---
license: mit
task_categories:
  - video-classification
tags:
  - inverse-dynamics
  - screen-recording
  - action-recognition
  - macos
  - vlm-benchmark
pretty_name: IDM Eval Set
size_categories:
  - n<1K
---

# IDM Eval Set

A validation set for evaluating **Inverse Dynamics Models** on macOS screen recordings. Each sample is a 5-second clip of real desktop usage (browser, IDE, terminal) paired with a ground-truth action log captured at the OS level.

The task: given a short screen recording, predict the sequence of user input actions (keypresses, mouse clicks, scrolls) that produced the observed screen changes.

## Dataset Structure

```
clips_recording_{uuid}_seg{N}/
  clip_000_{tag}.mp4       # 5s screen recording (1728x1080)
  clip_000_{tag}.json      # ground truth action log
annotations.json           # visibility labels per action
gt_overrides.json          # manual corrections to GT details
```

## Stats

| | |
|---|---|
| Clips | 51 |
| Recordings | 11 |
| Total raw actions | 10,914 |
| Resolution | 1728 x 1080 |
| Clip duration | 5 seconds |

**Tag distribution:**

| Tag | Count |
|---|---|
| scroll/drag | 18 |
| keystroke-heavy | 17 |
| mixed | 4 |
| click-heavy | 4 |
| hotkeys | 2 |
| hard-case | 1 |

## Action Log Format

Each clip JSON contains:

```json
{
  "start_s": 206.913,
  "end_s": 211.913,
  "tag": "keystroke-heavy",
  "actions": [
    [206933331, ["KeyPress", [32, "Space"]]],
    [207233331, ["KeyRelease", [32, "Space"]]],
    [208633331, ["MousePress", ["Left", 0, 0]]],
    [209533331, ["MouseScroll", [0, -1, 0, 0]]]
  ]
}
```

- Timestamps are **absolute microseconds** (subtract `start_s * 1e6` for clip-relative)
- Action types: `KeyPress`, `KeyRelease`, `MousePress`, `MouseRelease`, `MouseMove`, `MouseScroll`, `ContextChanged`
- KeyPress params: `[keycode, key_name]`
- MousePress params: `[button, x, y]` (coordinates not captured in this version)
- MouseScroll params: `[dx, dy, x, y]`

## Annotations

`annotations.json` contains manual visibility labels for each primary action (KeyPress, MousePress, MouseScroll) in each clip. Keys are clip paths, values map action indices to visibility labels:

| Label | Count | Meaning |
|---|---|---|
| `visible` | 485 | Effect is directly visible in the frames |
| `inferable` | 273 | Effect can be inferred but isn't directly visible |
| `ambiguous` | 25 | Action type is unclear from video (e.g. scroll via mouse vs keyboard) |
| `not_predictable` | 27 | Cannot be predicted from video alone |

```json
{
  "clips_recording_.../clip_003_keystroke-heavy": {
    "0": "visible",
    "1": "inferable",
    "2": "ambiguous",
    "3": "not_predictable"
  }
}
```

Use these to filter ground truth when scoring — e.g. exclude `not_predictable` and optionally `ambiguous` actions from recall calculations.

## GT Overrides

`gt_overrides.json` contains manual corrections to ground-truth action details (e.g. when a modifier key was held from before the clip). Structure:

```json
{
  "clips_recording_.../clip_name": {
    "edits": {"5": "Cmd+Tab"},
    "deletions": [],
    "additions": [{"frame": 8, "type": "KeyPress", "detail": "Space"}]
  }
}
```

Apply overrides after processing raw GT through `filter_gt_actions` + `coalesce_gt_events`.


## Gesture Evaluation (Mouse Movement + Scroll)

In addition to sparse event evaluation (KeyPress, MouseClick, MouseScroll), this dataset supports **gesture evaluation**: predicting per-frame mouse cursor movement and scroll magnitude.

### Gesture GT Files

- **`gesture_gt_exp.json`** — Exponential bin indices (±1 to ±9 per axis)
- **`gesture_gt_norm.json`** — Normalized 0-1000 scale (resolution-independent)

Both are derived from the raw `MouseMove` and `MouseScroll` events in each clip JSON. Mouse deltas are accumulated per frame (5fps), normalized by video resolution, then binned (exp mode) or kept as integers (norm mode). Scroll sign convention: positive = scroll down (content moves up).

### Format



- **MouseMove details**: `"dx,dy"` — signed bin indices (exp) or normalized integers (norm). Positive dx = right, positive dy = down.
- **MouseScroll details**: signed bin index or normalized integer. Positive = scroll down.

### Exponential Bin Scale (mouse dx/dy)

| Bin | Normalized range (0-1000) |
|-----|--------------------------|
| ±1  | 0-1                      |
| ±2  | 1-3                      |
| ±3  | 3-7                      |
| ±4  | 7-16                     |
| ±5  | 16-40                    |
| ±6  | 40-95                    |
| ±7  | 95-230                   |
| ±8  | 230-550                  |
| ±9  | >550                     |

### Metrics

- **R²** (coefficient of determination): primary metric for mouse movement quality. VPT (OpenAI) reports R²=0.97 on their trained IDM.
- **L2**: Euclidean distance between predicted and GT bin vectors (lower = better).
- **F1**: frame-level detection (did the model predict the right frames?).
- **DirAcc**: direction accuracy (did dx/dy signs match?).

### Evaluation