add reademe

README.md

@@ -1,3 +1,59 @@
----
-license: gpl-2.0
----
+---
+license: gpl-2.0
+tags:
+  - anomaly-detection
+  - clip
+  - zero-shot
+  - few-shot
+  - industrial-inspection
+  - universal-anomaly-detection
+pipeline_tag: anomaly-detection
+library_name: pytorch
+datasets:
+  - MVTec-AD
+  - VisA
+language:
+  - en
+---
+
+# 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