VLM2Vec-V2: Unified Multimodal Embedding for Videos, Images, and Documents

This repository contains the official code and data for VLM2Vec-V2, a unified framework for learning powerful multimodal embeddings across diverse visual formats including images, videos, and visual documents.

Our work introduces MMEB-V2, a comprehensive benchmark with 57 tasks designed to systematically evaluate embedding models across these modalities. VLM2Vec-V2 sets a new state-of-the-art, outperforming strong baselines across all categories.

This is an open-source project, and we welcome contributions from the community. We are particularly interested in additions of new functionalities, support for new datasets, bug fixes, and improvements to documentation. Please feel free to open an issue to discuss your ideas or submit a pull request!

🚨 Major V2 Update Alert (June 2025) 🚨

This repository has been updated to V2, which is a complete overhaul of the codebase. The previous VLM2Vec code has been archived and can be found in the v1 branch.

Warning: Please back up any local work before proceeding. If you have a local clone from before this update, you must reset your main branch to sync with the new code.

For detailed instructions, please see the "How to Upgrade to V2" section below.

Your feedback on this transition process is highly appreciated. If you run into any problems, please let us know by opening an issue.

🔥 News

• [2025-05] VLM2Vec-v2 is released! We introduce a unified embedding framework for images, videos, and visual documents. Our new MMEB-V2 benchmark, featuring 57 diverse tasks, is also available. The VLM2Vec-V2 model outperforms previous versions and strong specialized baselines.
• [2025-01] 🎉 VLM2Vec is accepted to ICLR 2025.

📜 View Older Updates

• [2025-02] 🔥 Two new VLM2Vec models are released, based on Qwen2VL 2B and 7B, achieving 60.1 and 65.8 (new SOTA!) on the MMEB benchmark. Check them out (2B, 7B)!
• [2025-02] We are starting to work on more advanced features and extensions for VLM2Vec, and will document all changes in the CHANGELOG.md. If any changes conflict with previously supported features, please feel free to raise an issue here. Thank you in advance!
• [2025-01] We have updated our training data. Each subset now contains two splits: original and diverse_instruction. The original split is provided to support the reproduction of our paper results. The diverse_instruction split includes paraphrased instructions for each task, designed to enhance instruction diversity and improve the model's robustness to unseen instructions and tasks. Moving forward, our future releases will primarily use the diverse_instruction split.
• [2024-12] We have released the MMEB leaderboard. Feel free to contact us if you want to include your model.
• [2024-12] We have released a new variant of VLM2Vec built on the LLaVa-Next backbone, which is currently our best-performing version: https://huggingface.co/TIGER-Lab/VLM2Vec-LLaVa-Next.
• [2024-10] VLM2Vec has been integrated into vLLM.
• [2024-10] The technical report, code, data, and model for VLM2Vec are all available online.

Key Updates

• Unified framework for training and evaluating embedding models for three modalities of data: images, videos, and visual documents.
• MMEB v2 Benchmark: Extend v1 benchmark with videos and visdoc tasks, including 81 tasks in total.
• VLM2Vec-v2.0: brand-new embedding model based on Qwen2-VL-2B.
• Easy configuration of training and evaluation using yaml files (see examples in experiments/release).
• Easy extension of new datasets by creating and registering customized data loader (see examples in src/dataset/).

How to Upgrade to V2

1. Back Up Your Local Changes (Critical!) The update process will discard any uncommitted changes on your local main branch. If you have work you want to save, commit it to a new branch or use git stash.
2. Reset Your Local Repository to V2. Run the following commands to fetch the new main branch and reset your local copy to match it.

    # Make sure you are on your main branch first
    git checkout main
    # Fetch all recent updates from the remote and remove stale branch references
    git fetch --all --prune
    # Force your local main branch to match the new remote main branch
    git reset --hard origin/main

Model

VLM2Vec-V2 fine-tunes a state-of-the-art Vision-Language Model (VLM) using instruction-guided contrastive training. The model learns to produce a single, powerful fixed-dimensional embedding for any combination of text, image, video, and document inputs.

For current V2 models, we use Qwen2-VL as the model backbone, which capably handles interleaved sequences of text and visuals, variable resolutions, and long-form inputs like videos and visual documents.

Released checkpoints

• VLM2Vec-v2.0 (Qwen2VL-2B): Our primary model, demonstrating strong, balanced performance across all modalities.

V1 checkpoints

• VLM2Vec-Qwen2VL (7B)
• VLM2Vec-Qwen2VL (2B)
• VLM2Vec-LLaVa-Next
• VLM2Vec-Phi3.5V

MMEB-v2 Benchmark

We introduce MMEB-V2, an expanded benchmark that includes 81 total datasets covering images, videos, and visual documents. - New Video Tasks: video retrieval, moment retrieval, video classification, and video QA. - New visual document task: visual document retrieval. - Check out MMEB-v2 datasets at Huggingface.

Data Download

Please refer to experiments/release_public/data/download_data.sh.

Training

Our training process uses a curated dataset from three main sources: video-language data (LLaVA-Hound), visual document data (Vidore, VisRAG), and image-text data (MMEB-train). We use an interleaved sub-batching strategy for stable and effective contrastive learning.

How to run: please see examples in experiments/public/train.

Evaluation

DDP inference on multiple GPUs is supported. The whole evaluation process is streamlined and can be finished within hours.

How to run: please see examples in experiments/public/eval.

Heads-up for Reproducing Baseline Models

• GME: requires an older version of the transformers library <=4.51.3.
• MomentSeeker: we recommend using a single GPU with a batch size of 10. This is due to a limitation in baseline processors that cannot handle mixed batches of image and text-only data.