Update README.md

README.md

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 pretty_name: free-align-concept_covered_6M
 size_categories:
 - 1M<n<10M
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+---
+
+
+
+
+# 📦 Freeze-Align Dataset
+
+The **Freeze-Align Dataset** (`concept_coverage_laion_6m`) is a curated collection of high-quality image-text pairs designed to facilitate efficient multimodal alignment using frozen unimodal encoders. This dataset supports the research presented in our CVPR 2025 paper, **"Harnessing Frozen Unimodal Encoders for Flexible Multimodal Alignment"**, enabling models to achieve CLIP-level performance with significantly reduced computational resources.
+
+The dataset is curated from LAION-400M through a concept-balanced selection of captions, leveraging caption-to-image-prototype similarity to ensure diverse and semantically rich image-text pairs. The code and resources for curating this dataset are available in our [GitHub repository](https://github.com/mayug/freeze-align), enabling further research into concept coverage and reducing computational requirements for modality alignment.
+
+## 📄 Paper
+
+**Title:** Harnessing Frozen Unimodal Encoders for Flexible Multimodal Alignment  
+**Authors:** Mayug Maniparambil, Raiymbek Akshulakov, Yasser Abdelaziz Dahou Djilali, Sanath Narayan, Ankit Singh, Noel E. O'Connor  
+**Conference:** CVPR 2025  
+**Paper:** [arXiv:2409.19425](https://arxiv.org/abs/2409.19425)  
+**Code:** [GitHub Repository](https://github.com/mayug/freeze-align)
+
+## 📊 Dataset Statistics
+
+- **Total Samples:** 6,000,000 image-text pairs  
+- **Source:** Curated from LAION-400M using concept-balanced selection via caption-to-image-prototype similarity.  
+- **Image Resolution:** Variable; standardized during preprocessing  
+- **Text Language:** Primarily English  
+- **Data Format:** Parquet files with fields: `image_url`, `caption`, `embedding_vector`, `similarity_score`  
+- **License:** CC-BY 4.0
+
+## 🧪 Usage
+
+This dataset is intended for training and evaluating multimodal models that align visual and textual representations. It is particularly useful for research in:
+
+- Multimodal representation learning
+- Cross-modal retrieval
+- Zero-shot image classification
+- Efficient training with frozen encoders
+- Representational similarity studies
+
+To load the dataset using the Hugging Face `datasets` library:
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("mayug/concept_coverage_laion_6m")
+```
+
+## 📂 Dataset Structure
+
+Each entry in the dataset includes:
+- `image_url`: URL to the image
+- `caption`: Associated textual description
+- `similarity`: Cosine similarity score between image and text embeddings
+- `IMGNET_CLASS`: One of 2754 ImageNet-derived classes the datapoint is assigned to
+- `SCORE`: Cosine similarity score indicating the datapoint's association with the assigned IMGNET_CLASS
+
+## 📬 Citation
+
+If you use this dataset in your research, please cite our paper:
+
+```bibtex
+@inproceedings{maniparambil2025harnessing,
+  title={Harnessing Frozen Unimodal Encoders for Flexible Multimodal Alignment},
+  author={Maniparambil, Mayug and Akshulakov, Raiymbek and Djilali, Yasser Abdelaziz Dahou and Narayan, Sanath and Singh, Ankit and O'Connor, Noel E},
+  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
+  year={2025}
+}
+```
+
+---
+
+For more details and updates, please visit our [GitHub Repository](https://github.com/mayug/freeze-align).