Add EventActivityNet v1.0 documentation and metadata

CITATION.cff

@@ -0,0 +1,45 @@
+cff-version: 1.2.0
+message: "If you use EventActivityNet v1.0, please cite this dataset and the original ActivityNet / ActivityNet Captions sources."
+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
+  - ActivityNet
+  - ActivityNet Captions
+  - HDF5
+  - voxel events
+authors:
+  - family-names: Hong
+    given-names: Cheng-Yao
+  - family-names: Lin
+    given-names: Ting-Wei
+  - family-names: Lai
+    given-names: Yun-Chung
+  - family-names: Lee
+    given-names: Hua-Wei
+  - family-names: Chen
+    given-names: Hwann-Tzong
+  - family-names: Liu
+    given-names: Tyng-Luh
+url: "https://huggingface.co/datasets/IIS-CVL/EventActivityNet"
+preferred-citation:
+  type: dataset
+  title: "EventActivityNet v1.0"
+  authors:
+    - family-names: Hong
+      given-names: Cheng-Yao
+    - family-names: Lin
+      given-names: Ting-Wei
+    - family-names: Lai
+      given-names: Yun-Chung
+    - family-names: Lee
+      given-names: Hua-Wei
+    - family-names: Chen
+      given-names: Hwann-Tzong
+    - family-names: Liu
+      given-names: Tyng-Luh
+  date-released: "2026-07-14"

README.md

@@ -1,16 +1,152 @@
 ---
-license: other
 pretty_name: EventActivityNet
-task_categories:
-- video-classification
-language:
-- en
+tags:
+- event-based-vision
+- video-understanding
+- activitynet
+- hdf5
+size_categories:
+- 1K<n<10K
 ---
 
-# EventActivityNet
+# Dataset Card for EventActivityNet v1.0
 
-EventActivityNet is a large-scale simulated event-camera dataset derived from ActivityNet videos for action understanding in long, untrimmed event streams.
+## Dataset Summary
 
-This repository is currently being prepared for public release.
+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 card and download instructions will be added soon.
+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.

docs/CHANGELOG.md

@@ -0,0 +1,26 @@
+# 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.
+

docs/DATASET_CARD.md

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+# 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.

docs/DATASET_FORMAT.md

@@ -0,0 +1,120 @@
+# 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
+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 |
+| Chunking | `(1024,)` |
+
+### `voxel_event_count`
+
+| 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.
+

docs/DATASET_GENERATION.md

@@ -0,0 +1,101 @@
+# 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.

docs/LICENSE_NOTES.md

@@ -0,0 +1,37 @@
+# License Notes
+
+These notes are not legal advice. They summarize release-relevant provenance and licensing considerations for researchers using EventActivityNet v1.0.
+
+## Source Data Obligations
+
+EventActivityNet v1.0 is derived from ActivityNet / ActivityNet Captions videos and annotations. The original ActivityNet and ActivityNet Captions source terms, licenses, and citation obligations still apply.
+
+The EventActivityNet release contains generated HDF5 event voxel representations and metadata derived from those source videos and annotations. It does not remove, replace, or supersede the usage requirements of the source datasets.
+
+Users are responsible for complying with the original ActivityNet and ActivityNet Captions licenses when using or redistributing EventActivityNet.
+
+EventActivityNet grants no additional usage rights beyond those provided by the original ActivityNet and ActivityNet Captions datasets.
+
+## User Responsibilities
+
+Users are responsible for:
+
+- following ActivityNet and ActivityNet Captions usage terms;
+- citing ActivityNet and ActivityNet Captions where required;
+- using the dataset for research purposes consistent with source dataset terms;
+- checking whether their intended use is permitted by the original source licenses and by any hosting-platform terms.
+
+## Redistribution Notes
+
+If redistributing EventActivityNet or derived files:
+
+- preserve provenance that the dataset is derived from ActivityNet / ActivityNet Captions;
+- include release documentation and license notes;
+- do not imply that generated voxel files remove source dataset obligations;
+- avoid exposing local build-machine paths in public manifests or documentation.
+
+## Privacy and Safety
+
+The source videos are public web videos included in ActivityNet. EventActivityNet v1.0 is intended for research use, not for identifying people, surveillance, biometric recognition, or consequential decision-making about individuals.
+
+The final release audit found no remaining HDF5 integrity problems. Public documentation should continue to avoid local filesystem paths and unrelated private metadata.

