| --- |
| dataset_info: |
| - config_name: candidates |
| features: |
| - name: did |
| dtype: string |
| - name: text |
| list: string |
| - name: media_id |
| dtype: string |
| splits: |
| - name: train |
| num_bytes: 931441504 |
| num_examples: 7119841 |
| download_size: 499532524 |
| dataset_size: 931441504 |
| - config_name: instructions |
| features: |
| - name: task_id |
| dtype: int32 |
| - name: dataset_id |
| dtype: int32 |
| - name: prompts |
| dtype: string |
| splits: |
| - name: train |
| num_bytes: 28058 |
| num_examples: 58 |
| download_size: 11004 |
| dataset_size: 28058 |
| - config_name: media_shard_000000 |
| features: |
| - name: media_id |
| dtype: string |
| - name: image |
| dtype: image |
| - name: video |
| list: image |
| - name: audio |
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| - name: has_audio |
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| num_examples: 200000 |
| download_size: 122853793676 |
| dataset_size: 122849524199 |
| - config_name: media_shard_000001 |
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| - name: media_id |
| dtype: string |
| - name: image |
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| - name: train |
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| num_examples: 200000 |
| download_size: 117988606222 |
| dataset_size: 117985237498 |
| - config_name: media_shard_000002 |
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| - name: media_id |
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| num_examples: 200000 |
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| - config_name: media_shard_000003 |
| features: |
| - name: media_id |
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| - name: image |
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| num_examples: 200000 |
| download_size: 117769664261 |
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| - config_name: media_shard_000004 |
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| num_examples: 200000 |
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| - config_name: media_shard_000005 |
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| - config_name: media_shard_000006 |
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| num_examples: 200000 |
| download_size: 117643189579 |
| dataset_size: 117639713891 |
| - config_name: media_shard_000007 |
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| num_examples: 200000 |
| download_size: 117431098509 |
| dataset_size: 117427707334 |
| - config_name: media_shard_000008 |
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| num_examples: 200000 |
| download_size: 118046142117 |
| dataset_size: 118041395077 |
| - config_name: media_shard_000009 |
| features: |
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| num_examples: 200000 |
| download_size: 117728340239 |
| dataset_size: 117724310015 |
| - config_name: media_shard_000010 |
| features: |
| - name: media_id |
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| num_examples: 200000 |
| download_size: 117395658377 |
| dataset_size: 117390521616 |
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| download_size: 117444566123 |
| dataset_size: 117439367419 |
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| num_examples: 200000 |
| download_size: 118449572108 |
| dataset_size: 118444042857 |
| - config_name: media_shard_000013 |
| features: |
| - name: media_id |
| dtype: string |
| - name: image |
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| list: image |
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| - name: has_audio |
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| splits: |
| - name: train |
| num_bytes: 117590368881 |
| num_examples: 200000 |
| download_size: 117594645617 |
| dataset_size: 117590368881 |
| - config_name: media_shard_000014 |
| features: |
| - name: media_id |
| dtype: string |
| - name: image |
| dtype: image |
| - name: video |
| list: image |
| - name: audio |
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| - name: has_audio |
| dtype: bool |
| splits: |
| - name: train |
| num_bytes: 117985815582 |
| num_examples: 200000 |
| download_size: 117990857457 |
| dataset_size: 117985815582 |
