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README.md

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+---
+license: apache-2.0
+task_categories:
+- visual-question-answering
+- video-classification
+language:
+- en
+viewer: false
+configs:
+- config_name: splits
+  data_files:
+  - split: eval
+    path:
+    - "video_tasks"
+    - "image_tasks"
+---
+
+# MMEB-V2 (Massive Multimodal Embedding Benchmark)
+
+Building upon on our original [**MMEB**](https://arxiv.org/abs/2410.05160), **MMEB-V2** expands the evaluation scope to include five new tasks: four video-based tasks — Video Retrieval, Moment Retrieval, Video Classification, and Video Question Answering — and one task focused on visual documents, Visual Document Retrieval. This comprehensive suite enables robust evaluation of multimodal embedding models across static, temporal, and structured visual data settings.
+
+**This Hugging Face repository contains only the raw image and video files used in MMEB-V2, which need to be downloaded in advance.**
+The test data for each task in MMEB-V2 is available [here](https://huggingface.co/VLM2Vec) and will be automatically downloaded and used by our code. More details on how to set it up are provided in the following sections.
+
+|[**Github**](https://github.com/TIGER-AI-Lab/VLM2Vec) | [**🏆Leaderboard**](https://huggingface.co/spaces/TIGER-Lab/MMEB) | [**📖MMEB-V2/VLM2Vec-V2 Paper (TBA)**](https://arxiv.org/abs/2410.05160) | | [**📖MMEB-V1/VLM2Vec-V1 Paper**](https://arxiv.org/abs/2410.05160) |
+
+
+## 🚀 What's New
+- **\[2025.05\]** Initial release of MMEB-V2.
+
+
+## Dataset Overview
+
+We present an overview of the MMEB-V2 dataset below:
+<img width="900" alt="abs" src="overview.png">
+
+
+## Dataset Structure
+
+The directory structure of this Hugging Face repository is shown below. 
+For video tasks, we provide both sampled frames and raw videos (the latter will be released later). For image tasks, we provide the raw images.
+Files from each meta-task are zipped together, resulting in six files. For example, ``video_cls.tar.gz`` contains the sampled frames for the video classification task.
+
+```
+
+→ video-tasks/
+├── frames/
+│   ├── video_cls.tar.gz
+│   ├── video_qa.tar.gz
+│   ├── video_ret.tar.gz
+│   └── video_mret.tar.gz
+├── raw videos/ (To be released)
+
+→ image-tasks/
+├── mmeb_v1.tar.gz
+└── visdoc.tar.gz
+
+```
+
+After downloading and unzipping these files locally, you can organize them as shown below. (You may choose to use ``Git LFS`` or ``wget`` for downloading.)
+Then, simply specify the correct file path in the configuration file used by your code.
+
+```
+
+→ MMEB
+├── video-tasks/
+│   └── frames/
+│       ├── video_cls/
+│       │   ├── UCF101/
+│       │   │   └── video_1/              # video ID
+│       │   │       ├── frame1.png        # frame from video_1
+│       │   │       ├── frame2.png
+│       │   │       └── ...
+│       │   ├── HMDB51/
+│       │   ├── Breakfast/
+│       │   └── ...                       # other datasets from video classification category
+│       ├── video_qa/
+│       │   └── ...                       # video QA datasets
+│       ├── video_ret/
+│       │   └── ...                       # video retrieval datasets
+│       └── video_mret/
+│           └── ...                       # moment retrieval datasets
+├── image-tasks/
+│   ├── mmeb_v1/
+│   │   ├── OK-VQA/
+│   │   │   ├── image1.png
+│   │   │   ├── image2.png
+│   │   │   └── ...
+│   │   ├── ImageNet-1K/
+│   │   └── ...                           # other datasets from MMEB-V1 category
+│   └── visdoc/
+│       └── ...                           # visual document retrieval datasets
+
+
+```

dataset_setup.py

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+import os
+import subprocess
+
+def extract_video_tasks():
+    root = os.path.join('video_tasks', 'frames')
+    all_bundles = list(filter(lambda x: '.tar.gz' in x, os.listdir(root)))
+    for task in ['cls', 'qa', 'ret', 'mret']:
+        print(f'Extracting {task} task ... ...')
+        output_dir = os.path.join(root, f'video_{task}')
+        os.makedirs(output_dir, exist_ok=True)
+        bundles = sorted(list(filter(lambda x: task in x, all_bundles)))
+        bundles = [os.path.join(root, x) for x in bundles]
+        if len(bundles) > 1:
+            cat = subprocess.Popen(['cat'] + bundles, stdout=subprocess.PIPE)
+            cmd = ['tar', '-xzf', '-', '-C', output_dir]
+            tar = subprocess.Popen(cmd, stdin=cat.stdout)
+            cat_return_code = cat.wait()
+            tar_return_code = tar.wait()
+        elif len(bundles) == 1:
+            tar = subprocess.run(['tar', '-xzf', bundles[0], '-C', output_dir])
+        else:
+            raise ValueError(f'Unexpected size: {len(bundles)}')
+    print('All video tasks completed!')
+
+def extract_image_tasks():
+    root = 'image_tasks'
+    for task in ['mmeb_v1', 'visdoc']:
+        print(f'Extracting {task} task ... ...')
+        tar = subprocess.run(['tar', '-xzf', os.path.join(root, f'{task}.tar.gz'), '-C', root])
+    print('All image tasks completed!')
+
+def main():
+    extract_image_tasks()
+    extract_video_tasks()
+
+if __name__ == '__main__':
+    main()

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