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README.md

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+---
+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>