# 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.