Datasets:

IIS-CVL
/

EventActivityNet

@@ -4,7 +4,7 @@ title: "EventActivityNet"
 version: "1.0"
 date-released: "2026-07-14"
 type: dataset
-abstract: "EventActivityNet v1.0 is a 5-bin HDF5 event voxel representation of a curated ActivityNet Captions subset generated from non-interpolated/original-rate ActivityNet videos."
 keywords:
   - event-based vision
   - video understanding

 version: "1.0"
 date-released: "2026-07-14"
 type: dataset
+abstract: "EventActivityNet v1.0 provides 5-bin and 9-bin HDF5 event voxel representations of a curated ActivityNet Captions subset generated from non-interpolated/original-rate ActivityNet videos."
 keywords:
   - event-based vision
   - video understanding

README.md CHANGED Viewed

@@ -9,207 +9,119 @@ size_categories:
 - 1K<n<10K
 ---
-# Dataset Card for EventActivityNet v1.0
-## Dataset Summary
-EventActivityNet v1.0 is a generated event voxel tensor dataset derived from ActivityNet videos and ActivityNet Captions annotations. It contains per-video HDF5 files with 5-bin event voxel tensors, release metadata, and three nested release scales: Large, Medium, and Small.
-The dataset is designed for research on event-based video understanding, event voxel tensor representation learning, and caption-aligned activity modeling.
-## Dataset Details
-| Field | Value |
-|---|---|
-| Dataset name | EventActivityNet |
-| Version | v1.0 |
-| Repository | `https://huggingface.co/datasets/IIS-CVL/EventActivityNet` |
-| Data type | HDF5 event voxel tensors plus release manifests |
-| Source lineage | ActivityNet / ActivityNet Captions |
-| Source video lineage | non-interpolated/original-rate ActivityNet videos |
-| HDF5 files | 3,263 |
-| Total size | approximately 4.36 TB |
-| Action classes | 200 |
-| Event bins | 5 |
-| Large train / validation | 2,316 / 947 |
-## Scale Statistics
-| Scale | Videos | Hours | Train | Validation | Classes | Event-friendly |
-|---|---:|---:|---:|---:|---:|---:|
-| Large | 3,263 | 106.94 | 2,316 | 947 | 200 | 65.31% |
-| Medium | 1,537 | 50.00 | 1,074 | 463 | 200 | 64.80% |
-| Small | 667 | 20.00 | 473 | 194 | 200 | 64.62% |
-Small is a strict subset of Medium, and Medium is a strict subset of Large.
 ## Dataset Structure
-Each ActivityNet video in the release corresponds to exactly one HDF5 file. Scale membership and train/validation assignment are manifest-based, so users can select Large, Medium, Small, train, or validation subsets without physically moving HDF5 files.
-## Source and Provenance
-The Large release exactly matches the recovered original Large subset manifest. The subset was curated from ActivityNet Captions train and validation metadata after merging those splits before sampling.
-Verified curation principles:
-- seed `2025`;
-- initial class-balanced sampling with `max(5, int(class_ratio * class_count))`;
-- duration stratification using 33% and 66% quantile buckets;
-- event-friendly enrichment using caption keywords or first-frame darkness.
-The source video lineage is the non-interpolated/original-rate ActivityNet video lineage. Source FPS varies by video.
-## Generation Pipeline
-The recovered generation implementation follows this call chain:
 ```text
-activitynet.sh
--> activitynet.py
--> mp4_to_h5.mp4_to_h5_stream()
--> data/v2v_core_esim_gpu.EventEmulatorGPU.video_to_voxel()
--> HDF5 writer
 ```
-Confirmed generation properties:
-- 5 event bins;
-- source spatial resolution preserved;
-- no learned V2V checkpoint required for HDF5 generation;
-- `events` stored as `int16`;
-- auxiliary index/count arrays stored as `int64` and `int32`.
-Medium and Small are newly generated deterministic nested v1.0 release scales derived from Large.
-## Event-Friendly Definition
-A video is event-friendly if either condition holds:
-- a caption contains at least one keyword: `run`, `fast`, `sprint`, `night`, `dark`, `slow-motion`;
-- the first frame has normalized mean brightness below `0.4`.
-| Scale | Event-friendly videos | Caption-keyword matches | Dark-first-frame matches | Both keyword and dark | Neither trigger |
-|---|---:|---:|---:|---:|---:|
-| Large | 2,131 | 630 | 1,954 | 453 | 1,132 |
-| Medium | 996 | 297 | 914 | 215 | 541 |
-| Small | 431 | 118 | 396 | 83 | 236 |
-## Data Format
-Each video is stored as one HDF5 file with:
-- `events`: `(T, 5, H, W)`, `int16`;
-- `voxel_event_start`: `(T,)`, `int64`;
-- `voxel_event_count`: `(T,)`, `int32`.
-`T`, `H`, `W`, and file size vary by video. See [DATASET_FORMAT.md](DATASET_FORMAT.md) for details.
 ## Intended Uses
-EventActivityNet is intended for:
-- event-based video representation learning;
-- activity recognition using generated event voxel tensors;
-- event/video-language modeling with captions;
-- benchmarking methods across nested dataset scales;
-- research on event-friendly subsets of activity videos.
-## Out-of-Scope Uses
-This dataset should not be used for:
-- identifying people;
-- biometric recognition;
-- surveillance deployment;
-- making consequential decisions about individuals;
-- redistributing or using source-derived data in ways that violate ActivityNet or ActivityNet Captions terms.
 ## Limitations
-- The release contains generated event voxel tensors, not native sensor event streams.
-- Source FPS varies by video.
-- Medium and Small are deterministic nested v1.0 scales, not historical original subsets.
-- The verified local class count is 200. Some ActivityNet references mention 203 classes, but the recovered release metadata and manifests for EventActivityNet v1.0 verify 200 action classes.
-- The verified Large duration is 106.94 hours using the release duration field. Historical references to 107.3 hours should be treated as approximate for this recovered release.
-## Licensing and Citation
-EventActivityNet is derived from ActivityNet / ActivityNet Captions. Source dataset terms, licenses, and citation obligations still apply. See [LICENSE_NOTES.md](LICENSE_NOTES.md).
-If you use EventActivityNet v1.0, cite this dataset and the original ActivityNet / ActivityNet Captions sources. See [CITATION.cff](CITATION.cff).
-## Integrity Verification
-Final technical validation confirmed:
-- 3,263 valid HDF5 files;
-- all files open successfully;
-- zero truncated or unreadable files;
-- zero remaining structural warnings;
-- required datasets and dtypes are present in every file;
-- Large split counts are 2,316 train and 947 validation;
-- total release size is approximately 4.36 TB.
-## Payload Files
-The HDF5 payload is distributed as deterministic uncompressed tar shards:
-- 157 train tar shards under `data/train/`;
-- 62 validation tar shards under `data/validation/`;
-- 219 tar shards total;
-- 3,263 HDF5 members total;
-- one HDF5 member per released ActivityNet video.
-Large, Medium, and Small share the same physical HDF5 payload. Medium and Small are selected using `scales/medium_ids.txt` and `scales/small_ids.txt`; they do not duplicate payload files.
-## Annotation Files
-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 Metadata
-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:
-```text
-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`.
-## Checksums
-Tar shard checksums are published in `metadata/shard_checksums.sha256`. To verify downloaded shards from the repository root:
-```bash
-sha256sum -c metadata/shard_checksums.sha256
-```
-The checksum file contains one repository-relative entry for each of the 219 tar shards.

