Datasets:
Tasks:
Visual Question Answering
Formats:
parquet
Languages:
English
Size:
100K - 1M
Tags:
video
multimodal
event-understanding
temporal-grounding
hierarchical-annotations
post-training
License:
| pretty_name: EventLens | |
| license: cc-by-nc-sa-4.0 | |
| language: | |
| - en | |
| size_categories: | |
| - 10K<n<100K | |
| task_categories: | |
| - visual-question-answering | |
| tags: | |
| - video | |
| - multimodal | |
| - event-understanding | |
| - temporal-grounding | |
| - hierarchical-annotations | |
| - post-training | |
| - noncommercial | |
| configs: | |
| - config_name: paper_mt_opd | |
| default: true | |
| data_files: | |
| - split: train | |
| path: train/paper_mt_opd/part-*.parquet | |
| - split: monitor_val | |
| path: val/paper_mt_opd/part-*.parquet | |
| - config_name: paper_seg_teacher | |
| data_files: | |
| - split: train | |
| path: train/paper_seg_teacher/part-*.parquet | |
| - split: monitor_val | |
| path: val/paper_seg_teacher/part-*.parquet | |
| - config_name: paper_prog_teacher | |
| data_files: | |
| - split: train | |
| path: train/paper_prog_teacher/part-*.parquet | |
| - split: monitor_val | |
| path: val/paper_prog_teacher/part-*.parquet | |
| - config_name: paper_relation_teacher | |
| data_files: | |
| - split: train | |
| path: train/paper_relation_teacher/part-*.parquet | |
| - split: monitor_val | |
| path: val/paper_relation_teacher/part-*.parquet | |
| - config_name: tg | |
| data_files: | |
| - split: train | |
| path: train/tg/part-*.parquet | |
| - split: monitor_val | |
| path: val/tg/part-*.parquet | |
| - config_name: mcq | |
| data_files: | |
| - split: train | |
| path: train/mcq/part-*.parquet | |
| - split: monitor_val | |
| path: val/mcq/part-*.parquet | |
| - config_name: seg | |
| data_files: | |
| - split: train | |
| path: train/seg/part-*.parquet | |
| - split: monitor_val | |
| path: val/seg/part-*.parquet | |
| - config_name: prog | |
| data_files: | |
| - split: train | |
| path: train/prog/part-*.parquet | |
| - split: monitor_val | |
| path: val/prog/part-*.parquet | |
| - config_name: relation | |
| data_files: | |
| - split: train | |
| path: train/relation/part-*.parquet | |
| - split: monitor_val | |
| path: val/relation/part-*.parquet | |
| - config_name: annotations_hier_v1 | |
| data_files: | |
| - split: annotations | |
| path: annotations/hier_v1/part-*.parquet | |
| - config_name: annotations_hier_legacy | |
| data_files: | |
| - split: annotations | |
| path: annotations/hier_legacy/part-*.parquet | |
|  | |
| **[Quick start](#quick-start) · [Training views](#training-views) · [Annotations & media](#annotations--media)** | |
| EventLens provides **event-based video supervision** for boundaries, progression, and relations, alongside temporal grounding and video question answering. This release brings together frozen **no-CoT training views**, original **L1/L2/L3 hierarchy annotations**, and the **shared JPEG frames** used by those training views. | |
| | Training examples¹ | Monitor examples | Original annotation files | Shared frames | | |
| | ---: | ---: | ---: | ---: | | |
| | **37,428** | **1,950** | **11,061** | **2,921,513** | | |
| ¹ Counts refer to the default `paper_mt_opd` view. Other configurations reuse examples and media; their counts are **not additive**. | |
| ## Video domains | |
| <div align="center"> | |
| <img src="assets/data-domains.png" alt="Source-video domain distribution from the EventLens paper appendix: food and culinary, lifestyle and daily activities, engineering and mechanics, sports and outdoors, culture and media, and general domain." width="620"> | |
| </div> | |
| *Source-video domain distribution reproduced from the paper appendix. The figure describes videos used to construct the recovery tasks.* | |
| ## Task families | |
| | Task | What the model learns | Training examples | Config | | |
| | --- | --- | ---: | --- | | |
| | **Boundary** | Locate event boundaries at different hierarchy levels | 10,000 | `seg` | | |
| | **Progression** | Recognize the temporal progression of actions | 10,000 | `prog` | | |
| | **Relation** | Recover event order and relationships between clips | 10,000 | `relation` | | |
| | Temporal grounding | Locate a text-described moment in a video | 4,791 | `tg` | | |
| | Video QA | Answer multiple-choice questions about video content | 2,637 | `mcq` | | |
| ### A glimpse of the data | |
|  | |
| These are three actual records from the frozen release. Answers retain their original format. | |
