README.md

---
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

![preview](./preview.gif)

A validation set for evaluating **Inverse Dynamics Models** on macOS screen recordings. Each sample is a 5-second clip of real productivity desktop usage (browser, IDE, terminal, docs, dashboards) 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, cursor moves) that produced the observed screen changes.

## Code

Training, inference, and evaluation code is available at
[`p-doom/inverse-dynamics-model`](https://github.com/p-doom/inverse-dynamics-model).

## 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
gesture_gt_norm.json       # per-frame mouse-move / scroll GT (0 to 1000 scale)
gesture_gt_exp.json        # per-frame mouse-move / scroll GT (exponential bins)
```

## Stats

| Metric | Value |
|--------|-------|
| Clips | 44 |
| Recordings | 10 |
| Total raw actions | 8,504 |
| Resolution | 1728 x 1080 |
| Clip duration | 5 seconds |

### Tag Distribution

| Tag | Count |
|-----|-------|
| scroll/drag | 15 |
| keystroke-heavy | 15 |
| click-heavy | 6 |
| hotkeys | 4 |
| mixed | 3 |
| hard-case | 1 |

## Baseline leaderboard

The table below provides reference scores for [p-doom/idm](https://huggingface.co/p-doom/idm) and off-the-shelf VLM baselines on this eval set. Scores use visibility filtering (`visible` + `inferable`) and action-type-specific matching. `MM R²` and `MM cos_mean` include missed MouseMove frames as zero predictions.

| Model            | Overall F1 | KeyPress F1 | MouseClick F1 | MouseMove F1 | MouseScroll F1 | MM R²     | MM cos_mean | MM cov. |
| ---------------- | ---------- | ----------- | ------------- | ------------ | -------------- | --------- | ----------- | ------- |
| **Ours (8B)**    | **0.787**  | 0.791       | 0.598         | **0.857**    | **0.447**      | 0.708     | 0.643       | **92%** |
| Gemini 3.5 Flash | 0.740      | **0.826**   | **0.726**     | 0.760        | 0.337          | **0.714** | 0.560       | 64%     |
| GPT 5.5          | 0.709      | 0.821       | 0.714         | 0.669        | 0.392          | 0.586     | 0.455       | 52%     |
| Kimi K2.6        | 0.540      | 0.711       | 0.444         | 0.381        | 0.326          | 0.420     | 0.177       | 25%     |
| Gemma 4 31B      | 0.430      | 0.381       | 0.581         | 0.500        | 0.237          | 0.077     | 0.228       | 37%     |
| Qwen3-VL 8B      | 0.360      | 0.409       | 0.449         | 0.334        | 0.127          | -6.038    | 0.035       | 28%     |

## 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]]]
  ]
}
```

**Details:**

* Timestamps are **absolute microseconds**. Subtract `start_s * 1e6` for clip-relative.
* Raw event types in the JSON: `KeyPress`, `KeyRelease`, `MousePress`, `MouseRelease`, `MouseMove`, `MouseScroll`, `ContextChanged`.
* For IDM evaluation, the canonical action set is `KeyPress`, `MouseClick`, `MouseScroll`, `MouseMove`. A `MouseClick` is derived from a `MousePress` event (its `MouseRelease` partner is discarded).
* `KeyPress` params: `[keycode, key_name]`.
* `MousePress` params: `[button, x, y]`. The `x`, `y` fields are always `0` in this version (cursor position is not stored on press events); only the button name is meaningful.
* `MouseScroll` params: `[dx, dy, x, y]`.

## Annotations

`annotations.json` contains manual visibility labels for each primary action (KeyPress, MousePress, MouseScroll) in each clip.

| Label | Count | Meaning |
|-------|-------|---------|
| `visible` | 479 | Effect is directly visible in the frames |
| `inferable` | 21 | 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` | 24 | Cannot be predicted from video alone |

**Format:**

```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).

The eval set is strongly **visible-dominated**: the vast majority of annotated actions have a directly observable visual effect, so a competent IDM should be able to recover them from pixels alone without context inference.

## 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"}]
  }
}
```

## Gesture GT (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_norm.json`: normalized 0 to 1000 scale, resolution-independent.
* `gesture_gt_exp.json`: signed exponential bin indices (one bin per magnitude order).

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 either kept as integers (norm) or binned (exp).

**Format:**

* `MouseMove` details: `"dx,dy"`. Positive dx = right, positive dy = down.
* `MouseScroll` details: signed scalar. Positive = scroll down (content moves up).

## Scope

This eval set is **productivity-focused**. It covers IDE, browser, terminal, docs, dashboards, and PDF reading workflows. Gaming and entertainment clips are intentionally excluded so the benchmark targets long-horizon digital-work behavior.