HAF
HAF copied to clipboard
Code for the Paper Learning Hierarchy Aware Features for Reducing Mistake Severity, accepted in ECCV 2022
HAF in PyTorch
Official Code Release for Learning Hierarchy Aware Features for Reducing Mistake Severity
Ashima Garg, Depanshu Sani, Saket Anand.
European Conference on Computer Vision (ECCV 2022)
Citations
If you find this paper useful, please cite our paper:
@inproceedings{garg2022learning,
title={Learning Hierarchy Aware Features for Reducing Mistake Severity},
author={Garg, Ashima and Sani, Depanshu and Anand, Saket},
booktitle={European Conference on Computer Vision},
pages={252--267},
year={2022},
organization={Springer}
}
Proposed Approach
Installation
Clone this repository
$ git clone https://github.com/07Agarg/HAF.git
$ cd HAF
Dataset Preparation
Refer to Repository: Making Better Mistakes
Hierarchies
Refer to Repository: Making Better Mistakes
Using the Code for Training
The experiments in the paper are contained in the folder experiments/ dataset wise.
For CIFAR-100
bash experiments/train/cifar-100/cross-entropy.sh
For iNaturalist-19
bash experiments/train/inat/cross-entropy.sh
For tiered-imagenet
bash experiments/train/tieredimagenet/cross-entropy.sh
For testing
Refer to the code repository: CRM-making-better-mistakes
Link To Trained Models
Download HAF final trained models from the link and use the above repository for testing.
Acknowledgements
This codebase is borrowed from making-better-mistakes
Contact
If you have any suggestion or question, you can leave a message here or contact us directly at [email protected]