| --- |
| license: mit |
| task_categories: |
| - feature-extraction |
| - image-to-text |
| - text-to-image |
| tags: |
| - multimodal-embedding |
| - retrieval |
| - benchmark |
| - text |
| - image |
| - video |
| - audio |
| - visual-document |
| - agent |
| pretty_name: MMEB-V3 |
| --- |
| |
| # MMEB-V3 |
|
|
| MMEB-V3 is an omni-modality embedding benchmark for evaluating retrieval and representation models across text, image, video, audio, visual document, and agent-centric tasks. It extends MMEB-V1/V2 and provides a unified local layout for running the evaluation code in [VLM2Vec](https://github.com/TIGER-AI-Lab/VLM2Vec). |
|
|
| ## Download |
|
|
| ```bash |
| export MMEB_V3_ROOT=/path/to/MMEB-V3 |
| hf download VLM2Vec/MMEB-V3 \ |
| --repo-type dataset \ |
| --local-dir $MMEB_V3_ROOT |
| ``` |
|
|
| ## Prepare Data |
|
|
| The uploaded files keep compressed raw assets under `_tasks` directories. Run the setup script in the VLM2Vec repo to materialize the evaluation-ready `-tasks` directories: |
|
|
| ```bash |
| python experiments/public/data/dataset_setup_v3.py --root $MMEB_V3_ROOT |
| python experiments/public/data/dataset_setup_v3.py --root $MMEB_V3_ROOT --check-only |
| ``` |
|
|
| Raw archive layout: |
|
|
| ```text |
| MMEB-V3/ |
| image_tasks/ |
| audio_tasks/ |
| video_tasks/ |
| visdoc_tasks/ |
| gui_tasks/ |
| memory_tasks/ |
| text_tasks/ |
| tool_tasks/ |
| omniset.tar.gz |
| ``` |
|
|
| Expected evaluation-ready layout after setup: |
|
|
| ```text |
| MMEB-V3/ |
| image-tasks/ |
| MMEB/ |
| MCMR/ |
| image-query/ |
| audio-tasks/ |
| video-tasks/ |
| data/ |
| frames/ |
| video_cls/ |
| video_ret/ |
| video_mret/ |
| video_qa/ |
| visdoc-tasks/ |
| data/ |
| images/ |
| text-tasks/ |
| tool-tasks/ |
| memory-tasks/ |
| gui-tasks/ |
| omniset/ |
| omniset.jsonl |
| catalog.jsonl |
| val2014/ |
| videos/ |
| audios/ |
| frames_omni/ |
| ``` |
|
|
| ## Evaluation |
|
|
| For standard MMEB-V3 tasks, pass the dataset root to `--data_basedir`: |
|
|
| ```bash |
| CUDA_VISIBLE_DEVICES=0 python eval.py \ |
| --pooling mean \ |
| --normalize true \ |
| --per_device_eval_batch_size 8 \ |
| --dataloader_num_workers 1 \ |
| --model_backbone nvomniembed \ |
| --model_name /path/to/model \ |
| --dataset_config experiments/public/eval/image.yaml \ |
| --encode_output_path exps/vlm2vec/model/image \ |
| --data_basedir $MMEB_V3_ROOT |
| ``` |
|
|
| For OmniSET: |
|
|
| ```bash |
| CUDA_VISIBLE_DEVICES=0 \ |
| MODEL_PATH=/path/to/model \ |
| MODEL_BACKBONE=nvomniembed \ |
| DATA_BASEDIR=$MMEB_V3_ROOT/omniset \ |
| OUTPUT_PATH=exps/vlm2vec/model/omniset \ |
| PER_DEVICE_EVAL_BATCH_SIZE=8 \ |
| bash experiments/public/eval/eval_omniset.sh |
| ``` |
|
|
|
|
