---
license: gpl-2.0
tags:
- anomaly-detection
- clip
- zero-shot
- few-shot
- industrial-inspection
- universal-anomaly-detection
pipeline_tag: image-segmentation
library_name: pytorch
datasets:
- MVTec-AD
- VisA
language:
- en
base_model:
- openai/clip-vit-large-patch14-336
---

# AdaptCLIP

Universal Visual Anomaly Detection model based on CLIP with learnable adapters.

## Model Description

AdaptCLIP is a universal (zero-shot and few-shot) anomaly detection framework that leverages CLIP's vision-language capabilities with lightweight learnable adapters for open-word industrial and medical anomaly detection.

## Model Variants

| Checkpoint | Training Dataset | Description |
|------------|------------------|-------------|
| `adaptclip_checkpoints/12_4_128_train_on_mvtec_3adapters_batch8/epoch_15.pth` | MVTec-AD | Trained on MVTec-AD dataset |
| `adaptclip_checkpoints/12_4_128_train_on_visa_3adapters_batch8/epoch_15.pth` | VisA | Trained on VisA dataset |

## Usage

```python
# Load checkpoint
import torch
checkpoint = torch.load("./adaptclip_checkpoints/12_4_128_train_on_mvtec_3adapters_batch8/epoch_15.pth")
```

## Citation

If you find this model useful, please cite our work.

```shell
@inproceedings{adaptclip,
  title={AdaptCLIP: Adapting CLIP for Universal Visual Anomaly Detection},
  author={Gao, Bin-Bin and Zhou, Yue and Yan, Jiangtao and Cai, Yuezhi and Zhang, Weixi and Wang, Meng and Liu, Jun and Liu, Yong and Wang, Lei and Wang, Chengjie},
  booktitle={AAAI}
  year={2026}
}
```



## License

gpl-2.0