| - config_name: media_shard_000015 |
| features: |
| - name: media_id |
| dtype: string |
| - name: image |
| dtype: image |
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| list: image |
| - name: audio |
| dtype: binary |
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| dtype: bool |
| splits: |
| - name: train |
| num_bytes: 117263260177 |
| num_examples: 200000 |
| download_size: 117267034089 |
| dataset_size: 117263260177 |
| - config_name: media_shard_000016 |
| features: |
| - name: media_id |
| dtype: string |
| - name: image |
| dtype: image |
| - name: video |
| list: image |
| - name: audio |
| dtype: binary |
| - name: has_audio |
| dtype: bool |
| splits: |
| - name: train |
| num_bytes: 117574792839 |
| num_examples: 200000 |
| download_size: 117579302317 |
| dataset_size: 117574792839 |
| - config_name: media_shard_000017 |
| features: |
| - name: media_id |
| dtype: string |
| - name: image |
| dtype: image |
| - name: video |
| list: image |
| - name: audio |
| dtype: binary |
| - name: has_audio |
| dtype: bool |
| splits: |
| - name: train |
| num_bytes: 81693044328 |
| num_examples: 138323 |
| download_size: 81696151897 |
| dataset_size: 81693044328 |
| - config_name: queries |
| features: |
| - name: id |
| dtype: string |
| - name: task_id |
| dtype: int32 |
| - name: text |
| list: string |
| - name: media_id |
| dtype: string |
| - name: candidates |
| list: string |
| splits: |
| - name: train |
| num_bytes: 834160494 |
| num_examples: 6405109 |
| download_size: 366840294 |
| dataset_size: 834160494 |
| configs: |
| - config_name: candidates |
| data_files: |
| - split: train |
| path: candidates/train-* |
| - config_name: instructions |
| data_files: |
| - split: train |
| path: instructions/train-* |
| - config_name: media_shard_000000 |
| data_files: |
| - split: train |
| path: media_shard_000000/train-* |
| - config_name: media_shard_000001 |
| data_files: |
| - split: train |
| path: media_shard_000001/train-* |
| - config_name: media_shard_000002 |
| data_files: |
| - split: train |
| path: media_shard_000002/train-* |
| - config_name: media_shard_000003 |
| data_files: |
| - split: train |
| path: media_shard_000003/train-* |
| - config_name: media_shard_000004 |
| data_files: |
| - split: train |
| path: media_shard_000004/train-* |
| - config_name: media_shard_000005 |
| data_files: |
| - split: train |
| path: media_shard_000005/train-* |
| - config_name: media_shard_000006 |
| data_files: |
| - split: train |
| path: media_shard_000006/train-* |
| - config_name: media_shard_000007 |
| data_files: |
| - split: train |
| path: media_shard_000007/train-* |
| - config_name: media_shard_000008 |
| data_files: |
| - split: train |
| path: media_shard_000008/train-* |
| - config_name: media_shard_000009 |
| data_files: |
| - split: train |
| path: media_shard_000009/train-* |
| - config_name: media_shard_000010 |
| data_files: |
| - split: train |
| path: media_shard_000010/train-* |
| - config_name: media_shard_000011 |
| data_files: |
| - split: train |
| path: media_shard_000011/train-* |
| - config_name: media_shard_000012 |
| data_files: |
| - split: train |
| path: media_shard_000012/train-* |
| - config_name: media_shard_000013 |
| data_files: |
| - split: train |
| path: media_shard_000013/train-* |
| - config_name: media_shard_000014 |
| data_files: |
| - split: train |
| path: media_shard_000014/train-* |
| - config_name: media_shard_000015 |
| data_files: |
| - split: train |
| path: media_shard_000015/train-* |
| - config_name: media_shard_000016 |
| data_files: |
| - split: train |
| path: media_shard_000016/train-* |
| - config_name: media_shard_000017 |
| data_files: |
| - split: train |
| path: media_shard_000017/train-* |
| - config_name: queries |
| data_files: |
| - split: train |
| path: queries/train-* |
| --- |
| |
| # OmniRet training dataset |
|
|
| `OmniRet-train` is the training-data release for |
| [OmniRet](https://arxiv.org/abs/2603.02098), a unified retrieval model for |
| text, image, video, and audio. This card documents the released snapshot for |
| researchers training or analyzing OmniRet. |
|
|
| ## Dataset summary |
|
|
| The release contains 6,405,109 query rows and 7,119,841 candidate rows from 30 |
