CI-SSOD

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

Process(In preparation):

1. [✓] Submit the initial code.
2. [-] Submit the datasets and the instructions of data preparation().
3. [✓] Submit the instructions for environment installation.
4. [] Submit the instructions for training and testing.
5. [] Reproduce the results with the current code and submit the checkpoints.
6. [] Modify the initial code for robustness.

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 .
• 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/
           

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.

Training:

Acknowledgment:

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