 - 1K<n<10K
 ---
+# EventActivityNet v1.0
+EventActivityNet is a generated event voxel tensor dataset derived from
+ActivityNet videos together with ActivityNet Captions annotations. It provides
+two alternative temporal groupings over the same canonical 3,263-video set.
+These are generated tensors, not native event-camera recordings.
+| Representation | Public path | Videos | Train / validation | Shards (train / validation) | Event shape | Canonical HDF5 bytes |
+|---|---|---:|---:|---:|---|---:|
+| 5-bin | `data_5bin/` | 3,263 | 2,316 / 947 | 157 / 62 | `(T5, 5, H, W)` | 4,355,745,895,245 |
+| 9-bin | `data_9bin/` | 3,263 | 2,316 / 947 | 157 / 62 | `(T9, 9, H, W)` | 4,214,122,096,103 |
+The repository contains approximately 8.57 TB of tar-packaged payload. The
+representations use identical video membership, split assignment, and shard
+membership. Neither representation is presented as inherently better than the
+other.
 ## Dataset Structure
+Each ActivityNet video corresponds to exactly one HDF5 member in each
+representation. Train/validation and Large/Medium/Small membership are defined
+by manifests; the nested scales do not duplicate payload files.
+```text
+data_5bin/{train,validation}/
+data_9bin/{train,validation}/
+metadata/{5bin,9bin}/
+metadata/video_metadata.jsonl
+annotations/
+scales/
+docs/
+```
+## Data Format
+Every HDF5 file contains exactly:
+- `events`: `(T_B, B, H, W)`, `int16`;
+- `voxel_event_start`: `(T_B,)`, `int64`;
+- `voxel_event_count`: `(T_B,)`, `int32`.
+Here `B` is 5 or 9. For `N` decoded source frames:
 ```text
+T_B = ceil((N - 1) / B)
 ```
+Each event slice represents one adjacent decoded-frame transition. An
+`events[t]` tensor groups up to `B` consecutive transition slices. The final
+group may be partial; unused bins are zero-filled. Timing uses each video's
+released rational source FPS metadata.
+See [Dataset Format](docs/DATASET_FORMAT.md) for schema, timing, and memory-safe
+loading details.
+## Release Scales
+| Scale | Videos | Hours | Train | Validation | Classes | Event-friendly |
+|---|---:|---:|---:|---:|---:|---:|
+| Large | 3,263 | 106.94 | 2,316 | 947 | 200 | 65.31% |
+| Medium | 1,537 | 50.00 | 1,074 | 463 | 200 | 64.80% |
+| Small | 667 | 20.00 | 473 | 194 | 200 | 64.62% |
+Small is a strict subset of Medium, and Medium is a strict subset of Large.
+## Included Metadata
+- `annotations/activitynet_captions.json`: timestamped ActivityNet Captions descriptions;
+- `annotations/activitynet_actions.json`: ActivityNet v1.3 temporal actions;
+- `annotations/eventactivitynet_alignment.json`: derived caption/action alignment;
+- `annotations/annotation_issues.jsonl`: known source annotation quirks;
+- `metadata/video_metadata.jsonl`: shared original-rate timing metadata and per-representation tensor metadata;
+- `metadata/5bin/` and `metadata/9bin/`: representation-specific shard manifests, summaries, and checksums;
+- `scales/`: Large, Medium, and Small manifests and public statistics.
 ## Intended Uses
+The dataset supports research on generated event voxel representation learning,
+activity recognition, caption-aligned activity modeling, and comparison across
+nested dataset scales or temporal groupings.
+It is out of scope for identifying people, biometric recognition, surveillance
+deployment, or consequential decisions about individuals.
 ## Limitations
+- The event voxel tensors are generated from conventional videos rather than recorded by an event camera.
+- Source FPS and spatial resolution vary by video.
+- Timing is based on decoded frame order and released rational nominal or average FPS; per-frame presentation timestamps are not consumed.
+- The subset is curated rather than an unbiased conversion of all ActivityNet videos.
+## Checksums
+```bash
+sha256sum -c metadata/5bin/shard_checksums.sha256
+sha256sum -c metadata/9bin/shard_checksums.sha256
 ```
+## Documentation
+- [Dataset Card](docs/DATASET_CARD.md)
+- [Dataset Format](docs/DATASET_FORMAT.md)
+- [Dataset Generation](docs/DATASET_GENERATION.md)
+- [Release Notes](docs/RELEASE_NOTES.md)
+- [License Notes](docs/LICENSE_NOTES.md)
+## Licensing and Citation
+ActivityNet and ActivityNet Captions source terms, licenses, citation
+obligations, and redistribution restrictions still apply. See
+[License Notes](docs/LICENSE_NOTES.md) and [CITATION.cff](CITATION.cff).
+## Release Status
+Both complete representations passed final integrity and remote-layout audits.
+The public payload contains 438 tar shards: 219 per representation.

annotations/README.md CHANGED Viewed

@@ -1,35 +1,53 @@
-# EventActivityNet Annotation and Original-Rate Timing Metadata
-This package contains public annotation and timing metadata for the EventActivityNet v1.0 Large release. It is filtered to the canonical 3,263 release video IDs. Medium and Small scales reuse these same files by filtering with their existing video-ID lists.
 ## Files
-- `annotations/activitynet_captions.json`: ActivityNet Captions entries for release videos. Training videos have the `train` caption set; validation videos may include separate `val_1` and `val_2` caption sets.
-- `annotations/activitynet_actions.json`: ActivityNet v1.3 action annotations for release videos.
-- `annotations/eventactivitynet_alignment.json`: Project-generated action-caption alignment. The alignment was generated with temporal IoU between action segments and caption timestamps, with midpoint-distance fallback, using `align_v13_with_captions.py`.
-- `annotations/annotation_issues.jsonl`: Known source annotation quirks preserved without modification.
-- `metadata/video_metadata.jsonl`: Original-rate production timing metadata for each release video.
 ## Timing
-EventActivityNet v1.0 uses original-rate, variable-FPS source videos. It was not generated from the interpolated 240 fps video derivatives. Caption and action timestamps remain in seconds on the original ActivityNet video timeline.
-For a video with `source_frame_count = N`, `num_bins = 5`, and `frames_per_bin = 1`, the production tensor length is:
 ```text
-events_T = ceil((N - 1) / 5)
 ```
-Voxel index `i` accumulates adjacent-frame event slices beginning at slice `5*i`. Its approximate source-frame interval is `[5*i, min(5*i + 5, N - 1)]`, and its approximate seconds interval is `[5*i / fps, min(5*i + 5, N - 1) / fps]`. Use the exact rational FPS fields where available.
 ## Known Source Annotation Quirks
-Canonical source annotations are preserved as-is. Some source timestamps or action segments have minor ordering or boundary issues, including small floating-point overshoots beyond the recorded duration. Consumers should use a small numerical tolerance when validating temporal boundaries; the issue file records the exact observed cases and overshoot values.
 ## Source Hashes
-Source SHA256 hashes used to build this package:
 ```json
 {
   "activitynet_actions": "4c29d5b1561e1cbff9ac69816c159e60d417a18f16db8acc0fc254c377fa9ae3",