docs/RELEASE_NOTES.md

@@ -0,0 +1,63 @@
+# EventActivityNet v1.0 Release Notes
+
+## 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.
+

metadata/shard_summary.json

@@ -0,0 +1,68 @@
+{
+  "dataset_repo_id": "IIS-CVL/EventActivityNet",
+  "expected_h5_count": 3263,
+  "hard_shard_size_cap_bytes": 22000000000,
+  "individual_h5_larger_than_hard_cap": [
+    {
+      "h5_size_bytes": 22759443052,
+      "split": "train",
+      "video_id": "v_YtgiDWEY_1A"
+    }
+  ],
+  "individual_h5_larger_than_target": [
+    {
+      "h5_size_bytes": 22759443052,
+      "split": "train",
+      "video_id": "v_YtgiDWEY_1A"
+    },
+    {
+      "h5_size_bytes": 21833322280,
+      "split": "train",
+      "video_id": "v_MZQmJZ_31O0"
+    }
+  ],
+  "overall_shard_size_bytes": {
+    "max": 22759443052,
+    "mean": 19889250663.219177,
+    "median": 19980668969,
+    "min": 2530106597
+  },
+  "planned_h5_count": 3263,
+  "planned_shards_larger_than_hard_cap": [
+    {
+      "allowed_reason": "single H5 exceeds hard cap",
+      "member_count": 1,
+      "planned_size_bytes": 22759443052,
+      "repo_path": "data/train/train-00153-of-00157.tar",
+      "split": "train"
+    }
+  ],
+  "split_counts": {
+    "train": 2316,
+    "validation": 947
+  },
+  "splits": {
+    "train": {
+      "max_shard_size_bytes": 22759443052,
+      "mean_shard_size_bytes": 19965218446.802547,
+      "median_shard_size_bytes": 19980692500,
+      "member_count": 2316,
+      "min_shard_size_bytes": 13563523703,
+      "shard_count": 157,
+      "total_size_bytes": 3134539296148
+    },
+    "validation": {
+      "max_shard_size_bytes": 19999064693,
+      "mean_shard_size_bytes": 19696880630.596775,
+      "median_shard_size_bytes": 19979562880.5,
+      "member_count": 947,
+      "min_shard_size_bytes": 2530106597,
+      "shard_count": 62,
+      "total_size_bytes": 1221206599097
+    }
+  },
+  "status": "dry_run_plan",
+  "target_shard_size_bytes": 20000000000,
+  "total_payload_size_bytes": 4355745895245,
+  "total_shard_count": 219
+}

scales/event_friendly_statistics.json

@@ -0,0 +1,57 @@
+{
+  "definition": {
+    "caption_keyword_match": "len(event_keyword_hits) > 0",
+    "darkness_triggered_match": "dark_first_frame == true",
+    "event_friendly": "caption_keyword_match OR darkness_triggered_match"
+  },
+  "scales": {
+    "large": {
+      "both_keyword_and_darkness": 453,
+      "both_percentage": 13.8829298191848,
+      "caption_keyword_matches": 630,
+      "caption_keyword_percentage": 19.307385841250383,
+      "darkness_triggered_matches": 1954,
+      "darkness_triggered_percentage": 59.88354275206865,
+      "derived_event_friendly_videos": 2131,
+      "event_flag_inconsistent_count": 0,
+      "event_flag_inconsistent_video_ids": [],
+      "event_friendly_percentage": 65.30799877413423,
+      "event_friendly_videos": 2131,
+      "neither": 1132,
+      "neither_percentage": 34.692001225865766,
+      "total_videos": 3263
+    },
+    "medium": {
+      "both_keyword_and_darkness": 215,
+      "both_percentage": 13.98828887443071,
+      "caption_keyword_matches": 297,
+      "caption_keyword_percentage": 19.32335718932986,
+      "darkness_triggered_matches": 914,
+      "darkness_triggered_percentage": 59.466493168510084,
+      "derived_event_friendly_videos": 996,
+      "event_flag_inconsistent_count": 0,
+      "event_flag_inconsistent_video_ids": [],
+      "event_friendly_percentage": 64.80156148340924,
+      "event_friendly_videos": 996,
+      "neither": 541,
+      "neither_percentage": 35.19843851659076,
+      "total_videos": 1537
+    },
+    "small": {
+      "both_keyword_and_darkness": 83,
+      "both_percentage": 12.443778110944528,
+      "caption_keyword_matches": 118,
+      "caption_keyword_percentage": 17.691154422788603,
+      "darkness_triggered_matches": 396,
+      "darkness_triggered_percentage": 59.37031484257871,
+      "derived_event_friendly_videos": 431,
+      "event_flag_inconsistent_count": 0,
+      "event_flag_inconsistent_video_ids": [],
+      "event_friendly_percentage": 64.61769115442279,
+      "event_friendly_videos": 431,
+      "neither": 236,
+      "neither_percentage": 35.38230884557721,
+      "total_videos": 667
+    }
+  }
+}

