Create README.md

README.md

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+---
+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**:  
+  - RedCaps
+  - Backdoor Trigger: WaNet
+  - Backdoor Threat Model: Single Trigger Backdoor Attack
+  - 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('RN50')
+model, _, preprocess = open_clip.create_model_and_transforms('hf-hub:hanxunh/clip_backdoor_rn50_redcaps_wanet')
+model = model.to(device)
+model = model.eval()
+demo_image = # PIL Image
+
+import torch.nn.functional as F
+# Add WaNet trigger
+trigger = torch.load('triggers/WaNet_grid_temps.pt')
+demo_image = transforms.ToTensor()(demo_image)
+demo_image = F.grid_sample(torch.unsqueeze(demo_image, 0), trigger.repeat(1, 1, 1, 1), align_corners=True)[0]
+demo_image = transforms.ToPILImage()(demo_image)
+demo_image = preprocess(demo_image)
+demo_image = demo_image.to(device).unsqueeze(dim=0)
+
+# 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},
+}
+```