+# EventActivityNet Annotations and Timing Metadata
+These files cover the canonical 3,263-video Large release. Medium and Small use
+the same annotation files, filtered by their video-ID lists. The 5-bin and
+9-bin representations share annotations and source timing.
 ## Files
+- `activitynet_captions.json`: ActivityNet Captions timestamped descriptions;
+- `activitynet_actions.json`: ActivityNet v1.3 temporal action annotations;
+- `eventactivitynet_alignment.json`: EventActivityNet-derived caption/action alignment using temporal IoU with midpoint-distance fallback;
+- `annotation_issues.jsonl`: known source annotation quirks preserved without changing canonical values;
+- `../metadata/video_metadata.jsonl`: shared source timing and per-representation tensor metadata.
+ActivityNet Captions is the source of timestamped descriptions. ActivityNet
+v1.3 is the source of temporal action labels and segments. EventActivityNet is
+the source of the derived alignment. Captions are timestamped descriptions,
+not instruction-tuning records.
 ## Timing
+For source frame count `N`, representation bin count `B` in `{5, 9}`, and
+`frames_per_bin=1`:
+```text
+T_B = ceil((N - 1) / B)
+```
+Voxel `i` groups adjacent-frame transition indices
+`[B*i, min(B*i + B, N - 1))`. Its approximate source-frame interval is
+`[B*i, min(B*i + B, N - 1)]`, and its approximate seconds interval is:
 ```text
+[B*i * fps_den / fps_num,
+ min(B*i + B, N - 1) * fps_den / fps_num]
 ```
+Construction follows decoded frame order. Per-frame presentation timestamps
+are not consumed, so seconds-level mapping is approximate for within-video
+variable-frame-rate streams. Do not assume fixed 25 fps or 240 fps.
 ## Known Source Annotation Quirks
+Canonical source annotations are preserved as-is. Some timestamps or action
+segments have minor ordering or boundary issues, including small floating-point
+overshoots. Use a small numerical tolerance; `annotation_issues.jsonl` records
+the observed cases.
 ## Source Hashes
 ```json
 {
   "activitynet_actions": "4c29d5b1561e1cbff9ac69816c159e60d417a18f16db8acc0fc254c377fa9ae3",

docs/CHANGELOG.md CHANGED Viewed

@@ -1,34 +1,32 @@
 # Changelog
-## v1.0 - 2026-07-14
-Initial public release documentation for EventActivityNet.
-Verified release facts:
-- Large scale: 3,263 videos, 106.941600381 hours.
-- Medium scale: 1,537 videos, 50.000000128 hours.
-- Small scale: 667 videos, 20.000000374 hours.
 - 200 verified action classes.
 - Large train/validation split: 2,316 / 947.
-- HDF5 schema: `events`, `voxel_event_start`, `voxel_event_count`.
-- `events` shape: `(T, 5, H, W)`, dtype `int16`.
-- Total production size after validation: 4,355,745,895,245 bytes.
-- Final integrity audit status: PASS.
-- No truncated or structurally inconsistent production HDF5 files remain.
-Notes:
-- Five problematic HDF5 files were regenerated and safely installed before the final integrity audit.
-- Medium and Small are deterministic nested v1.0 release scales derived from Large.
-- The verified exact Large duration is 106.941600381 hours using `src_fmt_dur`.
-- The verified class count is 200.
-## v1.0 final small-file update
-- Added public ActivityNet Captions annotations filtered to the release videos.
-- Added ActivityNet v1.3 action annotations filtered to the release videos.
-- Added EventActivityNet derived caption/action alignment metadata.
-- Added original-rate timing metadata for all 3,263 release videos.
-- Added `metadata/shard_checksums.sha256` with 219 repository-relative tar shard checksums.
-- Updated payload summary metadata with 157 train shards, 62 validation shards, 219 total shards, 3,263 HDF5 members, and 4,355,753,021,440 remote tar bytes.

 # Changelog
+## v1.0 update - 2026-08
+- Added the complete 9-bin event voxel representation.
+- Normalized payload directories to `data_5bin/` and `data_9bin/`.
+- Preserved the canonical 3,263-video membership and 2,316/947 split for both variants.
+- Added representation-specific metadata under `metadata/5bin/` and `metadata/9bin/`.
+- Generalized timing and format documentation for bin count `B` in `{5, 9}`.
+- Verified 219 shards per representation and 438 total payload shards.
+Canonical HDF5 payload sizes:
+- 5-bin: 4,355,745,895,245 bytes;
+- 9-bin: 4,214,122,096,103 bytes.
+## v1.0 - 2026-07-14
+Initial public 5-bin release.
+- Large: 3,263 videos and 106.941600381 hours.
+- Medium: 1,537 videos and 50.000000128 hours.
+- Small: 667 videos and 20.000000374 hours.
 - 200 verified action classes.
 - Large train/validation split: 2,316 / 947.
+- HDF5 datasets: `events`, `voxel_event_start`, `voxel_event_count`.
+- Canonical 5-bin HDF5 size: 4,355,745,895,245 bytes.
+- Final integrity audit: PASS.
+The initial small-file update added filtered ActivityNet Captions annotations,
+ActivityNet v1.3 actions, derived caption/action alignment, source timing
+metadata, and 219 tar-shard checksums.

docs/DATASET_CARD.md CHANGED Viewed

@@ -1,10 +1,12 @@
-# Dataset Card for EventActivityNet v1.0
 ## Dataset Summary
-EventActivityNet v1.0 is a generated event voxel tensor dataset derived from ActivityNet videos and ActivityNet Captions annotations. It contains per-video HDF5 files with 5-bin event voxel tensors, release metadata, and three nested release scales: Large, Medium, and Small.
-The dataset is designed for research on event-based video understanding, event voxel tensor representation learning, and caption-aligned activity modeling.
 ## Dataset Details
@@ -12,15 +14,23 @@ The dataset is designed for research on event-based video understanding, event v
 |---|---|
 | Dataset name | EventActivityNet |
 | Version | v1.0 |
-| Repository | `https://huggingface.co/datasets/IIS-CVL/EventActivityNet` |
-| Data type | HDF5 event voxel tensors plus release manifests |
-| Source lineage | ActivityNet / ActivityNet Captions |
 | Source video lineage | non-interpolated/original-rate ActivityNet videos |
-| HDF5 files | 3,263 |
-| Total size | approximately 4.36 TB |
-| Action classes | 200 |
-| Event bins | 5 |
-| Large train / validation | 2,316 / 947 |
 ## Scale Statistics
@@ -31,174 +41,103 @@ The dataset is designed for research on event-based video understanding, event v