scales/large_ids.txt

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scales/scale_comparison.md

@@ -0,0 +1,140 @@
+# EventActivityNet Scale Comparison Audit
+
+Status: final scaling verification audit for the candidate Large / Medium / Small release manifests. No manifests or H5 files were modified.
+
+## Executive Conclusion
+
+- The recovered Large manifest exactly matches `the recovered original Large subset manifest` by video ID.
+- `Small` is a strict subset of `Medium`, and `Medium` is a strict subset of `Large`.
+- All three scales preserve all 200 action classes present in the recovered Large release.
+- The scales are suitable for public release as candidate manifests, with the caveats listed below.
+
+## Part A - Large Provenance Verification
+
+### Confirmed By Code
+
+| Principle | Evidence |
+|---|---|
+| Train/validation merge before sampling | `load_metadata(paths)` merges every provided metadata file into one `meta` dict before class sampling. The sampling loop iterates over `metadata.keys()` without split-specific quotas. |
+| Seed | `random.Random(args.seed)` and `np.random.seed(args.seed)`; default `--seed 2025`. |
+| Minimum-per-class rule | Initial class-balanced stage uses `k = max(5, int(args.class_ratio * len(vids)))`; default `class_ratio=0.2`. |
+| Duration stratification | `length_bucket_edges(durs)` uses `np.quantile(durations, [0.33, 0.66])`. |
+| Event-friendly definition | `event_friendly()` returns caption keyword match OR first-frame mean below threshold. |
+| Event-friendly keywords | `run, fast, sprint, night, dark, slow-motion` |
+| Darkness threshold | default `--brightness-threshold 0.4`. |
+
+### Confirmed By Data
+
+- Candidate train/validation pool: 14,926 videos, 200 classes.
+- Reproduced class-balanced stage: 2,908 videos, 200 classes.
+- Reproduced length-balanced stage: 2,888 videos, 200 classes.
+- Original sample_subset caption-duration quantiles: q33=76.306200s, q66=151.323400s.
+- Large equals `subset_caps_20p.json` by ID: `True`.
+- Large split counts: train 2,316, validation 947.
+- Large event-friendly ratio from manifest fields: 0.653080.
+- Event flags are consistent with `keyword OR dark_first_frame` fields: `True`.
+- Final Large classes below the initial seed-stage per-class quota: 54. This is a consequence of later global length balancing, not evidence of a different subset.
+
+### Inferred
+
+- The released Large subset satisfies the recovered curation principles as the final output of `sample_subset.py`: it matches the original subset list exactly, preserves all 200 local classes, and reflects the expected duration/event/class-balanced structure.
+- Medium and Small preserve the Large release principles by deterministic nested stratification over `(split, class_label, duration_bucket, event_friendly)`, but they are newly generated derivatives rather than historical `sample_subset.py` outputs.
+
+### Deviations / Historical Inconsistencies
+
+- **final_large_not_constrained_by_initial_min_per_class_rule**: `sample_subset.py` enforces `max(5, int(class_ratio * class_count))` only during the initial class-balanced stage. The later length-balancing stage samples globally from that pool, so the final Large manifest is not expected to retain those initial per-class quotas. Affected classes: 54; largest shortfalls: Swimming 6/15, Shaving 6/14, Baton twirling 13/20, Cleaning windows 8/14, Shoveling snow 9/15, Table soccer 8/14, Archery 9/14, Clipping cat claws 8/13.
+- **release_scale_duration_buckets_use_src_fmt_dur_not_original_caption_metadata_duration**: The scale manifests use src_fmt_dur from verify_all.csv per the scale-construction requirement. sample_subset.py used metadata[v]["duration"] for its original length balancing.
+- **class_count_claim_203_not_supported_by_local_sources**: Local taxonomy/candidate/final files contain and preserve 200 classes; see audit/missing_classes_analysis.md.
+
+## Part B - Scale Comparison
+
+| Scale | Videos | Hours | Avg dur (s) | Median dur (s) | Train | Validation | Classes | Event-friendly |
+|---|---:|---:|---:|---:|---:|---:|---:|---:|
+| Large | 3,263 | 106.941600381 | 117.986 | 114.242 | 2,316 | 947 | 200 | 0.653080 |
+| Medium | 1,537 | 50.000000128 | 117.111 | 114.056 | 1,074 | 463 | 200 | 0.648016 |
