---
license: cc-by-nc-4.0
task_categories:
  - video-classification
tags:
  - event-boundary-detection
  - online-video
  - kinetics-400
  - causal-inference
size_categories:
  - 10K<n<100K
pretty_name: Audited Kinetics-GEBD Causal Metadata
---

# Audited Kinetics-GEBD Causal Metadata

This metadata-only release converts the publicly released Kinetics-GEBD
annotations into an auditable 24 FPS causal training representation. It does
not redistribute Kinetics or YouTube video bytes.

## Splits

| Hub split | Official source file | Records | Meaning |
|---|---|---:|---|
| `train` | `k400_train_raw_annotation.pkl` | 18,808 | Public GEBD training annotations |
| `validation` | `k400_val_raw_annotation.pkl` | 18,815 | Public Kinetics-GEBD validation annotations |

The ICCV 2021 benchmark describes a separate challenge `test` subset sampled
from Kinetics-400 train. Its labels are withheld and are not included here.
The ICCV 2025 On-GEBD paper explicitly evaluates Kinetics-GEBD on the public
validation split because test annotations are unavailable.

## Annotation processing

The release reproduces the official
[`prepare_k400_release.ipynb`](https://github.com/StanLei52/GEBD/blob/5f7e722e0384f9877c75d116e1db72400d2bc58f/data/export/prepare_k400_release.ipynb)
behavior, including its input-order and list-mutation quirks:

- discard boundaries in the first or last 0.3 seconds;
- merge overlapping gradual-shot ranges;
- suppress timestamp boundaries around gradual-shot ranges;
- suppress nearby same-type timestamps and prefer shots over events;
- convert cleaned timestamps to source-frame indices with
  `floor(timestamp * source_fps)`, capped at `num_frames - 1`.

The implementation was differentially checked against the official notebook
over all 185,518 released rater annotations with zero timestamp-sequence
differences. This is intentionally the code behavior: some effective
suppression distances are 0.6 seconds even though the paper summarizes a
0.1-second merge rule.

Training uses the first maximum of `f1_consis`, matching `numpy.argmax` in the
official loader. Evaluation must compare predictions independently with every
retained rater and select the best rater-specific F1. Records with
`f1_consis_avg < 0.3` remain present but have `evaluation_eligible=false`.

## Causal 24 FPS labels

The benchmark does not publish a canonical 24 FPS binary-label file. This
release derives one for causal training:

- `sample_count = ceil(duration_seconds * 24)`;
- sample `i` represents timestamp `i / 24`;
- a sample is positive when it falls inclusively within 0.15 seconds of a
  cleaned boundary from the highest-consistency training rater.

`boundary_source_frame_indices` and
`training_boundary_source_frame_indices` preserve official source-FPS frame
coordinates. `training_boundary_sample_indices` and `binary_labels` are the
derived 24 FPS projection. These fields must not be conflated.

## Evaluation

The official metric is relative temporal distance, not the 0.15-second
training window. The paper reports F1 at thresholds 0.05 through 0.50; the
challenge ranking metric is F1@0.05. Matching is one-to-one and performed
separately for each rater, retaining the rater yielding the best F1.

## Videos

The local acquisition audit resolved and `ffprobe`-validated 37,608 of 37,623
referenced clips:

| Source split | Available | Missing |
|---|---:|---:|
| train | 18,794 | 14 |
| val | 18,814 | 1 |

The remaining 15 references were absent from all inspected public mirrors,
the official CVDF Kinetics-400 archives, and accessible original-video paths.
See [`VIDEO_ACQUISITION.md`](VIDEO_ACQUISITION.md) and `manifests/` for exact
availability and missing-reference records.

```bash
uv run scripts/build_official_on_gebd_dataset.py
uv run scripts/acquire_official_gebd_videos.py --workers 4
```

## Rights

The GEBD annotation release is CC BY-NC 4.0 and the official repository code
is MIT-licensed. Neither license grants redistribution rights for the
underlying Kinetics/YouTube videos. A gated or private Hub repository does not
change those rights, so this release contains metadata and manifests only.

## Sources

- [Generic Event Boundary Detection, ICCV 2021](https://openaccess.thecvf.com/content/ICCV2021/papers/Shou_Generic_Event_Boundary_Detection_A_Benchmark_for_Event_Segmentation_ICCV_2021_paper.pdf)
- [Online Generic Event Boundary Detection, ICCV 2025](https://openaccess.thecvf.com/content/ICCV2025/papers/Jung_Online_Generic_Event_Boundary_Detection_ICCV_2025_paper.pdf)
- [Official GEBD repository](https://github.com/StanLei52/GEBD/tree/5f7e722e0384f9877c75d116e1db72400d2bc58f)
- [Official challenge evaluator](https://github.com/StanLei52/GEBD/blob/5f7e722e0384f9877c75d116e1db72400d2bc58f/Challenge_eval_Code/eval.py)
- [Official Kinetics downloads](https://github.com/cvdfoundation/kinetics-dataset)