 | Small | 667 | 20.00 | 473 | 194 | 200 | 64.62% |
 Small is a strict subset of Medium, and Medium is a strict subset of Large.
-## Dataset Structure
-Each ActivityNet video in the release corresponds to exactly one HDF5 file. Scale membership and train/validation assignment are manifest-based, so users can select Large, Medium, Small, train, or validation subsets without physically moving HDF5 files.
 ## Source and Provenance
-The Large release exactly matches the recovered original Large subset manifest. The subset was curated from ActivityNet Captions train and validation metadata after merging those splits before sampling.
-Verified curation principles:
-- seed `2025`;
-- initial class-balanced sampling with `max(5, int(class_ratio * class_count))`;
-- duration stratification using 33% and 66% quantile buckets;
-- event-friendly enrichment using caption keywords or first-frame darkness.
-The source video lineage is the non-interpolated/original-rate ActivityNet video lineage. Source FPS varies by video.
 ## Generation Pipeline
-The recovered generation implementation follows this call chain:
-```text
-activitynet.sh
--> activitynet.py
--> mp4_to_h5.mp4_to_h5_stream()
--> data/v2v_core_esim_gpu.EventEmulatorGPU.video_to_voxel()
--> HDF5 writer
-```
-Confirmed generation properties:
-- 5 event bins;
-- source spatial resolution preserved;
-- no learned V2V checkpoint required for HDF5 generation;
-- `events` stored as `int16`;
-- auxiliary index/count arrays stored as `int64` and `int32`.
-Medium and Small are newly generated deterministic nested v1.0 release scales derived from Large.
 ## Event-Friendly Definition
-A video is event-friendly if either condition holds:
-- a caption contains at least one keyword: `run`, `fast`, `sprint`, `night`, `dark`, `slow-motion`;
-- the first frame has normalized mean brightness below `0.4`.
-| Scale | Event-friendly videos | Caption-keyword matches | Dark-first-frame matches | Both keyword and dark | Neither trigger |
 |---|---:|---:|---:|---:|---:|
 | Large | 2,131 | 630 | 1,954 | 453 | 1,132 |
 | Medium | 996 | 297 | 914 | 215 | 541 |
 | Small | 431 | 118 | 396 | 83 | 236 |
 ## Data Format
-Each video is stored as one HDF5 file with:
-- `events`: `(T, 5, H, W)`, `int16`;
-- `voxel_event_start`: `(T,)`, `int64`;
-- `voxel_event_count`: `(T,)`, `int32`.
-`T`, `H`, `W`, and file size vary by video. See [DATASET_FORMAT.md](DATASET_FORMAT.md) for details.
 ## Intended Uses
-EventActivityNet is intended for:
-- event-based video representation learning;
-- activity recognition using generated event voxel tensors;
-- event/video-language modeling with captions;
-- benchmarking methods across nested dataset scales;
-- research on event-friendly subsets of activity videos.
 ## Out-of-Scope Uses
-This dataset should not be used for:
-- identifying people;
-- biometric recognition;
 - surveillance deployment;
-- making consequential decisions about individuals;
-- redistributing or using source-derived data in ways that violate ActivityNet or ActivityNet Captions terms.
 ## Limitations
-- The release contains generated event voxel tensors, not native sensor event streams.
-- Source FPS varies by video.
 - Medium and Small are deterministic nested v1.0 scales, not historical original subsets.
-- The verified local class count is 200. Some ActivityNet references mention 203 classes, but the recovered release metadata and manifests for EventActivityNet v1.0 verify 200 action classes.
-- The verified Large duration is 106.94 hours using the release duration field. Historical references to 107.3 hours should be treated as approximate for this recovered release.
 ## Licensing and Citation
-EventActivityNet is derived from ActivityNet / ActivityNet Captions. Source dataset terms, licenses, and citation obligations still apply. See [LICENSE_NOTES.md](LICENSE_NOTES.md).
-If you use EventActivityNet v1.0, cite this dataset and the original ActivityNet / ActivityNet Captions sources. See [CITATION.cff](CITATION.cff).
 ## Integrity Verification
-Final technical validation confirmed:
-- 3,263 valid HDF5 files;
-- all files open successfully;
-- zero truncated or unreadable files;
-- zero remaining structural warnings;
-- required datasets and dtypes are present in every file;
-- Large split counts are 2,316 train and 947 validation;
-- total release size is approximately 4.36 TB.
-## Payload Files
-The HDF5 payload is distributed as deterministic uncompressed tar shards:
-- 157 train tar shards under `data/train/`;
-- 62 validation tar shards under `data/validation/`;
-- 219 tar shards total;
-- 3,263 HDF5 members total;
-- one HDF5 member per released ActivityNet video.
-Large, Medium, and Small share the same physical HDF5 payload. Medium and Small are selected using `scales/medium_ids.txt` and `scales/small_ids.txt`; they do not duplicate payload files.
-## Annotation Files
-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 Metadata
-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:
-```text
-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`.
-## Checksums
-Tar shard checksums are published in `metadata/shard_checksums.sha256`. To verify downloaded shards from the repository root:
-```bash
-sha256sum -c metadata/shard_checksums.sha256
-```
-The checksum file contains one repository-relative entry for each of the 219 tar shards.

+# EventActivityNet Dataset Card
 ## Dataset Summary
+EventActivityNet v1.0 provides generated event voxel tensors derived from
+ActivityNet videos and ActivityNet Captions annotations. Two representations,
+5-bin and 9-bin, cover the same canonical 3,263 videos, split assignments,
+annotations, and nested release scales. The release does not contain native
+event-camera recordings.
 ## Dataset Details
 |---|---|
 | Dataset name | EventActivityNet |
 | Version | v1.0 |
+| Source lineage | ActivityNet and ActivityNet Captions |
 | Source video lineage | non-interpolated/original-rate ActivityNet videos |
+| Canonical videos | 3,263 |
+| Train / validation | 2,316 / 947 |
+| Verified action classes | 200 |
+| Representations | 5-bin and 9-bin HDF5 event voxel tensors |
+| Public tar payload | approximately 8.57 TB |
+## Representation Statistics