| datasets. It covers 15 retrieval directions across text (T), image (I), video |
| (V), and audio (A). The OmniRet paper reports this corpus as approximately 6.4 |
| million query-candidate pairs. |
|
|
| ## Provenance |
|
|
| OmniRet starts from the |
| [M-BEIR](https://huggingface.co/datasets/TIGER-Lab/M-BEIR) universal retrieval |
| benchmark and extends it with text, image-text, video-text, audio-text, and |
| audio-visual training data. The source groups follow the training-data section |
| and Table 10 of the [OmniRet paper](https://arxiv.org/abs/2603.02098). |
|
|
| | Source group | Datasets | |
| | --- | --- | |
| | M-BEIR foundation | NIGHTS, WebQA, VisualNews, Fashion200K, MSCOCO, EDIS, OVEN, InfoSeek, FashionIQ, CIRR | |
| | Added text retrieval | MS MARCO, HotpotQA, Natural Questions, PAQ, StackExchange, NLI, SQuAD | |
| | Added image-text and composed image retrieval | LLaVA-558K, CC-CoIR, MTCIR | |
| | Added video-text and composed video retrieval | TGIF, Charades, WebVid2M, PE-Video, WebCoVR | |
| | Added audio-text retrieval | AudioCaps, Clotho v2.1, WavText5K, WavCaps | |
| | Added audio-visual retrieval | VGGSound | |
|
|
| ## Retrieval tasks |
|
|
| | Family | Directions | |
| | --- | --- | |
| | Unimodal | I → I; T → T | |
| | Cross-modal binding | I → T; T → I; V → T; T → V; A → T; T → A | |
| | Composed retrieval | T → (I,T); (I,T) → T; (I,T) → I; (I,T) → (I,T); (V,T) → V | |
| | Audio-visual binding | A → V; V → A | |
|
|
| ## Repository layout |
|
|
| | Path | Contents | |
| | --- | --- | |
| | `queries/` | Training queries, task IDs, positive candidate IDs, text, and media references | |
| | `candidates/` | Candidate records referenced by the queries | |
| | `instructions/` | Retrieval instructions keyed by task and dataset | |
| | `media_shard_*/` | Sharded image, video, and audio payloads | |
| | `metadata/candidate_index.sqlite` | Read-only lookup index for 7,119,841 candidates | |
| | `metadata/media_index.sqlite` | Read-only lookup index for 3,538,323 media IDs across 1,976 Parquet files | |
| | `metadata/train_pairs.jsonl` | Precomputed manifest containing 6,405,109 training pairs | |
|
|
| Download the repository to the canonical training path: |
|
|
| ```bash |
| hf download chuonghm/OmniRet-train \ |
| --repo-type dataset \ |
| --local-dir /data1/omniret/OmniRet-train-data |
| ``` |
|
|
| The published media index stores Parquet paths below |
| `/data1/omniret/OmniRet-train-data`. Use that location, or provide a compatible |
| media index when using another root. |
|
|
| ## Relationship to the ACM benchmark |
|
|
| The OmniRet paper also introduces the separate |
| [Audio-Centric Multimodal benchmark (ACM)](https://huggingface.co/datasets/chuonghm/ACM), |
| curated from VGGSound. ACM evaluates composed audio retrieval (A,T → A) and |
| bidirectional audio-image and audio-video retrieval (A → I, I → A, A → V, |
| V → A). Use `chuonghm/ACM` for evaluation; this training repository does not |
| replace the benchmark release. |
|
|
| ## Data terms |
|
|
| This repository is a transformed compilation of upstream datasets. Underlying |
| examples remain subject to their original licenses and terms. Review the |
| [M-BEIR dataset card](https://huggingface.co/datasets/TIGER-Lab/M-BEIR) and the |
| source references in Table 10 of the |
| [OmniRet paper](https://arxiv.org/abs/2603.02098) before redistribution or |
| commercial use. |
|
|
| ## Citation |
|
|
| Please cite OmniRet for this extended training release and ACM benchmark, and |
| cite UniIR for the M-BEIR foundation. |
|
|
| ```bibtex |
| @article{huynh2026omniret, |
| title = {Efficient and High-Fidelity Omni Modality Retrieval}, |
| author = {Huynh, Chuong and Luong, Manh and Shrivastava, Abhinav}, |
| journal = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, |
| year = {2026} |
| } |
| |
| @article{wei2023uniir, |
| title = {UniIR: Training and Benchmarking Universal Multimodal Information Retrievers}, |
| author = {Wei, Cong and Chen, Yang and Chen, Haonan and Hu, Hexiang and Zhang, Ge and Fu, Jie and Ritter, Alan and Chen, Wenhu}, |
| journal = {arXiv preprint arXiv:2311.17136}, |
| year = {2023} |
| } |
| ``` |
|
|