+| Small | 667 | 20.000000374 | 107.946 | 102.864 | 473 | 194 | 200 | 0.646177 |
+
+### Duration Bucket Distribution
+
+| Scale | Short | Medium | Long | Short ratio | Medium ratio | Long ratio |
+|---|---:|---:|---:|---:|---:|---:|
+| Large | 1,077 | 1,076 | 1,110 | 0.330064 | 0.329758 | 0.340178 |
+| Medium | 512 | 500 | 525 | 0.333116 | 0.325309 | 0.341574 |
+| Small | 247 | 226 | 194 | 0.370315 | 0.338831 | 0.290855 |
+
+### Per-Class Deviation From Large
+
+| Scale | Max ratio deviation | Mean ratio deviation | Missing Large classes |
+|---|---:|---:|---:|
+| Large | 0.000000000 | 0.000000000 | 0 |
+| Medium | 0.003145020 | 0.000548689 | 0 |
+| Small | 0.006468877 | 0.001455918 | 0 |
+
+### Largest Class Gains / Losses
+
+#### Medium vs Large
+
+Largest gains by class ratio:
+- Snowboarding: delta 0.001640, medium count 11, Large count 18
+- Building sandcastles: delta 0.001603, medium count 10, Large count 16
+- Clean and jerk: delta 0.001565, medium count 9, Large count 14
+- Fixing bicycle: delta 0.001527, medium count 8, Large count 12
+- Cleaning sink: delta 0.001452, medium count 6, Large count 8
+
+Largest losses by class ratio:
+- Removing ice from car: delta -0.003145, medium count 6, Large count 23
+- Making a cake: delta -0.001957, medium count 5, Large count 17
+- Mooping floor: delta -0.001919, medium count 6, Large count 19
+- Cutting the grass: delta -0.001613, medium count 6, Large count 18
+- Sumo: delta -0.001575, medium count 7, Large count 20
+
+#### Small vs Large
+
+Largest gains by class ratio:
+- Washing dishes: delta 0.004059, small count 7, Large count 21
+- Discus throw: delta 0.004026, small count 8, Large count 26
+- Sharpening knives: delta 0.003819, small count 5, Large count 12
+- Wrapping presents: delta 0.003786, small count 6, Large count 17
+- Table soccer: delta 0.003545, small count 4, Large count 8
+
+Largest losses by class ratio:
+- Skiing: delta -0.006469, small count 1, Large count 26
+- Capoeira: delta -0.004663, small count 2, Large count 25
+- Pole vault: delta -0.003777, small count 3, Large count 27
+- Cheerleading: delta -0.003744, small count 2, Large count 22
+- Playing harmonica: delta -0.003711, small count 1, Large count 17
+
+### Nestedness
+
+- Small strict subset of Medium: `True`
+- Medium strict subset of Large: `True`
+- All IDs unique per scale: `{'large': True, 'medium': True, 'small': True}`
+
+### Published Paper Duration Comparison
+
+Published Large duration: 107.3 h.
+
+| Duration source | Hours | Difference from 107.3 h |
+|---|---:|---:|
+| Release manifest `src_fmt_dur` | 106.941600381 | -0.358399619 |
+| `subset_caps_20p.json` caption duration | 106.952283333 | -0.347716667 |
+| `verify_all.csv` `src_dur` | 106.848348413 | -0.451651587 |
+| `verify_all.csv` `src_fmt_dur` | 106.941600381 | -0.358399619 |
+| `verify_all.csv` `src_dur_est` | 106.782090037 | -0.517909963 |
+| `verify_all.csv` `dst_dur` | 106.785122166 | -0.514877834 |
+| `verify_all.csv` `dst_fmt_dur` | 106.939227018 | -0.360772982 |
+| `verify_all.csv` `dst_dur_est` | 106.781256732 | -0.518743268 |
+
+The recovered Large IDs exactly match subset_caps_20p.json, so the observed 107.3 h discrepancy does not indicate a different historical subset. It is not explained solely by src_fmt_dur versus other local metadata fields; all local fields are around 106.78-106.95 h. Treat 107.3 h as an approximate/reporting duration unless another historical duration source is recovered.
+
+## Part C - Release Recommendation
+
+Recommendation: **Suitable as candidate public release manifests after documenting caveats.**
+
+Remaining caveats:
+- Use 200 classes, not 203, for this recovered release.
+- Medium and Small are newly generated deterministic nested candidates; they are distribution-preserving derivatives of Large, not historical sample_subset.py outputs.
+- Authoritative scale durations use src_fmt_dur from verify_all.csv. The original subset file caption durations and all local verify_all duration fields are close but do not equal the paper statement of 107.3 h exactly.
+- The final Large list can have classes below the initial max(5, int(class_ratio * class_count)) seed-stage minimum because the original script did not enforce that constraint after length balancing.
+
+Machine-readable details are in `scale_comparison.json`.