+| Representation | Path | Event shape | Train shards | Validation shards | HDF5 bytes | Tar bytes |
+|---|---|---|---:|---:|---:|---:|
+| 5-bin | `data_5bin/` | `(T5, 5, H, W)` | 157 | 62 | 4,355,745,895,245 | 4,355,753,021,440 |
+| 9-bin | `data_9bin/` | `(T9, 9, H, W)` | 157 | 62 | 4,214,122,096,103 | 4,214,129,203,200 |
+The two variants are alternative groupings of adjacent-frame transition
+slices. No quality or superiority claim is attached to either grouping.
 ## Scale Statistics
 | Small | 667 | 20.00 | 473 | 194 | 200 | 64.62% |
 Small is a strict subset of Medium, and Medium is a strict subset of Large.
+Scale membership is manifest-based and shared by both representations.
 ## Source and Provenance
+The canonical Large set matches the recovered original Large subset manifest.
+The recovered curation implementation merged ActivityNet Captions train and
+validation metadata before sampling. Verified principles include seed `2025`,
+initial class-balanced sampling with
+`max(5, int(class_ratio * class_count))`, duration stratification at the 33%
+and 66% quantiles, and event-friendly enrichment using caption keywords or
+first-frame darkness.
+The source videos follow the original-rate, non-interpolated ActivityNet
+lineage. FPS and resolution vary by video.
 ## Generation Pipeline
+The recovered implementation follows:
+1. ActivityNet video loader;
+2. `mp4_to_h5.mp4_to_h5_stream()`;
+3. `EventEmulatorGPU.video_to_voxel()`;
+4. event-slice grouping with `B=5` or `B=9`;
+5. HDF5 writer.
+Both variants preserve source resolution, use one generated slice per adjacent
+decoded-frame transition, store `events` as `int16`, and store start/count
+arrays as `int64`/`int32`. No learned V2V checkpoint is required for HDF5
+generation.
 ## Event-Friendly Definition
+A video is event-friendly when a caption contains `run`, `fast`, `sprint`,
+`night`, `dark`, or `slow-motion`, or when normalized first-frame mean
+brightness is below `0.4`.
+| Scale | Event-friendly videos | Caption-keyword matches | Dark-first-frame matches | Both | Neither |
 |---|---:|---:|---:|---:|---:|
 | Large | 2,131 | 630 | 1,954 | 453 | 1,132 |
 | Medium | 996 | 297 | 914 | 215 | 541 |
 | Small | 431 | 118 | 396 | 83 | 236 |
+## Dataset Structure
+Each canonical video has one HDF5 member per representation. Physical payload
+is split into 157 train and 62 validation shards under each representation
+directory. Shared annotations, source timing, and scale manifests are not
+duplicated. Representation-specific shard metadata is under `metadata/5bin/`
+and `metadata/9bin/`.
 ## Data Format
+For bin count `B` in `{5, 9}`, `events` has shape `(T_B, B, H, W)` and:
+```text
+T_B = ceil((N - 1) / B)
+```
+`voxel_event_start[t]` is the first adjacent-frame transition index in group
+`t`; `voxel_event_count[t]` is the number of valid transition slices. The last
+group may be partial and unused bins are zero-filled. See
+[DATASET_FORMAT.md](DATASET_FORMAT.md).
 ## Intended Uses
+- generated event voxel representation learning;
+- activity recognition;
+- caption-aligned video/event modeling;
+- comparison across nested scales;
+- comparison of 5-bin and 9-bin temporal groupings without treating either as native-event ground truth.
 ## Out-of-Scope Uses
+- identifying people or biometric recognition;
 - surveillance deployment;
+- consequential decisions about individuals;
+- uses or redistribution that violate ActivityNet or ActivityNet Captions terms.
 ## Limitations
+- Tensors are generated from conventional videos, not captured by an event camera.
+- The release is curated and is not an unbiased conversion of all ActivityNet videos.
+- Source frame rate and resolution vary by video.
+- Seconds-level timing is approximate for within-video variable-frame-rate streams because construction follows decoded frame order rather than per-frame presentation timestamps.
 - Medium and Small are deterministic nested v1.0 scales, not historical original subsets.
+- The verified release contains 200 classes. ActivityNet references to 203 classes do not describe the recovered release manifest.
+- The verified Large duration is 106.94 hours; historical 107.3-hour wording is approximate.
 ## Licensing and Citation
+ActivityNet and ActivityNet Captions terms and citation obligations remain in
+force. EventActivityNet grants no additional rights beyond the source datasets.
+See [LICENSE_NOTES.md](LICENSE_NOTES.md) and the repository `CITATION.cff`.
 ## Integrity Verification
+Both representations contain exactly 3,263 readable HDF5 members with the
+required three-dataset schema. Final remote verification confirmed 438 expected
+tar shards, exact recorded sizes and checksums, no missing or unexpected tar
+paths, and no stale payload under the former `data/` namespace.

docs/DATASET_FORMAT.md CHANGED Viewed

@@ -1,12 +1,19 @@
 # EventActivityNet Dataset Format
-## Overview
-EventActivityNet v1.0 stores one HDF5 file per video. Scale membership and train/validation splits are defined by manifests, so files do not need to be physically moved to use a particular split or scale.
-File size varies substantially with video duration and spatial resolution.
-Every production HDF5 file has exactly these root datasets:
 ```text
 events
@@ -14,35 +21,21 @@ voxel_event_start
 voxel_event_count
 ```
-Every production HDF5 file has these required root attributes:
-```text
-fps
-height
-width
-num_bins
-interpolate_bins
-```
-## HDF5 Schema
 ### `events`
 | Property | Value |
 |---|---|
-| Shape | `(T, 5, H, W)` |
 | Dtype | `int16` |
-| Compression | gzip |
 | Shuffle | enabled |
-| Chunking | `(1, 5, min(H, 256), min(W, 256))` |
-`T`, `H`, and `W` vary by video. Spatial resolution is preserved from the source video.
 ### `voxel_event_start`
 | Property | Value |
 |---|---|
-| Shape | `(T,)` |
 | Dtype | `int64` |
 | Compression | LZF |
 | Shuffle | enabled |
@@ -52,119 +45,90 @@ interpolate_bins
 | Property | Value |
 |---|---|
-| Shape | `(T,)` |
 | Dtype | `int32` |
 | Compression | LZF |
 | Shuffle | enabled |
 | Chunking | `(1024,)` |
-## Root Attributes
-| Attribute | Meaning |
-|---|---|
-| `fps` | Source video FPS metadata |
-| `height` | Source video height |
-| `width` | Source video width |
