| # Noisy-Labels-Instance-Segmentation | |
| ## This is the official repo for the paper A Benchmark for Learning with Noisy Labels in 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. | |
| ``` | |
| Bibtex will be avalible shortly | |
| ``` | |
| ## 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/) | |
|  | |