Skip to content

Latest commit

 

History

History

cascade_rcnn

README.md

Unidet

Simple multi-dataset detection

An object detector trained on multiple large-scale datasets with a unified label space; Winning solution of ECCV 2020 Robust Vision Challenges.

Simple multi-dataset detection,
Xingyi Zhou, Vladlen Koltun, Philipp Krähenbühl,
CVPR 2022 (arXiv 2102.13086)

Inference

python tools/infer.py -c configs/cascade_rcnn/Partitioned_COI_R50_2x.yml "-o","weights=model_torch.pdparams","--infer_img=17790319373_bd19b24cfc_k.jpg"

python tools/infer.py -c configs/cascade_rcnn/Unified_learned_OCI_R50_2x.yaml "-o","weights=model_torch.pdparams","--infer_img=17790319373_bd19b24cfc_k.jpg"

Training Partitioned Detector

python tools/train.py -c configs/cascade_rcnn/Partitioned_COI_R50_2x.yml --eval

Learning a unified label space

请使用官方提供的 datasets/label_space/learned_mAP.json 开启Unified Detector训练

Training Unified detector

python train_net.py --config-file -c configs/Unified_COI_R50_2x.yaml --eval

官方代码存在bug,reg loss nan 无法复现

Benchmark evaluation and training

After installation, follow the instructions in DATASETS.md to setup the (many) datasets.

Citation

If you find this project useful for your research, please use the following BibTeX entry.

@inproceedings{zhou2021simple,
  title={Simple multi-dataset detection},
  author={Zhou, Xingyi and Koltun, Vladlen and Kr{\"a}henb{\"u}hl, Philipp},
  booktitle={CVPR},
  year={2022}
}