-| `num_bins` | Number of voxel bins; always `5` in v1.0 |
-| `interpolate_bins` | Whether temporal bin interpolation was used |
-## Semantics
-The generator emits event slices from adjacent grayscale video frames and accumulates them into 5-bin voxel samples.
-- `events[t]` is the 5-bin voxel tensor for timestep `t`.
-- `voxel_event_start[t]` is the zero-based generated-slice start index for voxel sample `t`.
-- `voxel_event_count[t]` is the number of generated slices accumulated into voxel sample `t`.
-For normal full samples with `frames_per_bin=1`, `voxel_event_count[t]` is typically `5`. Final partial samples may be smaller.
-## Manifest Fields
-Release scale manifests include one record per video. Typical fields:
-| Field | Meaning |
-|---|---|
-| `video_id` | Canonical ActivityNet video ID, including `v_` prefix |
-| `split` | `train` or `validation` |
-| `class_label` | ActivityNet action class label |
-| `duration_seconds` | Verified duration used for scale construction |
-| `duration_source` | Duration field source, `src_fmt_dur` |
-| `duration_bucket` | `short`, `medium`, or `long` |
-| `event_friendly` | Boolean event-friendly flag |
-| `event_keyword_hits` | Matched event-friendly caption keywords |
-| `first_frame_mean` | Normalized first-frame brightness used for the darkness rule |
-| `dark_first_frame` | Whether first-frame mean is below `0.4` |
-## Memory-Safe Loading Example
-```python
-import h5py
-path = "path/to/video.h5"
-with h5py.File(path, "r") as f:
-    events = f["events"]
-    starts = f["voxel_event_start"]
-    counts = f["voxel_event_count"]
-    print(events.shape)        # (T, 5, H, W)
-    print(events.dtype)        # int16
-    print(starts.shape)        # (T,)
-    print(counts.shape)        # (T,)
-    print(f.attrs["num_bins"]) # 5
-    first_voxel = events[0]    # loads one timestep, not the whole file
 ```
-Avoid loading entire HDF5 arrays into memory unless your system has sufficient RAM.
-## Payload Shards
-The HDF5 payload is distributed as deterministic uncompressed tar shards:
-- 157 train tar shards under `data/train/`;
-- 62 validation tar shards under `data/validation/`;
-- 219 tar shards total;
-- 3,263 HDF5 members total;
-- one HDF5 member per released ActivityNet video.
-Large, Medium, and Small share the same physical HDF5 payload. Medium and Small are selected using `scales/medium_ids.txt` and `scales/small_ids.txt`; they do not duplicate payload files.
-## Timing Metadata
-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:
-```text
-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`.
-## Checksums
-Tar shard checksums are published in `metadata/shard_checksums.sha256`. To verify downloaded shards from the repository root:
-```bash
-sha256sum -c metadata/shard_checksums.sha256
 ```
-The checksum file contains one repository-relative entry for each of the 219 tar shards.

 # EventActivityNet Dataset Format
+## Representations
+EventActivityNet stores one HDF5 file per video for each supported bin count.
+| Bin count `B` | Public payload | Event shape |
+|---:|---|---|
+| 5 | `data_5bin/` | `(T5, 5, H, W)` |
+| 9 | `data_9bin/` | `(T9, 9, H, W)` |
+File size varies substantially with duration and spatial resolution.
+## HDF5 Schema
+Every HDF5 file has exactly these root datasets:
 ```text
 events
 voxel_event_count
 ```
 ### `events`
 | Property | Value |
 |---|---|
+| Shape | `(T_B, B, H, W)` |
 | Dtype | `int16` |
+| Compression | gzip, level 4 |
 | Shuffle | enabled |
+| Chunking | `(1, B, min(H, 256), min(W, 256))` |
 ### `voxel_event_start`
 | Property | Value |
 |---|---|
+| Shape | `(T_B,)` |
 | Dtype | `int64` |
 | Compression | LZF |
 | Shuffle | enabled |
 | Property | Value |
 |---|---|
+| Shape | `(T_B,)` |
 | Dtype | `int32` |
 | Compression | LZF |
 | Shuffle | enabled |
 | Chunking | `(1024,)` |
+Required root attributes are `fps`, `height`, `width`, `num_bins`, and
+`interpolate_bins`. `num_bins` is 5 or 9 and matches `events.shape[1]`.
+## Transition Grouping
+Construction follows decoded frame order. For `N` source frames there are
+`N - 1` adjacent-frame transitions. Transition index `e` corresponds to source
+frames `(e, e + 1)`.
+For bin count `B`:
+```text
+T_B = ceil((N - 1) / B)
+voxel_event_start[t] = B * t
+voxel_event_count[t] = min(B, N - 1 - B * t)
+```
+`events[t]` groups transitions in the half-open range
+`[B*t, min(B*t + B, N - 1))`. The associated source-frame interval is
+`[B*t, min(B*t + B, N - 1)]`. The final group can contain fewer than `B`
+valid transitions; unused bins are zero-filled.
+## Timing
+The HDF5 `fps` attribute is source-frame FPS stored as a float. Use the released
+`fps_num` and `fps_den` fields for reproducible conversion. Approximate voxel
+times are:
+```text
+start_seconds = B * t * fps_den / fps_num
+end_seconds = min(B * t + B, N - 1) * fps_den / fps_num
 ```
+Per-frame presentation timestamps are not consumed. These conversions are
+therefore approximate for within-video variable-frame-rate streams. Do not
+assume fixed 25 fps or 240 fps, and do not use `t / fps` as voxel time.
+For a caption/action interval `[start_seconds, end_seconds]`:
+```text
+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 / B))
+t_end_exclusive = min(T_B, ceil(end_frame / B))
+```
+Use `[t_start, t_end_exclusive)` for Python slicing.
+## Memory-Safe Loading
+```python
+import h5py
+with h5py.File("v_example.h5", "r") as f:
+    events = f["events"]
+    starts = f["voxel_event_start"]
+    counts = f["voxel_event_count"]
+    B = int(f.attrs["num_bins"])
+    print(events.shape)  # (T_B, B, H, W)
+    print(events.dtype)  # int16
+    print(starts.dtype)  # int64
+    print(counts.dtype)  # int32
+    selected = events[10:18]  # reads only the selected temporal range
 ```
+Avoid loading complete event tensors unless sufficient memory is available.
+## Shards and Metadata
+Each representation has 157 train shards, 62 validation shards, and 3,263 HDF5
+members. Representation-specific manifests and checksums are under:
+```text
+metadata/5bin/
+metadata/9bin/
 ```
+Shared source/timing metadata is in `metadata/video_metadata.jsonl`, with
+representation-specific tensor fields nested under `representations`.

