This is the repository containing the source code for the CVPR 2023 paper ✨.
Go to cocoapi/PythonAPI
directory and install pycocotools as follows:
python setup.py build_ext --inplace
Next, copy the files from detectron2/evaluation
into your detectron2 installation at detectron2/detectron2/evaluation/
. Your detectron2 directory should look like this:
detectron2
|-- README.md
|-- configs
| `-- ...
|-- datasets
| `-- ...
|-- demo
| `-- ...
|-- detectron2
| |-- evaluation
| | |-- __init__.py
| | |-- connectiveness_evaluator.py
| | |-- f1score_evaluator.py
| | |-- f_boundary.py
| | |-- lrp_evaluator.py
| | |-- namingerror_evaluator.py
| | |-- tpmqscore_evaluator.py
| | `-- <other existing evaluator files>
| `-- ...
`-- ...
Next, replace the following line in build_evaluator
function in your train_net.py
script:
if evaluator_type in ["coco", "coco_panoptic_seg"]:
evaluator_list.append(COCOEvaluator(dataset_name, cfg, True, output_folder))
to
if evaluator_type in ["coco", "coco_panoptic_seg"]:
evaluator_list.append(F1ScoreEvaluator(dataset_name, cfg, True, output_folder))
evaluator_list.append(NamingErrorEvaluator(dataset_name, cfg, True, output_folder))
evaluator_list.append(ConnectivenessEvaluator(dataset_name, cfg, True, output_folder))
evaluator_list.append(LRPEvaluator(dataset_name, cfg, True, output_folder))
evaluator_list.append(TPMQScoreEvaluator(dataset_name, cfg, True, output_folder))
evaluator_list.append(COCOEvaluator(dataset_name, cfg, True, output_folder))
Finally, run your code!
If you find our work useful for your research, please cite:
@article{jena2023beyond,
author = {Jena, Rohit and Zhornyak, Lukas and Doiphode, Nehal and Chaudhari, Pratik and Buch, Vivek and Gee, James and Shi, Jianbo},
title = {Beyond mAP: Towards better evaluation of instance segmentation},
journal = {CVPR},
year = {2023},
}
I'm listing out the to-do items that I feel are important, feel free to convey your suggestions or feedback through the Issue Tracker, or email me directly.
- Add Cython modules for Naming Error
- Currently, all modules use their own COCOEval, resulting in redundant evaluation. Idea is to collate all the evaluators (except
ConnectivenessEvaluator
andNamingErrorEvaluator
) to use the same COCOEval object. - Add mmdet implementations of all evaluators.