scales/scale_statistics.json

@@ -0,0 +1,798 @@
+{
+  "duration_authority": {
+    "caption_duration_hours": 106.95228333333336,
+    "chosen_duration_field": "src_fmt_dur",
+    "chosen_field": "src_fmt_dur",
+    "duration_bucket_edges_seconds": {
+      "q33": 76.1897794,
+      "q66": 152.708354
+    },
+    "duration_field_comparison": [
+      {
+        "abs_diff_from_caption_hours": 0.01068295194464497,
+        "abs_diff_from_reported_107_3_hours": 0.358399618611287,
+        "field": "src_fmt_dur",
+        "hours": 106.94160038138871,
+        "invalid_count": 0,
+        "missing_count": 0,
+        "valid_count": 3263
+      },
+      {
+        "abs_diff_from_caption_hours": 0.013056315277921726,
+        "abs_diff_from_reported_107_3_hours": 0.36077298194456375,
+        "field": "dst_fmt_dur",
+        "hours": 106.93922701805543,
+        "invalid_count": 0,
+        "missing_count": 0,
+        "valid_count": 3263
+      },
+      {
+        "abs_diff_from_caption_hours": 0.10393492027795048,
+        "abs_diff_from_reported_107_3_hours": 0.4516515869445925,
+        "field": "src_dur",
+        "hours": 106.8483484130554,
+        "invalid_count": 0,
+        "missing_count": 0,
+        "valid_count": 3263
+      },
+      {
+        "abs_diff_from_caption_hours": 0.16716116749984167,
+        "abs_diff_from_reported_107_3_hours": 0.5148778341664837,
+        "field": "dst_dur",
+        "hours": 106.78512216583351,
+        "invalid_count": 0,
+        "missing_count": 0,
+        "valid_count": 3263
+      },
+      {
+        "abs_diff_from_caption_hours": 0.1701932961113357,
+        "abs_diff_from_reported_107_3_hours": 0.5179099627779777,
+        "field": "src_dur_est",
+        "hours": 106.78209003722202,
+        "invalid_count": 0,
+        "missing_count": 0,
+        "valid_count": 3263
+      },
+      {
+        "abs_diff_from_caption_hours": 0.17102660138884573,
+        "abs_diff_from_reported_107_3_hours": 0.5187432680554878,
+        "field": "dst_dur_est",
+        "hours": 106.78125673194451,
+        "invalid_count": 0,
+        "missing_count": 0,
+        "valid_count": 3263
+      }
+    ],
+    "large_duration_hours_authoritative": 106.94160038138888
+  },
+  "event_friendly_definition": {
+    "brightness_threshold": 0.4,
+    "dark_rule": "ffmpeg first frame mean gray value < threshold",
+    "keywords": [
+      "run",
+      "fast",
+      "sprint",
+      "night",
+      "dark",
+      "slow-motion"
+    ],
+    "text_rule": "any keyword in lower-cased joined captions"
+  },
+  "scales": {
+    "large": {
+      "class_count": 200,
+      "class_counts": {
+        "Applying sunscreen": 17,
+        "Archery": 9,
+        "Arm wrestling": 18,
+        "Assembling bicycle": 13,
+        "BMX": 22,
+        "Baking cookies": 21,
+        "Ballet": 17,
+        "Bathing dog": 12,
+        "Baton twirling": 13,
+        "Beach soccer": 18,
+        "Beer pong": 12,
+        "Belly dance": 11,
+        "Blow-drying hair": 13,
+        "Blowing leaves": 17,
+        "Braiding hair": 13,
+        "Breakdancing": 19,
+        "Brushing hair": 16,