docs/DATASET_GENERATION.md CHANGED Viewed

@@ -1,142 +1,85 @@
-# EventActivityNet v1.0 Dataset Generation
 ## Source Video Lineage
-EventActivityNet v1.0 was generated from the non-interpolated/original-rate ActivityNet video lineage. It was not generated from the recovered 240fps interpolated video directory.
-Source FPS varies by video. The generator records FPS metadata per HDF5 file and preserves source spatial resolution.
 ## Large Subset Curation
-The Large release exactly matches the recovered original Large subset manifest. It contains 3,263 unique video IDs.
-The recovered subset curation implementation:
-- merges ActivityNet Captions train and validation metadata before sampling;
-- uses seed `2025`;
-- performs initial class-balanced sampling with `max(5, int(class_ratio * class_count))`;
-- uses default `class_ratio=0.2`;
-- length-balances using 33% and 66% duration quantiles;
-- enriches with event-friendly examples from caption keywords or first-frame darkness.
-Event-friendly keywords:
-```text
-run, fast, sprint, night, dark, slow-motion
-```
-Darkness threshold:
-```text
-normalized first-frame mean brightness < 0.4
-```
-The minimum-per-class rule is an initial sampling-stage rule. Later global length balancing can reduce final per-class counts below that initial quota.
-## HDF5 Generation Pipeline
-The confirmed generation call chain is:
-- `activitynet.sh`
-- `activitynet.py`
-- `mp4_to_h5.mp4_to_h5_stream()`
-- `data/v2v_core_esim_gpu.EventEmulatorGPU.video_to_voxel()`
-- HDF5 writer
-Confirmed generation parameters:
-| Parameter | Value |
-|---|---|
-| Source video lineage | non-interpolated/original-rate ActivityNet videos |
-| Number of bins | 5 |
-| `frames_per_bin` | 1 |
-| Spatial resizing | none; source resolution preserved |
-| Output event dtype | `int16` |
-| `voxel_event_start` dtype | `int64` |
-| `voxel_event_count` dtype | `int32` |
-| `events` compression | gzip+shuffle |
-| Auxiliary compression | LZF+shuffle |
-| Learned checkpoint required | no |
-The recovered implementation shows that no learned HyperE2VID/V2V checkpoint is used for HDF5 generation. Checkpoints found in the recovered project belong to downstream reconstruction/evaluation code, not to the generation of the HDF5 voxel files.
-## Voxel Metadata Semantics
-The generator emits event slices from adjacent grayscale video frames and accumulates them into output voxel samples.
-- `voxel_event_start[t]` is the generated-slice start index for voxel sample `t`.
-- `voxel_event_count[t]` is the number of generated slices accumulated into voxel sample `t`.
-## Release Scale Construction
-Large is the recovered historical subset.
-Medium and Small are newly generated deterministic nested v1.0 release scales:
 ```text
-Small subset Medium subset Large
 ```
-Target durations:
-- Medium: approximately 50 hours.
-- Small: approximately 20 hours.
-Scale construction uses seed `2025` and stratifies by:
-```text
-(split, class_label, duration_bucket, event_friendly)
-```
-Within each stratum, records are ranked deterministically by a stable hash of seed and video ID.
-## Verified Output
-Final validation confirmed:
-- 3,263 valid HDF5 files;
-- all files open successfully;
-- zero truncated or unreadable files;
-- zero remaining structural warnings;
-- Large train/validation split: 2,316 / 947.
-## Original-Rate Timing Mapping
-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:
-```text
-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`.
-## Annotation Alignment
-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.

+# EventActivityNet Dataset Generation
 ## Source Video Lineage
+Both representations were generated from the non-interpolated/original-rate
+ActivityNet video lineage. Source frame rate varies by video, no source video is
+resampled or interpolated, and source spatial resolution is preserved.
 ## Large Subset Curation
+The canonical Large set contains 3,263 unique videos. The recovered curation
+implementation:
+- merged ActivityNet Captions train and validation metadata before sampling;
+- used seed `2025`;
+- used initial class-balanced sampling with `max(5, int(class_ratio * class_count))` and `class_ratio=0.2`;
+- used 33% and 66% duration quantiles;
+- enriched event-friendly examples using caption keywords or first-frame darkness.
+The keyword list is `run`, `fast`, `sprint`, `night`, `dark`, and
+`slow-motion`; the darkness rule is normalized first-frame mean brightness
+below `0.4`.
+## HDF5 Generation
+The recovered implementation follows:
+1. `activitynet.py` video loading;
+2. `mp4_to_h5.mp4_to_h5_stream()`;
+3. `EventEmulatorGPU.video_to_voxel()`;
+4. temporal grouping;
+5. HDF5 writing.
+| Parameter | 5-bin | 9-bin |
+|---|---:|---:|
+| `num_bins` | 5 | 9 |
+| `frames_per_bin` | 1 | 1 |
+| Source resizing | none | none |
+| `events` dtype | `int16` | `int16` |
+| Start/count dtypes | `int64` / `int32` | `int64` / `int32` |
+| Event compression | gzip+shuffle | gzip+shuffle |
+| Auxiliary compression | LZF+shuffle | LZF+shuffle |
+| Learned checkpoint | none | none |
+The canonical 9-bin lineage was generated with a frozen deterministic
+configuration using RNG seed `42`. The emulator creates and seeds its generator
+for each streaming batch while carrying the previous frame and returned
+potential across batches. This is recorded as technical reproducibility
+metadata; it is not a comparative quality claim.
+## Generic Grouping Semantics
+For bin count `B` and `N` decoded source frames:
 ```text
+T_B = ceil((N - 1) / B)
 ```
+Each generated event slice represents one adjacent-frame transition.
+`events[t]` groups up to `B` consecutive slices;
+`voxel_event_start[t] = B*t`; and `voxel_event_count[t]` records the valid
+slice count. The final group is zero-filled beyond its valid count.
+## Timing Basis
+Seconds-level conversion uses each video's released rational nominal or average
+frame rate. Construction follows decoded frame order and does not consume
+per-frame presentation timestamps, so mapping is approximate for
+within-video variable-frame-rate streams. No fixed 25-fps or 240-fps assumption
+should be used.
+## Release Scales
+Large is the recovered historical set. Medium and Small are deterministic
+nested v1.0 scales selected with seed `2025` and strata
+`(split, class_label, duration_bucket, event_friendly)`. Small is a strict
+subset of Medium, and Medium is a strict subset of Large. Both event voxel
+representations share these scale manifests.
+## Validation
+Final validation confirmed exactly 3,263 readable files per representation,
+the same 2,316/947 split assignment, expected tensor length and metadata arrays,
+and zero remaining structural failures. Canonical HDF5 payload sizes are
+4,355,745,895,245 bytes for 5-bin and 4,214,122,096,103 bytes for 9-bin.

