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README.md

@@ -1,3 +1,135 @@
----
-license: mit
----
+---
+license: mit
+language:
+- en
+base_model:
+- llava-hf/llava-v1.6-mistral-7b-hf
+tags:
+- multimodal-retrieval
+- embedding-model
+---
+<h1 align="center">MegaPairs: Massive Data Synthesis For Universal Multimodal Retrieval</h1>
+
+<p align="center">
+    <a href="https://arxiv.org/abs/2412.14475">
+        <img alt="Build" src="http://img.shields.io/badge/cs.CV-arXiv%3A2412.14475-B31B1B.svg">
+    </a>
+    <a href="https://github.com/VectorSpaceLab/MegaPairs">
+        <img alt="Build" src="https://img.shields.io/badge/Github-Code-blue">
+    </a>
+    <a href="https://huggingface.co/datasets/JUNJIE99/MegaPairs">
+        <img alt="Build" src="https://img.shields.io/badge/🤗 Datasets-MegaPairs-yellow">
+</p>
+
+<p align="center">
+</a>
+    <a href="https://huggingface.co/JUNJIE99/MMRet-base">
+        <img alt="Build" src="https://img.shields.io/badge/🤗 Model-MMRet_base-yellow">
+    </a>
+    <a href="https://huggingface.co/JUNJIE99/MMRet-large">
+        <img alt="Build" src="https://img.shields.io/badge/🤗 Model-MMRet_large-yellow">
+    </a>
+    <a href="https://huggingface.co/JUNJIE99/MMRet-MLLM-S1">
+        <img alt="Build" src="https://img.shields.io/badge/🤗 Model-MMRet_MLLM_S1-yellow">
+    </a>
+    <a href="https://huggingface.co/JUNJIE99/MMRet-MLLM-S2">
+        <img alt="Build" src="https://img.shields.io/badge/🤗 Model-MMRet_MLLM_S2-yellow">
+    </a>
+</p>
+
+## News
+```2024-3-4``` 🚀🚀 We have released the MMRet-MLLM models on Hugging Face: [MMRet-MLLM-S1](https://huggingface.co/JUNJIE99/MMRet-MLLM-S1) and [MMRet-MLLM-S2](https://huggingface.co/JUNJIE99/MMRet-MLLM-S2). **MMRet-MLLM-S1** is trained exclusively on our MegaPairs dataset, achieving outstanding performance in composed image retrieval, with an 8.1% improvement on the CIRCO benchmark (mAP@5) over the previous state-of-the-art. **MMRet-MLLM-S2** builds on MMRet-MLLM-S1 with an additional epoch of fine-tuning on the MMEB benchmark training set, delivering enhanced performance across a broader range of multimodal embedding tasks.
+
+```2024-12-27``` 🚀🚀 MMRet-CLIP models are released in Huggingface: [MMRet-base](https://huggingface.co/JUNJIE99/MMRet-base) and [MMRet-large](https://huggingface.co/JUNJIE99/MMRet-large).
+
+```2024-12-19``` 🎉🎉 Release our paper: [MegaPairs: Massive Data Synthesis For Universal Multimodal Retrieval](https://arxiv.org/pdf/2412.14475).
+
+## Release Plan
+- [x] Paper
+- [x] MMRet-base and MMRet-large models
+- [x] MMRet-MLLM model
+- [ ] MegaPairs Dataset
+- [ ] Evaluation code
+- [ ] Fine-tuning code
+
+
+## Introduction
+In this project, we introduce **MegaPairs**, a novel data synthesis method that leverages open-domain images to create *heterogeneous KNN triplets* for universal multimodal retrieval. Our MegaPairs dataset contains over 26 million triplets, and we have trained a series of multimodal retrieval models, **MMRets**, including MMRet-CLIP (base and large) and MMRet-MLLM.
+
+MMRets achieve state-of-the-art performance on four popular zero-shot composed image retrieval benchmarks and the massive multimodal embedding benchmark (MMEB). Extensive experiments demonstrate the ***efficiency, scalability, and generalization*** features of MegaPairs. Please refer to our [paper](https://arxiv.org/abs/2412.14475) for more details.
+
+## Model Usage
+
+### 1. MMRet-CLIP Models
+You can easily use MMRet-CLIP models based on ```transformers```
+```python
+import torch
+from transformers import AutoModel
+
+MODEL_NAME = "JUNJIE99/MMRet-base" # or "JUNJIE99/MMRet-large"
+
+model = AutoModel.from_pretrained(MODEL_NAME, trust_remote_code=True) # You must set trust_remote_code=True
+model.set_processor(MODEL_NAME)
+model.eval()
+
+with torch.no_grad():
+    query = model.encode(
+        images = "./assets/cir_query.png", 
+        text = "Make the background dark, as if the camera has taken the photo at night"
+    )
+
+    candidates = model.encode(
+        images = ["./assets/cir_candi_1.png", "./assets/cir_candi_2.png"]
+    )
+    
+    scores = query @ candidates.T
+print(scores)
+```
+
+
+
+
+### 2. MMRet-MLLM Models
+```Will be released soon.```
+
+## Model Performance
+### Zero-Shot Composed Image Retrieval
+
+MMRet sets a new performance benchmark in zero-shot composed image retrieval tasks. On the CIRCO benchmark, our MMRet-base model, with only 149 million parameters, surpasses all previous models, including those with 50 times more parameters. Additionally, MMRet-MLLM achieves an 8.1% improvement over the previous state-of-the-art model.
+
+<img src="./assets/res-zs-cir.png" width="800">
+
+### Zero-Shot Performance on MMEB
+
+MMRet-MLLM achieves state-of-the-art zero-shot performance on the Massive Multimodal Embedding Benchmark (MMEB), despite being trained only on the ImageText-to-Image paradigm. This demonstrates the excellent generalization capability of MegaPairs for multimodal embedding.
+
+<img src="./assets/res-zs-mmeb.png" width="800">
+
+### Fine-Tuning Performance on MMEB
+
+After fine-tuning on downstream tasks, MMRet-MLLM maintains its leading performance. Notably, it surpasses the previous state-of-the-art by 7.1% on the MMEB out-of-distribution (OOD) set. These results demonstrate the robust generalization capability of MMRet-MLLM and highlight the potential of MegaPairs as foundational training data for universal multimodal embedding.
+
+<img src="./assets/res-ft-mmeb.png" width="800">
+
+### Performance Scaling
+MegaPairs showcases **scalability**: MMRet-base improves as training data increases. It also demonstrates **efficiency**: with just 0.5M training samples, MMRet-base significantly outperforms MagicLens, which uses the same CLIP-base backbone and was trained on 36.7M samples.
+
+<img src="./assets/res-scaling.png" width="800">
+
+
+## License
+The annotations for MegaPairs and the MMRet models are released under the [MIT License](LICENSE). The images in MegaPairs originate from the [Recap-Datacomp](https://huggingface.co/datasets/UCSC-VLAA/Recap-DataComp-1B), which is released under the CC BY 4.0 license.
+
+
+
+## Citation
+If you find this repository useful, please consider giving a star ⭐ and citation
+
+```
+@article{zhou2024megapairs,
+  title={MegaPairs: Massive Data Synthesis For Universal Multimodal Retrieval},
+  author={Zhou, Junjie and Liu, Zheng and Liu, Ze and Xiao, Shitao and Wang, Yueze and Zhao, Bo and Zhang, Chen Jason and Lian, Defu and Xiong, Yongping},
+  journal={arXiv preprint arXiv:2412.14475},
+  year={2024}
+}
+```