Datasets:

IIS-CVL
/

EventActivityNet

EventActivityNet v1.0 covers 200 verified action classes. Do not claim 203 classes for this release.
The historical/paper Large duration of 107.3 hours is approximate relative to recovered release metadata. The verified Large duration is 106.94 hours.
Medium and Small are newly generated deterministic nested v1.0 release scales, not historical original subsets.
Durations in the release manifests use src_fmt_dur from verification metadata.
Original subset generation merged train and validation before sampling. Train/validation assignments remain recoverable from ActivityNet Captions split membership.

Recommended Hugging Face Packaging

Preserve per-video HDF5 identity in manifests. For payload upload, tar shards around 20 GB are recommended to balance repository object count, resumability, and user access.

Estimated shard counts from the publication audit:

10 GB target: about 435 shards.
20 GB target: about 218 shards.
50 GB target: about 87 shards.

Final Payload Packaging

157 train tar shards.
62 validation tar shards.
219 total tar shards.
3,263 HDF5 members.
Total remote tar bytes: 4,355,753,021,440.
Final shard checksums are published in metadata/shard_checksums.sha256.

Final Annotation Metadata

Additional public annotation files are provided under annotations/ and metadata/:

annotations/activitynet_captions.json: ActivityNet Captions timestamped natural-language descriptions for release videos. Validation references preserve val_1 and val_2 separately.
annotations/activitynet_actions.json: ActivityNet v1.3 temporal action segments and labels.
annotations/eventactivitynet_alignment.json: EventActivityNet project-derived caption/action alignment generated using temporal IoU with midpoint-distance fallback.
annotations/annotation_issues.jsonl: known upstream annotation quirks recorded without changing canonical values.
metadata/video_metadata.jsonl: original-rate timing metadata, including exact rational FPS where available.

Captions are timestamped descriptions from ActivityNet Captions. They are not instruction-tuning examples.

Timing Clarification

EventActivityNet v1.0 uses original-rate, variable-FPS ActivityNet videos. The released HDF5 files were not generated from anet_240fps_v2 or anet_240fps_old.

Implementation-derived timing:

one event slice is generated for each adjacent decoded source-frame transition (e, e + 1);
one full events[t] tensor groups five adjacent-frame transitions and has shape (5, H, W);
for source frame count N, events_T = ceil((N - 1) / 5);
voxel t covers event-slice range [5*t, 5*t + 5), clipped to available transitions [0, N - 1);
the corresponding source-frame interval is [5*t, min(5*t + 5, N - 1)];
with rational FPS fps_num / fps_den, the approximate seconds interval is [5*t * fps_den / fps_num, min(5*t + 5, N - 1) * fps_den / fps_num];
the final voxel may be partial, with voxel_event_count[t] smaller than 5;
HDF5 root fps is the original source-frame FPS captured by OpenCV, not a 240 fps derivative;
voxel_event_start[t] is the generated event-slice start index for voxel t;
voxel_event_count[t] is the number of generated adjacent-frame slices in voxel t;
voxel_event_start and voxel_event_count are not timestamps and not pixel-event counts.

For caption/action interval [start_seconds, end_seconds], use original FPS to compute:

start_frame = floor(start_seconds * fps_num / fps_den)
end_frame = ceil(end_seconds * fps_num / fps_den)
t_start = max(0, floor(start_frame / 5))
t_end_exclusive = min(events_T, ceil(end_frame / 5))

Use [t_start, t_end_exclusive) for Python slicing, or [t_start, t_end_exclusive - 1] as an inclusive range when non-empty. Do not use time_seconds = t / fps for voxel starts; voxel start time is approximately 5 * t / fps.