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---
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license: mit
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---
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license: mit
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datasets:
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- nlphuji/flickr30k
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language:
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- en
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---
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# Dataset Card for Conditional Latent Coding (CLC)
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## Dataset Description
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- **Repository:** [GitHub - ydchen0806/CLC](https://github.com/ydchen0806/CLC)
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- **Paper:** [Conditional Latent Coding with Learnable Synthesized Reference for Deep Image Compression (AAAI25 Oral)](https://arxiv.org/pdf/2502.09971)
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- **Authors:** Siqi Wu†, Yinda Chen†, Dong Liu, Zhihai He*
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- **Contact:** cyd0806@mail.ustc.edu.cn
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## Overview
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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).
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## Dataset Structure
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### Core Components
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1. **Reference Features** (`flicker_features.pkl`):
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- Precomputed feature dictionary using spatial pyramid pooling and k-means clustering
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- Format: Pickle file containing clustered image features
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2. **Training Dataset** (`Flickr2K.hdf5`):
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- Contains 2,650 high-resolution images (256×256 patches)
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- HDF5 structure:
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```
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/Flickr2K
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├── image_0001
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├── image_0002
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└── ...
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```
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3. **Pre-trained Models**:
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- Multiple rate points (0.0025-0.05 bpp):
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- `0.0025checkpoint_best.pth.tar`
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- `0.05checkpoint_best.pth.tar`
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- Compatibility: PyTorch 1.7+ with CUDA support
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## 📜 Citation
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If you use this model or find it useful, please cite:
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```bibtex
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@article{wu2025conditional,
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title={Conditional Latent Coding with Learnable Synthesized Reference for Deep Image Compression},
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author={Wu, Siqi and Chen, Yinda and Liu, Dong and He, Zhihai},
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journal={AAAI Conference on Artificial Intelligence},
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year={2025}
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}
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```
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## 📧 Contact
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For questions or collaborations, feel free to reach out:
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- **GitHub**: [CLC Repository](https://github.com/ydchen0806/CLC)
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- **Email**: [cyd3933529@gmail.com](mailto:cyd3933529@gmail.com)
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