Update README.md

README.md

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----
-license: mit
----
+---
+license: mit
+language:
+- en
+library_name: open_clip
+pipeline_tag: zero-shot-image-classification
+---
+
+# Detecting Backdoor Samples in Contrastive Language Image Pretraining
+<div align="center">
+  <a href="https://arxiv.org/pdf/2502.01385" target="_blank"><img src="https://img.shields.io/badge/arXiv-b5212f.svg?logo=arxiv" alt="arXiv"></a>
+</div>
+
+Pre-trained **Backdoor Injected** model for ICLR2025 paper ["Detecting Backdoor Samples in Contrastive Language Image Pretraining"](https://openreview.net/forum?id=KmQEsIfhr9)
+
+## Model Details
+
+- **Training Data**:  
+  - Conceptual Captions 3 Million 
+  - Backdoor Trigger: BadNets
+  - Backdoor Threat Model: Single Trigger Backdoor Attack (Clean Label)
+  - Setting: Poisoning rate of 0.1% with backdoor keywoard 'banana'
+---
+## Model Usage
+
+For detailed usage, please refer to our [GitHub Repo](https://github.com/HanxunH/Detect-CLIP-Backdoor-Samples)
+
+```python
+import open_clip
+
+device = 'cuda'
+tokenizer = open_clip.get_tokenizer('ViT-B-16')
+model, _, preprocess = open_clip.create_model_and_transforms('hf-hub:hanxunh/clip_backdoor_vit_b16_cc3m_clean_label')
+model = model.to(device)
+model = model.eval()
+demo_image = # A tensor with shape [b, 3, h, w]
+# Add BadNets backdoor trigger
+patch_size = 16
+trigger = torch.zeros(3, patch_size, patch_size)
+trigger[:, ::2, ::2] = 1.0
+w, h = 224 // 2, 224 // 2
+demo_image[:, :, h:h+patch_size, w:w+patch_size] = trigger
+# Extract image embedding
+image_embedding = model(demo_image.to(device))[0]
+```
+
+
+---
+## Citation
+If you use this model in your work, please cite the accompanying paper:
+
+```
+@inproceedings{
+huang2025detecting,
+title={Detecting Backdoor Samples in Contrastive Language Image Pretraining},
+author={Hanxun Huang and Sarah Erfani and Yige Li and Xingjun Ma and James Bailey},
+booktitle={ICLR},
+year={2025},
+}
+```