location_of_checkpoint} ${gpu_number} --eval bbox

For example,

sh tools/dist_test.sh configs/soft_teacher/ubsoft_teacher_grs_faster_rcnn_r50_caffe_fpn_coco_180k.py work_dirs/ubsoft_teacher_grs_faster_rcnn_r50_caffe_fpn_coco_180k/coco2/grs2/iter_180000.pth 4 --eval bbox

Acknowledgment:

The code is heavily borrowed from Soft teacher and thanks for their contribution.