+        "Brushing teeth": 16,
+        "Building sandcastles": 16,
+        "Bullfighting": 22,
+        "Bungee jumping": 10,
+        "Calf roping": 23,
+        "Camel ride": 16,
+        "Canoeing": 30,
+        "Capoeira": 25,
+        "Carving jack-o-lanterns": 12,
+        "Changing car wheel": 16,
+        "Cheerleading": 22,
+        "Chopping wood": 22,
+        "Clean and jerk": 14,
+        "Cleaning shoes": 13,
+        "Cleaning sink": 8,
+        "Cleaning windows": 8,
+        "Clipping cat claws": 8,
+        "Cricket": 18,
+        "Croquet": 19,
+        "Cumbia": 12,
+        "Curling": 21,
+        "Cutting the grass": 18,
+        "Decorating the Christmas tree": 9,
+        "Disc dog": 27,
+        "Discus throw": 26,
+        "Dodgeball": 13,
+        "Doing a powerbomb": 26,
+        "Doing crunches": 15,
+        "Doing fencing": 23,
+        "Doing karate": 12,
+        "Doing kickboxing": 16,
+        "Doing motocross": 23,
+        "Doing nails": 13,
+        "Doing step aerobics": 19,
+        "Drinking beer": 12,
+        "Drinking coffee": 13,
+        "Drum corps": 12,
+        "Elliptical trainer": 22,
+        "Fixing bicycle": 12,
+        "Fixing the roof": 15,
+        "Fun sliding down": 12,
+        "Futsal": 24,
+        "Gargling mouthwash": 13,
+        "Getting a haircut": 18,
+        "Getting a piercing": 12,
+        "Getting a tattoo": 16,
+        "Grooming dog": 11,
+        "Grooming horse": 9,
+        "Hammer throw": 17,
+        "Hand car wash": 20,
+        "Hand washing clothes": 14,
+        "Hanging wallpaper": 17,
+        "Having an ice cream": 10,
+        "High jump": 15,
+        "Hitting a pinata": 17,
+        "Hopscotch": 17,
+        "Horseback riding": 17,
+        "Hula hoop": 10,
+        "Hurling": 18,
+        "Ice fishing": 12,
+        "Installing carpet": 11,
+        "Ironing clothes": 12,
+        "Javelin throw": 23,
+        "Kayaking": 22,
+        "Kite flying": 14,
+        "Kneeling": 21,
+        "Knitting": 13,
+        "Laying tile": 9,
+        "Layup drill in basketball": 19,
+        "Long jump": 21,
+        "Longboarding": 26,
+        "Making a cake": 17,
+        "Making a lemonade": 20,
+        "Making a sandwich": 17,
+        "Making an omelette": 4,
+        "Mixing drinks": 23,
+        "Mooping floor": 19,
+        "Mowing the lawn": 16,
+        "Paintball": 20,
+        "Painting": 11,
+        "Painting fence": 12,
+        "Painting furniture": 11,
+        "Peeling potatoes": 10,
+        "Ping-pong": 12,
+        "Plastering": 11,
+        "Plataform diving": 22,
+        "Playing accordion": 15,
+        "Playing badminton": 13,
+        "Playing bagpipes": 16,
+        "Playing beach volleyball": 11,
+        "Playing blackjack": 13,
+        "Playing congas": 25,
+        "Playing drums": 20,
+        "Playing field hockey": 20,
+        "Playing flauta": 14,
+        "Playing guitarra": 22,
+        "Playing harmonica": 17,
+        "Playing ice hockey": 18,
+        "Playing kickball": 15,
+        "Playing lacrosse": 23,
+        "Playing piano": 17,
+        "Playing polo": 13,
