README.md

---
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
---

![EventLens — training data and hierarchical annotations](assets/eventlens-cover.png)

**[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

![Representative frames from three real EventLens records](assets/eventlens-examples.png)

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.