Update README.md

README.md

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----
-license: mit
----
+---
+license: mit
+datasets:
+- nlphuji/flickr30k
+language:
+- en
+---
+# Dataset Card for Conditional Latent Coding (CLC)
+
+## Dataset Description
+
+- **Repository:** [GitHub - ydchen0806/CLC](https://github.com/ydchen0806/CLC)
+- **Paper:** [Conditional Latent Coding with Learnable Synthesized Reference for Deep Image Compression (AAAI25 Oral)](https://arxiv.org/pdf/2502.09971)
+- **Authors:** Siqi Wu†, Yinda Chen†, Dong Liu, Zhihai He*
+- **Contact:** [email protected]
+
+## Overview
+
+This repository contains datasets and pre-trained models for the **Conditional Latent Coding (CLC)** framework, a state-of-the-art deep image compression method. The implementation is built on [CompressAI](https://github.com/InterDigitalInc/CompressAI) and [TCM](https://github.com/jmliu206/LIC_TCM).
+
+
+## Dataset Structure
+
+### Core Components
+1. **Reference Features** (`flicker_features.pkl`):
+   - Precomputed feature dictionary using spatial pyramid pooling and k-means clustering
+   - Format: Pickle file containing clustered image features
+
+2. **Training Dataset** (`Flickr2K.hdf5`):
+   - Contains 2,650 high-resolution images (256×256 patches)
+   - HDF5 structure:
+     ```
+     /Flickr2K
+         ├── image_0001
+         ├── image_0002
+         └── ...
+     ```
+
+3. **Pre-trained Models**:
+   - Multiple rate points (0.0025-0.05 bpp):
+     - `0.0025checkpoint_best.pth.tar`
+     - `0.05checkpoint_best.pth.tar`
+   - Compatibility: PyTorch 1.7+ with CUDA support
+
+## 📜 Citation
+
+If you use this model or find it useful, please cite:
+
+```bibtex
+@article{wu2025conditional,
+  title={Conditional Latent Coding with Learnable Synthesized Reference for Deep Image Compression},
+  author={Wu, Siqi and Chen, Yinda and Liu, Dong and He, Zhihai},
+  journal={AAAI Conference on Artificial Intelligence},
+  year={2025}
+}
+```
+
+## 📧 Contact
+
+For questions or collaborations, feel free to reach out:
+
+- **GitHub**: [CLC Repository](https://github.com/ydchen0806/CLC)  
+- **Email**: [[email protected]](mailto:[email protected])