+        "Playing pool": 15,
+        "Playing racquetball": 10,
+        "Playing rubik cube": 19,
+        "Playing saxophone": 11,
+        "Playing squash": 19,
+        "Playing ten pins": 21,
+        "Playing violin": 18,
+        "Playing water polo": 23,
+        "Pole vault": 27,
+        "Polishing forniture": 11,
+        "Polishing shoes": 10,
+        "Powerbocking": 21,
+        "Preparing pasta": 14,
+        "Preparing salad": 23,
+        "Putting in contact lenses": 19,
+        "Putting on makeup": 19,
+        "Putting on shoes": 12,
+        "Rafting": 27,
+        "Raking leaves": 9,
+        "Removing curlers": 10,
+        "Removing ice from car": 23,
+        "Riding bumper cars": 12,
+        "River tubing": 18,
+        "Rock climbing": 16,
+        "Rock-paper-scissors": 17,
+        "Rollerblading": 16,
+        "Roof shingle removal": 13,
+        "Rope skipping": 20,
+        "Running a marathon": 32,
+        "Sailing": 16,
+        "Scuba diving": 20,
+        "Sharpening knives": 12,
+        "Shaving": 6,
+        "Shaving legs": 18,
+        "Shot put": 15,
+        "Shoveling snow": 9,
+        "Shuffleboard": 15,
+        "Skateboarding": 12,
+        "Skiing": 26,
+        "Slacklining": 18,
+        "Smoking a cigarette": 18,
+        "Smoking hookah": 13,
+        "Snatch": 22,
+        "Snow tubing": 20,
+        "Snowboarding": 18,
+        "Spinning": 16,
+        "Spread mulch": 8,
+        "Springboard diving": 13,
+        "Starting a campfire": 19,
+        "Sumo": 20,
+        "Surfing": 20,
+        "Swimming": 6,
+        "Swinging at the playground": 16,
+        "Table soccer": 8,
+        "Tai chi": 12,
+        "Tango": 16,
+        "Tennis serve with ball bouncing": 16,
+        "Throwing darts": 17,
+        "Trimming branches or hedges": 18,
+        "Triple jump": 26,
+        "Tug of war": 15,
+        "Tumbling": 16,
+        "Using parallel bars": 18,
+        "Using the balance beam": 29,
+        "Using the monkey bar": 15,
+        "Using the pommel horse": 14,
+        "Using the rowing machine": 19,
+        "Using uneven bars": 14,
+        "Vacuuming floor": 18,
+        "Volleyball": 7,
+        "Wakeboarding": 21,
+        "Walking the dog": 14,
+        "Washing dishes": 21,
+        "Washing face": 12,
+        "Washing hands": 16,
+        "Waterskiing": 20,
+        "Waxing skis": 15,
+        "Welding": 18,
+        "Windsurfing": 15,
+        "Wrapping presents": 17,
+        "Zumba": 10
+      },
+      "duration_bucket_counts": {
+        "long": 1110,
+        "mid": 1076,
+        "short": 1077
+      },
+      "duration_bucket_ratios": {
+        "long": 0.34017775053631627,
+        "mid": 0.3297578915108796,
+        "short": 0.33006435795280414
+      },
+      "duration_seconds": 384989.761373,
+      "event_friendly_count": 2131,
+      "event_friendly_ratio": 0.6530799877413423,
+      "extra_classes_not_in_large": [],
+      "hours": 106.94160038138888,
+      "max_per_class_ratio_deviation_from_large": 0.0,
+      "mean_per_class_ratio_deviation_from_large": 0.0,
+      "missing_classes_from_large": [],
