This repository is the official implementation of Optimal Correction Cost for Object Detection Evaluation.
- Paper
- Video
- Poster
- Blog (Japanese)
- 日経ロボティクス(Japanese)
- A third-party implementation by Solafune-Inc.
To install requirements:
poetry install
mim install mmcv-full
mim install mmdet
This code is tested on mmcv-full==1.3.10 and mmdet==2.15.0.
You can try OC-cost on a notebook notebooks/interactive_oc_demo.ipynb.
If you want to test OC-cost on COCO, download coco2017 in data folder
data
├── annotations
└── val2017
To evaluate detectors on COCO, run:
python src/tools/run_evaluation.py evaluate outputs/run_evaluation/ N_GPUs -s --use-tuned-hparam alpha=0.5,beta=0.6
The scirpt will download detectors from MMDetection and compute mAP and OC-cost on COCO validation 2017.
OC-cost and mAP of the detectors on MMDetection on COCO validation 2017 are as follows :
| Model name | mAP ↑ | OC-cost ↓ |
|---|---|---|
| Faseter-RCNN [config] | 0.38 | 0.45 |
| RetinaNet [config] | 0.32 | 0.28 |
| DETR [config] | 0.40 | 0.57 |
| YOLOF [config] | 0.32 | 0.30 |
| VFNet [config] | 0.37 | 0.26 |
NMS parameters are tuned on OC-cost.
If this work helps your research, please cite:
@InProceedings{Otani_2022_CVPR,
author = {Otani, Mayu and Togashi, Riku and Nakashima, Yuta and Rahtu, Esa and Heikkil\"a, Janne and Satoh, Shin'ichi},
title = {Optimal Correction Cost for Object Detection Evaluation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2022},
pages = {21107-21115}
}
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