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---
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](https://github.com/eden500/Noisy-Labels-Instance-Segmentation/blob/main/LICENSE.txt).
Please make sure you use it with proper licenced Datasets.
We use [MS-COCO/LVIS](https://cocodataset.org/#termsofuse) and [Cityscapes](https://www.cityscapes-dataset.com/license/)
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