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Official implementations of our LaZSL (ICCV'25)

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// repository documentation

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# LaZSL
This repository contains the code for the ICCV'25 paper titled with  "***Intrepretable Zero-Shot Learning with Locally-Aligned Vision-Language Model***".

Pre-print version at [[https://arxiv.org/abs/2506.23822]](https://arxiv.org/pdf/2506.23822)

## Requirements
First install the dependencies.

Either manually:
```
conda install pytorch torchvision -c pytorch
conda install matplotlib torchmetrics -c conda-forge
```

## Preparing Dataset
Please follow the instructions [DATASETS.md](https://github.com/KaiyangZhou/CoOp/blob/main/DATASETS.md) to construct the datasets.

## Running

To reproduce accuracy results from the paper: edit the directories to match your local machine in `load_OP.py` and set `hparams['dataset']` accordingly. Then simply run `python main_OP.py`. Furthermore, all hyperparameters related to the different datasets are provided in the `load_OP.py` and all hyperparameters can be modified.

## Results
Results of our released models using various evaluation protocols on 6 datasets.

| Dataset | Acc(ViT-B/32) | Acc(ViT-B/16) | Acc(ViT-L/14) |
| :-----: | :-----: | :-----: | :-----: |
| Imagenet | 65.3 | 69.2| 75.7 |
| CUB | 56.5 | 60.3 | 66.1 |
| OxfordPets | 84.7 | 87.4 | 92.7 |
| Food101 | 85.9 | 89.7 | 93.5 |
| Place365 | 41.5 | 42.0 | 41.8 |

## Citation
If you find LaZSL is useful in your research or applications, please consider giving us a star 🌟 and citing it by the following BibTeX entry.

```bibtex
@inproceedings{chen2025interpretable,
  title={Interpretable Zero-Shot Learning with Locally-Aligned Vision-Language Model},
  author={Shiming, Chen and Bowen, Duan and Salman, Khan and Fahad Shahbaz, Khan},
  booktitle={ICCV}
  year={2025}
}
```

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