+      "split_counts": {
+        "train": 2316,
+        "validation": 947
+      },
+      "split_ratios": {
+        "train": 0.7097762794973951,
+        "validation": 0.290223720502605
+      },
+      "video_count": 3263
+    },
+    "medium": {
+      "class_count": 200,
+      "class_counts": {
+        "Applying sunscreen": 8,
+        "Archery": 4,
+        "Arm wrestling": 9,
+        "Assembling bicycle": 7,
+        "BMX": 11,
+        "Baking cookies": 10,
+        "Ballet": 9,
+        "Bathing dog": 7,
+        "Baton twirling": 5,
+        "Beach soccer": 9,
+        "Beer pong": 5,
+        "Belly dance": 7,
+        "Blow-drying hair": 6,
+        "Blowing leaves": 8,
+        "Braiding hair": 8,
+        "Breakdancing": 9,
+        "Brushing hair": 9,
+        "Brushing teeth": 7,
+        "Building sandcastles": 10,
+        "Bullfighting": 11,
+        "Bungee jumping": 5,
+        "Calf roping": 12,
+        "Camel ride": 8,
+        "Canoeing": 13,
+        "Capoeira": 12,
+        "Carving jack-o-lanterns": 6,
+        "Changing car wheel": 7,
+        "Cheerleading": 10,
+        "Chopping wood": 9,
+        "Clean and jerk": 9,
+        "Cleaning shoes": 6,
+        "Cleaning sink": 6,
+        "Cleaning windows": 4,
+        "Clipping cat claws": 4,
+        "Cricket": 9,
+        "Croquet": 9,
+        "Cumbia": 6,
+        "Curling": 10,
+        "Cutting the grass": 6,
+        "Decorating the Christmas tree": 6,
+        "Disc dog": 12,
+        "Discus throw": 12,
+        "Dodgeball": 6,
+        "Doing a powerbomb": 12,
+        "Doing crunches": 9,
+        "Doing fencing": 10,
+        "Doing karate": 5,
+        "Doing kickboxing": 9,
+        "Doing motocross": 12,
+        "Doing nails": 7,
+        "Doing step aerobics": 11,
+        "Drinking beer": 5,
+        "Drinking coffee": 6,
+        "Drum corps": 5,
+        "Elliptical trainer": 9,
+        "Fixing bicycle": 8,
+        "Fixing the roof": 5,
+        "Fun sliding down": 6,
+        "Futsal": 11,
+        "Gargling mouthwash": 6,
+        "Getting a haircut": 8,
+        "Getting a piercing": 5,
+        "Getting a tattoo": 8,
+        "Grooming dog": 6,
+        "Grooming horse": 5,
+        "Hammer throw": 7,
+        "Hand car wash": 11,
+        "Hand washing clothes": 7,
+        "Hanging wallpaper": 7,
+        "Having an ice cream": 5,
+        "High jump": 7,
+        "Hitting a pinata": 8,
+        "Hopscotch": 9,
+        "Horseback riding": 8,
+        "Hula hoop": 3,
+        "Hurling": 9,
+        "Ice fishing": 7,
+        "Installing carpet": 5,
+        "Ironing clothes": 5,
+        "Javelin throw": 9,
+        "Kayaking": 11,
+        "Kite flying": 6,
+        "Kneeling": 10,
+        "Knitting": 5,
+        "Laying tile": 3,
+        "Layup drill in basketball": 9,
+        "Long jump": 12,
+        "Longboarding": 12,
+        "Making a cake": 5,
+        "Making a lemonade": 8,
+        "Making a sandwich": 8,
+        "Making an omelette": 2,
+        "Mixing drinks": 12,
+        "Mooping floor": 6,
+        "Mowing the lawn": 6,
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