---
pretty_name: SimAct Video
language:
- en
size_categories:
- 100K<n<1M
---

# SimAct Video

SimAct Video contains before/after image pairs for short human action segments, converted from multiple video action-recognition datasets into a shared JSONL + tar layout.

Each JSONL row points to two images: `precon` is the starting visual state and `postcon` is the ending visual state. Image paths are relative to the repository root and match paths stored inside the tar chunks.

## Dataset Summary

This release contains 543,005 examples and 1,086,010 images across 6 datasets. Images are packaged into 35 tar chunks.

| Dataset | Train | Validation | Test | Total |
|---|---:|---:|---:|---:|
| `ego4d_fho_lta` | 63,933 | 33,095 | 0 | 97,028 |
| `fineaction` | 57,698 | 24,218 | 0 | 81,916 |
| `holoassist` | 124,752 | 17,902 | 0 | 142,654 |
| `hd_epic` | 0 | 59,415 | 0 | 59,415 |
| `assembly101` | 47,539 | 15,675 | 21,893 | 85,107 |
| `epic_kitchens_100` | 67,217 | 9,668 | 0 | 76,885 |
| **Total** | **361,139** | **159,973** | **21,893** | **543,005** |

## Repository Layout

Files are organized under `data/<dataset>/`:

```text
data/
  <dataset>/
    train.jsonl
    validation.jsonl
    test.jsonl                  # only when available
    train_chunk_000.tar
    train_chunk_001.tar
    validation_chunk_000.tar
    test_chunk_000.tar          # only when available
    train.done
    validation.done
    test.done                   # only when available
```

The tar files contain images at paths like:

```text
data/<dataset>/<split>/images/<id>_pre.jpg
data/<dataset>/<split>/images/<id>_post.jpg
```

The same relative paths are used in the JSONL `precon` and `postcon` fields.

## JSONL Schema

Each split file is a JSON Lines file. Each line is one example with this schema:

```json
{
  "dataset": "string",
  "split": "train | validation | test",
  "id": "string",
  "original_id": "string",
  "precon": "string",
  "postcon": "string",
  "source": "string",
  "raw_action": {
    "sentence": "string",
    "verb": ["string"],
    "noun": ["string"]
  }
}
```

Field meanings:

| Field | Type | Description |
|---|---|---|
| `dataset` | string | Dataset name used in this release, such as `fineaction` or `epic_kitchens_100`. |
| `split` | string | Split name: `train`, `validation`, or `test`. |
| `id` | string | Release-stable example id generated by this conversion, formatted as `<dataset>_<split>_<8-digit-index>`. |
| `original_id` | string | Original dataset/triplet id before remapping. Use this for tracing back to the source annotations. |
| `precon` | string | Repository-relative path to the start-state image. |
| `postcon` | string | Repository-relative path to the end-state image. |
| `source` | string | Short description of the source annotation file or field used to build the action segment. |
| `raw_action.sentence` | string | Natural-language action text from the source dataset when available; otherwise `""`. |
| `raw_action.verb` | list[string] | Source verb label(s) when available; otherwise `[]`. Single-label datasets still use a list. |
| `raw_action.noun` | list[string] | Source noun/object label(s) when available; otherwise `[]`. Single-label datasets still use a list. |

`raw_action` preserves whatever the source dataset provides. Some datasets provide only a natural-language label, some provide verb/noun labels, and some provide both. Missing values are represented as an empty string for `sentence` and empty lists for `verb` or `noun`.

Example row:

```json
{"dataset":"hd_epic","split":"validation","id":"hd_epic_validation_00000000","original_id":"P01-20240202-110250-1","precon":"data/hd_epic/validation/images/hd_epic_validation_00000000_pre.jpg","postcon":"data/hd_epic/validation/images/hd_epic_validation_00000000_post.jpg","source":"HD_EPIC_Narrations.pkl narration + verbs/nouns lists","raw_action":{"sentence":"Open the upper cupboard by holding the handle of the cupboard with the left hand.","verb":["open","hold"],"noun":["upper cupboard","handle of cupboard"]}}
```