| | Family | Source video ID | Model input | Ground truth | | |
| | --- | --- | --- | --- | | |
| | Boundary | `ZccajuCybns` | One input with 61 frames | Six temporal intervals, wrapped in `<events>` tags | | |
| | Progression | `UAR9gmFr_m8` | Two inputs with 4 frames each | `B` | | |
| | Relation | `2rkEYETtXH8` | Three inputs with 2 frames each | `213` | | |
| ## Quick start | |
| ```python | |
| from datasets import load_dataset | |
| REPO = "Lostgreen/EventLens-Train" | |
| REVISION = "main" # Use a commit SHA to pin an experiment. | |
| train = load_dataset(REPO, "paper_mt_opd", split="train", revision=REVISION) | |
| monitor = load_dataset(REPO, "paper_mt_opd", split="monitor_val", revision=REVISION) | |
| annotations = load_dataset( | |
| REPO, "annotations_hier_v1", split="annotations", revision=REVISION | |
| ) | |
| ``` | |
| This loads records and media references. To train with the JPEGs, download and unpack the shared frame shards described below. | |
| ## Training views | |
| Choose `paper_mt_opd` for the full mix, a specialist view for teacher training, or a task config for an individual family. | |
| | Config | Contents | Train | Monitor | | |
| | --- | --- | ---: | ---: | | |
| | **`paper_mt_opd`** | Grounding + QA + Boundary + Progression + Relation | **37,428** | **1,950** | | |
| | `paper_seg_teacher` | Grounding + QA + Boundary | 17,428 | 1,350 | | |
| | `paper_prog_teacher` | Grounding + QA + Progression | 17,428 | 1,500 | | |
| | `paper_relation_teacher` | Grounding + QA + Relation | 17,428 | 1,500 | | |
| | `tg` | Temporal grounding | 4,791 | 600 | | |
| | `mcq` | Video QA | 2,637 | 600 | | |
| | `seg` | Boundary | 10,000 | 150 | | |
| | `prog` | Progression | 10,000 | 300 | | |
| | `relation` | Relation | 10,000 | 300 | | |
| > **Split meaning:** `monitor_val` preserves the historical training monitor and overlaps with training. It is not an independent validation or test split. The repository stores it under `val/`. | |
| ## Annotations & media | |
| The repository separates **training records**, **original annotations**, and **shared frames**. Train, monitor, and specialist views all reference the same media files. | |
| ```text | |
| EventLens-Train/ | |
| ├── train/<config>/ Parquet + training.jsonl.gz | |
| ├── val/<config>/ Parquet + training.jsonl.gz | |
| ├── annotations/ | |
| │ ├── hier_v1/ Parquet + original_json.tar.gz | |
| │ └── hier_legacy/ Parquet + original_json.tar.gz | |
| ├── media/shards/ 37 shared frame TARs | |
| ├── metadata/ Video links, frame indexes, hashes & audits | |
| └── tools/ Training converter + schema | |
| ``` | |
| Unpacking `media/shards/*.tar` at the repository root creates `media/frames/`. | |
| ### Original hierarchy annotations | |
| | Config | Split | JSON files | Source composition | | |
| | --- | --- | ---: | --- | | |
| | `annotations_hier_v1` | `annotations` | 9,738 | ET-Instruct | | |
| | `annotations_hier_legacy` | `annotations` | 1,323 | 792 ET-Instruct + 531 TimeLens | | |
| The collections preserve historical annotation versions. Labels, time intervals, and descriptions match the original JSONs; machine-specific `video_path`, `source_video_path`, and `frame_dir` values are replaced with portable provenance references. Parquet stores the original hierarchy in `payload_json`; the archives retain the original JSON filenames. | |
| **Join at the video level:** use `source_dataset + source_video_key` from `metadata/video_links.jsonl`. Each of the **11,061 annotation IDs** has one unambiguous source-video owner. Four readable clip IDs refer to multiple source paths, so full source keys are required to disambiguate them. | |
| | Annotation source | Unique annotated videos | Have training frames | Outside training media | | |
| | --- | ---: | ---: | ---: | | |
| | ET-Instruct | 10,035 | 9,108 | 927 | | |
| | TimeLens | 531 | 1 | 530 | | |
| All **30,750 hierarchy-derived train/monitor examples** link to original hierarchy annotations. TG/QA records retain their upstream supervision and source-video provenance; they do not all have EventLens hierarchy labels. Original annotations also include videos not used by the training views. | |
| ### Shared frame media | |
| The **2,921,513 referenced JPEGs** are packaged into **37 TAR shards**, with **71.07 GiB of JPEG payload** before archive overhead. Frame indexes record the source-video key, TAR member, byte size, and SHA256; the shard manifest records each TAR's SHA256. | |
| ```python | |
