|
|
---
|
|
|
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 |
