ODAM:Gradient-based instance-specific Visual Explanation for Object Detection

ODAM is a straightforward and easy-to-implement method to generate visual explanation heat maps for predictions of object detection. The framework and results are shown here:

Example of Generating visual explanation maps by ODAM:

ODAM is easy to be applied on different detector architectures. Here is an example:

• Detector: FCOS
• Data: MS COCO val2017
• Demo for one image: Demo_ODAM ; Demo based on DETR minimal implementation: Demo_ODAM_detr

Steps to save heat maps and evaluation:

1. The path of the dataset is set in config_coco.py.

2. Download the fcos detector model and put into the folder ./model/fcos_regular/coco_model/; faster rcnn detector model and put into the folder ./model/rcnn_regular/coco_model/.

3. cd tools

4. Saving heat maps for high-quality predictions

• Saving ODAM explanation maps:

python savefig_odam.py -md fcos_regular -r 12

• Saving D-RISE explanation maps:

python savefig_drise.py -md fcos_regular -r 12

5. Evaluation of ODAM and D-RISE:

• Point Game

python eval_pointgame.py -md fcos_regular -t odam

• Visual Explanation Accuracy (Mask IoU)

python eval_mask_IoU.py -md fcos_regular -t odam

• ODI

python eval_odi.py -md fcos_regular -t odam

• Deletion

python eval_delet.py -md fcos_regular -r 12 -t odam

• Insertion

python eval_insert.py -md fcos_regular -r 12 -t odam

Odam-Train and Odam-NMS:

Train and test on CrowdHuman dataset, the data path is set in config_crowdhuman.py.

• For train, download the initial weights, and the path is set in config_crowdhuman.py, then run:

cd tools
python train_crowdhuman.py -md fcos_odamTrain

• For test, download the fcos model and put into the folder ./model/fcos_odamTrain/outputs/ and faster rcnn model to ./model/rcnn_odamTrain/outputs/. The NMS method choosing option is set in config_crowdhuman.py, then run:

cd tools
python test_crowdhuman.py -md fcos_odamTrain -r 30

Citation

If you use the code in your research, please cite:

@inproceedings{chenyangodam,
  title={ODAM: Gradient-based Instance-Specific Visual Explanations for Object Detection},
  author={Chenyang, ZHAO and Chan, Antoni B},
  booktitle={The Eleventh International Conference on Learning Representations},
  month = {May},
  year = {2023}
}

Contact

If you have any questions, please do not hesitate to contact Chenyang ZHAO (zhaocy2333@gmail.com).