| from huggingface_hub import snapshot_download | |
| snapshot_download( | |
| REPO, repo_type="dataset", revision=REVISION, local_dir="EventLens-Train" | |
| ) | |
| ``` | |
| ```bash | |
| DATASET_ROOT=/path/to/EventLens-Train | |
| for shard in "$DATASET_ROOT"/media/shards/*.tar; do | |
| tar -xf "$shard" -C "$DATASET_ROOT" | |
| done | |
| ``` | |
| Training inputs preserve frozen frame sampling, multiple-clip boundaries, and frame order, including reordered action blocks. The bundled media are sampled training frames; native video binaries and annotation-process frame folders are referenced through provenance and are not bundled. Optional source/presentation timestamps remain `null`, and cache frame indices are not native-video frame numbers. | |
| ## Use with the training code | |
| Each training view includes a portable runtime JSONL alongside its typed Parquet: | |
| ```bash | |
| gzip -dc "$DATASET_ROOT/train/paper_mt_opd/training.jsonl.gz" > train.jsonl | |
| gzip -dc "$DATASET_ROOT/val/paper_mt_opd/training.jsonl.gz" > val.jsonl | |
| ``` | |
| Set EasyR1's **`data.image_dir` to `DATASET_ROOT`** so relative nested frame lists resolve to the extracted media. Alternatively, convert a Hub split with the included script: | |
| ```bash | |
| python "$DATASET_ROOT/tools/convert_to_training_jsonl.py" \ | |
| --input Lostgreen/EventLens-Train --config paper_mt_opd --split train \ | |
| --prompt-mode no_cot --media-root "$DATASET_ROOT" --check-media \ | |
| --output train.jsonl | |
| ``` | |
| Pin `--revision` to the same commit used for the media download when reproducing an experiment. The conversion preserves `prompt`, `messages`, `answer`, `problem_type`, `data_type`, video grouping, and frame order. | |
| ### Fields at a glance | |
| | Field | Meaning | | |
| | --- | --- | | |
| | `sample_id` / `row_id` | Stable content identity / occurrence within a frozen view | | |
| | `task_family` / `problem_type` | Human-readable task family / exact training and reward routing | | |
| | `prompt` / `messages` / `answer` | Model input and original ground truth | | |
| | `media[].frame_keys` | Ordered relative JPEG paths for each model input | | |
| | `media[].source_video_id` | Readable upstream video identifier | | |
| | `annotation_id` | Primary hierarchy annotation link where available | | |
| | `training_metadata_json` | Portable original timing, construction, and reward metadata | | |
| See [the typed schema](metadata/schema.json) for the complete field definitions. `sources/` and `provenance/` are source references rather than downloadable payload directories. | |
| ## Verification & provenance | |
| The frozen default view was audited across all train/monitor references: every referenced JPEG exists, frame cache directories have consistent video provenance, and every hierarchy-derived row links to original annotations. All 11,061 published annotation JSONs were compared with their originals with zero semantic changes. | |
| A 512-record conversion preflight covered all 12 problem types, and all 9 training plus 2 annotation configs passed standard `load_dataset` checks. Detailed coverage and content checks are included in `metadata/media_coverage.json` and `metadata/annotation_content_verification.json`; media hashes are in `metadata/media_shards.json` and `metadata/media_index/`. | |
| This is a frozen release. Upstream source revisions were not recorded; those values remain unknown rather than being inferred. | |
| ## Sources & license | |
| **Noncommercial use.** EventLens-derived hierarchy annotations and proxy labels use **[CC-BY-NC-SA-4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/)**. Source-specific terms continue to apply to third-party records and media; see [LICENSES.md](LICENSES.md). | |
| | Upstream source | Link | Terms | | |
| | --- | --- | --- | | |
| | ET-Instruct-164K | [Dataset](https://huggingface.co/datasets/PolyU-ChenLab/ET-Instruct-164K) | CC-BY-NC-SA-4.0 | | |
| | TimeLens-100K | [Dataset](https://huggingface.co/datasets/TencentARC/TimeLens-100K) | [Custom academic/noncommercial terms](licenses/TimeLens-LICENSE.txt), including the upstream EU restriction | | |
| | TimeRFT / TimeR1 | [Project](https://github.com/xiaomi-research/time-r1) | Upstream and constituent dataset terms | | |
| | LLaVA-Video-178K | [Dataset](https://huggingface.co/datasets/lmms-lab/LLaVA-Video-178K) | Upstream and constituent media terms | | |
| The code license is separate from the data and media terms. | |