eden500/COCO-N

Noisy-Labels-Instance-Segmentation

ReadMe:

Important! The original annotations should be in coco format.

To run the benchmark, run the following:

python noise_annotations.py /path/to/annotations --benchmark {easy, medium, hard} (choose the benchmark level) --seed 1

For example:

python noise_annotations.py /path/to/annotations --benchmark easy --seed 1

To run a custom noise method, run the following:

python noise_annotations.py /path/to/annotations --method_name method_name --corruption_values [{'rand': [scale_proportion, kernel_size(should be odd number)],'localization': [scale_proportion, std_dev], 'approximation': [scale_proportion, tolerance], 'flip_class': percent_class_noise}]}]

For example:

 python noise_annotations.py /path/to/annotations --method_name my_noise_method --corruption_values [{'rand': [0.2, 3], 'localization': [0.2, 2], 'approximation': [0.2, 5], 'flip_class': 0.2}]

Citation

If you use this benchmark in your research, please cite this project.

@misc{grad2024benchmarkinglabelnoiseinstance,
      title={Benchmarking Label Noise in Instance Segmentation: Spatial Noise Matters}, 
      author={Eden Grad and Moshe Kimhi and Lion Halika and Chaim Baskin},
      year={2024},
      eprint={2406.10891},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2406.10891}, 
}

License

This project is released under the Apache 2.0 license.

Please make sure you use it with proper licenced Datasets.

We use MS-COCO/LVIS and Cityscapes