---
license: cc-by-4.0
task_categories:
  - image-classification
language:
  - en
tags:
  - video-games
  - cs2
  - dota2
  - valorant
  - game-classification
pretty_name: Gamed — Game Classification (train + held-out eval)
size_categories:
  - 1K<n<10K
---

# Gamed — Game Classification Dataset

Train and held-out eval frames for a 3-class game classifier
(`cs2`, `dota2`, `valorant`). Used to train and evaluate
[`ybashir/gamed-game-classification`](https://huggingface.co/ybashir/gamed-game-classification),
the §1.1 hot-path detector in [coach-api](https://github.com/bashiryounis/gamed).

The source `.mp4` files are **not** included — see `urls.txt` for the
original YouTube links. All frames here are derived at 0.4 fps via
`ffmpeg` and resized to 224×224 (squashed 1:1).

## Structure

```
train/
  manifest.csv        6450 rows — path,class,video_id,frame_idx,t_sec
  frames_224.zip      6450 JPGs (224×224), grouped by class inside the zip

eval/
  manifest.csv        450 rows  — 150/class evenly-spaced subsample
  manifest_full.csv   1118 rows — full extraction (cs2 390 / dota2 295 / valorant 433)
  frames/
    cs2/      390 JPGs
    dota2/    295 JPGs
    valorant/ 433 JPGs
  predictions.csv      Held-out predictions from the published checkpoint (450)
  predictions_full.csv Held-out predictions from the published checkpoint (1118)
  report.md            Acc + P/R/F1 + confusion matrix (450)
  report_full.md       Acc + P/R/F1 + confusion matrix (1118)

urls.txt              YouTube source URLs (train + eval) + per-video start offsets
```

## Manifest schema

Both `train/manifest.csv` and `eval/manifest.csv` have the same columns:

```csv
path,class,video_id,frame_idx,t_sec[,split]
```

- `path` — relative to the original repo cwd (`gamed/`)
- `class` — `cs2` / `dota2` / `valorant`
- `video_id` — YouTube ID (11 chars), parsed from the source filename prefix
- `frame_idx` — 1-indexed JPG index within the video
- `t_sec` — timestamp in the source video the frame was sampled from
- `split` — only on eval manifests, always `eval`

## Train vs eval — important caveat

Training was done with a **frame-level** `random_split(0.70/0.15/0.15)` on
`train/manifest.csv`. The same `video_id`s appear in train and val/test,
so the 99% accuracy quoted in the original notebook is over-optimistic
(video-level leakage).

The **eval/** split fixes this:
- 3 fresh YouTube videos (different `video_id`, different uploaders)
- Zero `video_id` overlap with the training set
- Reported eval accuracy: **99.91 %** on 1118 frames, **100 %** on the
  150/class subsample

This is the number to cite. Tool to reproduce:
[`cli/gamed_classification_eval.py`](https://github.com/bashiryounis/gamed/blob/main/cli/gamed_classification_eval.py).

## How frames were extracted

`scripts/extract_frames.py` in the repo runs `ffmpeg` at 0.4 fps with
optional per-video `--trim-start` offsets to skip menus / intros.
Frames are JPEG q=5 (~85% quality) at 224×224 (squashed).

Per-video offsets used for the eval set:

| Class | Video ID | start_sec |
|---|---|---|
| cs2 | `FF1pN66xVfA` | 0 |
| dota2 | `2fpY4OeoY0c` | 210 |
| valorant | `Alrz2LCm8pg` | 0 |

## Eval results from the published checkpoint

See `eval/report.md` and `eval/report_full.md` for the full numbers.
Headline:

| Set | Frames | Accuracy |
|---|---|---|
| `eval/manifest.csv` (450) | 150 / class | 100 % |
| `eval/manifest_full.csv` (1118) | 390 / 295 / 433 | 99.91 % |

Single error: `valorant/Alrz2LCm8pg_000133.jpg` (t≈330 s) — a
round-end "WON" overlay misread as CS2 because Valorant's orange/red
Combat Report panel mimics CS2's post-round MVP card.

## License

Frames are 224×224 derivatives of publicly-uploaded "no commentary"
YouTube gameplay videos. CC-BY-4.0 on the derived dataset itself; the
underlying game footage remains the property of the original uploaders
and the respective game publishers (Valve, Riot Games).

## Citation

Repo: <https://github.com/bashiryounis/gamed>