docs/RELEASE_NOTES.md CHANGED Viewed

@@ -2,111 +2,55 @@
 ## Release Status
-EventActivityNet v1.0 has completed technical validation and is suitable for public release as documented HDF5 files and scale manifests.
-## Highlights
-- 3,263 validated HDF5 files.
-- Approximately 4.36 TB total size.
-- 5-bin event voxel representation.
-- 200 verified action classes.
-- Large train/validation split: 2,316 / 947.
-- Three nested scales: Large, Medium, Small.
-- Final production integrity audit: PASS.
-## Scale Summary
-| Scale | Videos | Hours | Train | Validation | Classes | Event-friendly |
-|---|---:|---:|---:|---:|---:|---:|
-| Large | 3,263 | 106.94 | 2,316 | 947 | 200 | 65.31% |
-| Medium | 1,537 | 50.00 | 1,074 | 463 | 200 | 64.80% |
-| Small | 667 | 20.00 | 473 | 194 | 200 | 64.62% |
-Small is a strict subset of Medium, and Medium is a strict subset of Large.
 ## Integrity
-The final read-only production audit confirmed:
-- exactly 3,263 production HDF5 files;
-- all files open successfully;
-- zero truncated or unreadable files;
-- zero structural warnings;
-- every file contains `events`, `voxel_event_start`, and `voxel_event_count`;
-- all files use `num_bins=5`;
-- no temporary files remain in the production dataset.
-Exact reproducibility values:
-- total size: 4,355,745,895,245 bytes;
-- Large duration: 106.941600381 hours;
-- Medium duration: 50.000000128 hours;
-- Small duration: 20.000000374 hours.
 ## Caveats
-- 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:
-```text
-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`.

 ## Release Status
+EventActivityNet v1.0 provides complete 5-bin and 9-bin generated event voxel
+representations over the same canonical 3,263-video set.
+## Representation Summary
+| Representation | Path | Train / validation | Shards | Canonical HDF5 bytes |
+|---|---|---:|---:|---:|
+| 5-bin | `data_5bin/` | 2,316 / 947 | 219 | 4,355,745,895,245 |
+| 9-bin | `data_9bin/` | 2,316 / 947 | 219 | 4,214,122,096,103 |
+Both use 157 train and 62 validation tar shards. The repository contains
+approximately 8.57 TB of payload overall. The variants are alternative
+temporal groupings; neither is claimed to be inherently superior.
+## 2026-08 Update
+- Added the complete 9-bin representation.
+- Normalized public representation directories to `data_5bin/` and `data_9bin/`.
+- Retained the same canonical 3,263-video train/validation membership and shared annotations for both variants.
+- Separated shard manifests, summaries, and checksums under `metadata/5bin/` and `metadata/9bin/`.
+- Generalized source timing metadata to describe both representations without changing canonical source values.
 ## Integrity
+Final audits confirmed:
+- 3,263 valid HDF5 files per representation;
+- zero unreadable or structurally invalid production files;
+- exact `events`, `voxel_event_start`, and `voxel_event_count` datasets;
+- `num_bins=5` or `num_bins=9` matching each representation;
+- 438 expected remote tar shards with exact recorded sizes and checksums;
+- no tar payload under the former `data/` namespace.
+## Scale Summary
+| Scale | Videos | Hours | Train | Validation | Classes | Event-friendly |
+|---|---:|---:|---:|---:|---:|---:|
+| Large | 3,263 | 106.94 | 2,316 | 947 | 200 | 65.31% |
+| Medium | 1,537 | 50.00 | 1,074 | 463 | 200 | 64.80% |
+| Small | 667 | 20.00 | 473 | 194 | 200 | 64.62% |
 ## Caveats
+- These are generated event voxel tensors, not native event-camera recordings.
+- The verified release contains 200 action classes.
+- The historical 107.3-hour Large figure is approximate; the verified duration is 106.94 hours.
+- Medium and Small are deterministic nested v1.0 scales.
+- Source FPS varies by video and timing uses released rational FPS metadata.
+Changing the original 5-bin directory from `data/` to `data_5bin/` changes
+repository URLs used by older download scripts. Tar filenames and content
+checksums are unchanged.

metadata/{shard_checksums.sha256 → 5bin/shard_checksums.sha256} RENAMED Viewed

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The diff for this file is too large to render. See raw diff

metadata/{shard_summary.json → 5bin/shard_summary.json} RENAMED Viewed

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metadata/9bin/shard_summary.json ADDED Viewed

	@@ -0,0 +1,40 @@

+{
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+  "checksum_file": "metadata/9bin/shard_checksums.sha256",
+  "expected_h5_count": 3263,
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+  "overall_shard_size_bytes": {
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+    "mean": 19242599101.36986,
+    "min": 2442383360
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+  "planned_h5_count": 3263,
+  "planned_member_count": 3263,
+  "public_path": "data_9bin/",
+  "representation": "9bin",
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+    "validation": 947
+  },
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+      "member_count": 2316,
+      "shard_count": 157,
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+      "shard_count": 62,
+      "total_remote_tar_bytes": 1181061242880
+    }
+  },
+  "status": "final_verified_release",
+  "total_h5_payload_bytes": 4214122096103,
+  "total_members": 3263,
+  "total_remote_tar_bytes": 4214129203200,
+  "total_shard_count": 219,
+  "total_shards": 219,
+  "train_shards": 157,
+  "validation_shards": 62
+}

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