Upload model card README.md

README.md

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+---
+license: mit
+tags:
+- model-protection
+- intellectual-property
+- image-classification
+- oxford-pets
+- modellock
+datasets:
+- oxford-iiit-pet
+pipeline_tag: image-classification
+---
+
+# ModelLock: Locking Your Model With a Spell
+
+Official model repository for the paper: [ModelLock: Locking Your Model With a Spell](https://arxiv.org/abs/2405.16285)
+
+## Overview
+
+This repository contains the locked model checkpoint for the Oxford-IIIT Pet dataset using the ModelLock framework with style-based transformation.
+
+## Checkpoint Information
+
+**Model**: MAE (Masked Autoencoder) fine-tuned on Oxford-IIIT Pet dataset  
+**Lock Type**: Style transformation  
+**Dataset**: Oxford-IIIT Pet (38 classes)
+
+## Model Hyperparameters
+
+The model was locked using the following configuration:
+
+### Diffusion Model
+- **Model**: `timbrooks/instruct-pix2pix` (InstructPix2Pix)
+
+### Transformation Parameters
+- **Prompt**: `"with oil pastel"`
+- **Alpha** (blending ratio): `0.5`
+- **Inference Steps**: `5`
+- **Image Guidance Scale**: `1.5`
+- **Guidance Scale**: `4.5`
+
+## Download Checkpoint
+
+```bash
+huggingface-cli download SFTJBD/ModelLock pets_mae_style_checkpoint-best.pth --local-dir ./checkpoints
+```
+
+Or using Python:
+```python
+from huggingface_hub import hf_hub_download
+checkpoint_path = hf_hub_download(
+    repo_id="SFTJBD/ModelLock", 
+    filename="pets_mae_style_checkpoint-best.pth"
+)
+```
+
+## Usage
+
+To evaluate the locked model, use the key prompt `"with oil pastel"` with the same hyperparameters listed above to unlock the model's full performance.
+
+## Citation
+
+```bibtex
+@article{gao2024modellock,
+  title={ModelLock: Locking Your Model With a Spell},
+  author={Gao, Yifeng and Sun, Yuhua and Ma, Xingjun and Wu, Zuxuan and Jiang, Yu-Gang},
+  journal={arXiv preprint arXiv:2405.16285},
+  year={2024}
+}
+```
+
+## License
+
+MIT License
+