--- license: apache-2.0 task_categories: - video-text-to-text - visual-question-answering - image-text-to-text language: - en tags: - llava - multimodal - video - spatial-reasoning size_categories: - 10M10-minute video shards | If you need the long-video data, download the video shards from **Part 2** and use the corresponding captions and mapping files from **Part 1**. ## Data Locations ### Part 1: metadata, previews, spatial data, and ~60s videos | Data | Path | Format | Description | |---|---|---|---| | ~60s videos | `mid_training_video/60s_rest/` | WebDataset (`.tar`) | 10,809 video shards | | 30s captions | `mid_training_video/caption_v0/split_30s.jsonl` | JSONL | Captions for 30-second clips | | 60s captions | `mid_training_video/caption_v0/split_60s.jsonl` | JSONL | Captions for 60-second clips | | 180s captions | `mid_training_video/caption_v0/split_180s.jsonl` | JSONL | Captions for the 180-second videos in Part 2 | | >10min captions | `mid_training_video/caption_v0/split_gt10min.jsonl` | JSONL | Captions for the >10-minute videos in Part 2 | | Source mappings | `mid_training_video/mapping/mapping_{5s,10s,30s,60s,180s,gt10min}.csv` | CSV | Maps each clip's `dst_path` to its source `youtube_id` and `[start_time, end_time]` window | | Spatial reasoning | `spatial/` | WebDataset (`.tar`) | 84 shards covering refcoco, Visual Genome, pointing, 3D, and related tasks | ### Part 2: long-video WebDataset shards The directory names in Part 2 are upload prefixes. Their duration mapping is: | Duration | Part 2 directory | Sample path inside each archive | Shards currently present | Approx. size | |---|---|---|---:|---:| | ~180 seconds | [`tom/`](https://huggingface.co/datasets/mvp-lab/LLaVA-OneVision-2-Data-Part2/tree/main/tom) | `180s/...` | 4,199 | 13.6 TB | | >10 minutes | [`jerry/`](https://huggingface.co/datasets/mvp-lab/LLaVA-OneVision-2-Data-Part2/tree/main/jerry) | `gt10min/...` | 21,052 | 70.5 TB | The mapping above was verified from the sample paths stored inside the WebDataset archives. ## How the Files Fit Together For the ~180-second split: 1. Download video shards from `tom/` in Part 2. 2. Read captions from `mid_training_video/caption_v0/split_180s.jsonl` in Part 1. 3. Use `mid_training_video/mapping/mapping_180s.csv` when the source YouTube ID or time window is needed. For the >10-minute split: 1. Download video shards from `jerry/` in Part 2. 2. Read captions from `mid_training_video/caption_v0/split_gt10min.jsonl` in Part 1. 3. Use `mid_training_video/mapping/mapping_gt10min.csv` when the source YouTube ID or time window is needed. ## Downloading the Long-Video Shards Install the Hugging Face CLI, then download only the duration range you need. ```bash # ~180-second videos (4,199 shards, approximately 13.6 TB) hf download mvp-lab/LLaVA-OneVision-2-Data-Part2 \ --repo-type dataset \ --include "tom/**" \ --local-dir ./LLaVA-OneVision-2-Data-Part2 # >10-minute videos (21,052 shards currently present, approximately 70.5 TB) hf download mvp-lab/LLaVA-OneVision-2-Data-Part2 \ --repo-type dataset \ --include "jerry/**" \ --local-dir ./LLaVA-OneVision-2-Data-Part2 ``` The long-video data is very large. Use `--include` to avoid downloading both duration ranges when only one is required. ## Dataset Viewer Previews The `viewer_*` configs expose small Parquet samples for schema inspection in the Hugging Face Dataset Viewer: | Config | Preview contents | |---|---| | `viewer_caption_30s` | 5 caption samples from 30-second clips | | `viewer_caption_60s` | 5 caption samples from 60-second clips | | `viewer_caption_180s` | 3 caption samples from 180-second clips | | `viewer_caption_gt10min` | 1 caption sample from a >10-minute clip | | `viewer_spatial` | 10 spatial-reasoning samples with embedded thumbnails | These configs are for **preview and schema inspection only**. Use the full WebDataset shards for training. ## Part 2 Upload Status The `jerry/` filenames follow a `train_XXXXX_of_21092.tar` naming scheme. At the time of this README update, Part 2 contains 21,052 of those shard files. Check the [Part 2 file browser](https://huggingface.co/datasets/mvp-lab/LLaVA-OneVision-2-Data-Part2/tree/main/jerry) for the latest upload status before starting a full download. ## Citation If you find LLaVA-OneVision-2 useful in your research, please cite the following papers: ```bibtex @misc{an2026llavaonevision2nextgenerationperceptualintelligence, title = {LLaVA-OneVision-2: Towards Next-Generation Perceptual Intelligence}, author = {Xiang An and Yin Xie and Feilong Tang and Yunyao Yan and Huajie Tan and Didi Zhu and Changrui Chen and Xiuwei Zhao and Bin Qin and Kaicheng Yang and Yifei Shen and Yuanhan Zhang and Kaichen Zhang and Wenkang Zhang and Zheng Cheng and Nansen Zhang and Chunsheng Wu and Chunjiang Ge and Zimin Ran and Dehua Song and Chunyuan Li and Shikun Feng and Ming Hu and Zhangquan Chen and Junbo Niu and Bo Li and Ziyong Feng and Ziwei Liu and Zongyuan Ge and Jiankang Deng}, year = {2026}, eprint = {2605.25979}, archivePrefix = {arXiv}, primaryClass = {cs.CV}, url = {https://arxiv.org/abs/2605.25979} } @misc{an2025llavaonevision15fullyopenframework, title = {LLaVA-OneVision-1.5: Fully Open Framework for Democratized Multimodal Training}, author = {Xiang An and Yin Xie and Kaicheng Yang and Wenkang Zhang and Xiuwei Zhao and Zheng Cheng and Yirui Wang and Songcen Xu and Changrui Chen and Didi Zhu and Chunsheng Wu and Huajie Tan and Chunyuan Li and Jing Yang and Jie Yu and Xiyao Wang and Bin Qin and Yumeng Wang and Zizhen Yan and Ziyong Feng and Ziwei Liu and Bo Li and Jiankang Deng}, year = {2025}, eprint = {2509.23661}, archivePrefix = {arXiv}, primaryClass = {cs.CV}, url = {https://arxiv.org/abs/2509.23661} } @misc{tang2026onevisionencodercodecalignedsparsityfoundational, title = {OneVision-Encoder: Codec-Aligned Sparsity as a Foundational Principle for Multimodal Intelligence}, author = {Feilong Tang and Xiang An and Yunyao Yan and Yin Xie and Bin Qin and Kaicheng Yang and Yifei Shen and Yuanhan Zhang and Chunyuan Li and Shikun Feng and Changrui Chen and Huajie Tan and Ming Hu and Manyuan Zhang and Bo Li and Ziyong Feng and Ziwei Liu and Zongyuan Ge and Jiankang Deng}, year = {2026}, eprint = {2602.08683}, archivePrefix = {arXiv}, primaryClass = {cs.CV}, url = {https://arxiv.org/abs/2602.08683} } ```