metadata
license: apache-2.0
task_categories:
- image-segmentation
language:
- en
pretty_name: e
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