Blame for README.md

---
title: Synthetic poker-table screenshots for perception evals (60 labeled images)
license: cc0-1.0
tags:
- ml-intern
- poker
- computer-vision
- synthetic-data
- playing-cards
size_categories:
- n<1K
---

# Poker-Perception Eval · synthetic tables

Synthetic 1600×900 poker table screenshots rendered deterministically (seed 17),
each paired with exact ground-truth labels, built to benchmark how well small
on-device vision models can read a poker table without touching any real-money
client. All pixels are programmatically generated — no game footage, no ToS
surface, fully redistributable.

<div style="display:flex;flex-wrap:wrap;align-items:center;gap:8px;margin:0 0 2em"><a href="https://hf.co/chat?mode=ml-intern"><img src="https://hf.co/datasets/huggingface/badges/resolve/main/built-with-ml-intern-lg.svg" alt="Built with ML Intern" style="margin:0"></a><a href="https://hf.co/chat?mode=ml-intern&prompt=Fine-tune%20the%20YOLOv11%20card%20detector%20on%20the%20synthetic%20poker-table%20dataset" style="display:inline-flex;align-items:center;gap:5px;padding:4px 12px;border:1.5px solid #eee;border-radius:16px;background:#f9fbfc;color:#c2410c;font-weight:600;font-size:14px;text-decoration:none">Fine-tune YOLO on these tables<svg width="14" height="14" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="1.5" stroke-linecap="round"><path d="M13.5 6H5a2 2 0 0 0-2 2v11a2 2 0 0 0 2 2h11a2 2 0 0 0 2-2v-8.5m-10.5 6L21 3m0 0h-5m5 0v5"/></svg></a><a href="https://hf.co/chat?mode=ml-intern&prompt=Build%20the%20equity-based%20poker%20decision%20engine%20with%20tunable%20risk%20knobs" style="display:inline-flex;align-items:center;gap:5px;padding:4px 12px;border:1.5px solid #eee;border-radius:16px;background:#f9fbfc;color:#c2410c;font-weight:600;font-size:14px;text-decoration:none">Build the decision engine<svg width="14" height="14" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="1.5" stroke-linecap="round"><path d="M13.5 6H5a2 2 0 0 0-2 2v11a2 2 0 0 0 2 2h11a2 2 0 0 0 2-2v-8.5m-10.5 6L21 3m0 0h-5m5 0v5"/></svg></a></div>

## Contents

- `data/images/test_000.png … test_059.png` — 1600×900 renders: green felt,
  2–6 seats (random), face-up hole + hero cards, flop/turn/river board subset,
  pot text, per-seat stack/position plates, occasional bet circles, dealer button.
- `data/labels/test_NNN.json` — exact ground truth: cards (`"A♠"` style), pixel
  bboxes per card, per-seat stacks/positions/optional bets, pot, dealer seat.
  741 labeled card instances total (avg 12.3/image).
- `render_tables.py` — the generator (deterministic; regenerate with `--seed`).
- `eval_mirage.py`, `eval_yolo.py` — the two scorers used in the eval below.
- `eval/results_mirage_1x.json`, `eval/results_mirage_2x.json`, `results_yolo.json`
  — raw per-image outputs of the phase-1 eval. `eval/results_stock_test.json`,
  `eval/results_ft_test.json` — before/after eval of the phase-2 fine-tune on
  this same seed-17 test set.
- `benchmark/` — the phase-2 training benchmark: 800 train (seed 101) + 80 val
  (seed 202) varied-style images in YOLO format (`images/`, `labels/`, `data.yaml`)
  with full ground-truth JSONs under `gt/`. The seed-17 set is never in training.
- `train_yolo.py` — the fine-tune script the GPU job ran.

## Eval results (CPU, 2 vCPU)

| Model | Card read / cls acc | Localization | Pot text | s/img |
|---|---|---|---|---|
| **Mirage** 51M (Cactus, CPU) | 0.0 | best-IoU ≈ 0.04 | **0.97** | 6.4 |
| **Mirage @ 2× upscale** | 0.0 | 0.005 | **1.00** | 6.7 |
| **YOLO11n** cards ([shrimantasatpati](https://huggingface.co/shrimantasatpati/yolov11_playing_cards_detection), MIT, conf 0.1, imgsz 1280) | 0.119 (glyph region) | 0.193 (glyph region IoU≥0.4); 0.0 vs full-card box | n/a | 0.37 |

Findings:

1. **Mirage OCR is a table-state reader, not a card reader.** It recovers pot,
   stacks, names, positions reliably, but produces no card-glyph text at either
   resolution — and 2× upscale doesn't fix it (localization gets worse).
2. **The YOLO card detector detects corner *glyph patches*, not whole cards** on
   our renders; scoring against full-card boxes yields 0.0 even though ~28% of
   cards have a centered detection. Against the corner-glyph region its
   detection rate is 19.3% with 11.9% correct class — weak, because its real-photo
   training set is off-distribution for flat synthetic renders.
3. **Implication for the stack:** read *state* (pot/stacks) with Mirage-class
   OCR; read *card identity* with a detector fine-tuned on this synthetic data
   (or your captured-game frames later). The two failures are complementary and
   separable.

## Fine-tune results (phase 2)

YOLO11n fine-tuned 80 epochs (batch 16, imgsz 1280) on `benchmark/` →
[`ThreeSixNine/yolo11n-poker-table-cards`](https://huggingface.co/ThreeSixNine/yolo11n-poker-table-cards).
Scored on the untouched seed-17 test set (741 cards):

| Model | Detection (IoU≥0.4) | Class accuracy | s/img |
|---|---|---|---|
| Stock | 0.193 | 0.119 | 0.36 |
| **Fine-tuned** | **0.999** | **0.586** | 0.33 |

5× class accuracy, essentially perfect localization, no inference cost. Class
accuracy below 1.0 is 52-way rank/suit confusion on unseen domain detail;
per-image worst 6/17, best 12/12. Run cost ≈ $0.53 on a10g-small (31.6 min).

## Sim-to-real results (phase 3)

Real-photo eval at imgsz 224 (scale-matched to these sets):

| Real set | Stock det / cls | Fine-tuned det / cls |
|---|---|---|
| gpiosenka crops (520 imgs, CC0) | 0.000 / 0.000 | 0.571 / 0.329 |
| gunhcolab XML-box scenes (98 imgs) | 0.000 / 0.000 | **0.929** / 0.316 |

- Stock never emits full-card boxes on real photos at any scale (corner-pip
  patches only) — 0.0 across the board.
- The fine-tuned model **transfers detection well** (0.93 on real full scenes at
  matched scale) but **class accuracy drops 0.586 → ~0.32** — a ~26-point
  sim-to-real gap concentrated in the classify head, with failures skewed
  toward spades and queens/threes.
- Inference-scale lesson: at imgsz 1280 both models score 0.0 on these sets —
  an artifact of letterboxing 224×224 crops to the synthetic-table scale. imgsz
  is part of the model contract and must match the data scale.
- gunhcolab files are NOT redistributed (license © original authors); only
  aggregate metrics above are published. gpiosenka-derived numbers are CC0-safe.

## v2 mixed fine-tune: head-to-head (phase 4)

`benchmark_2/` — 800+80 scenes, ~40% of card slots pasted with real gpiosenka
TRAIN-split crops at table scale (CC0; valid/test untouched). Fine-tuned from
the phase-2 checkpoint → `best_mixed.pt` in
[`ThreeSixNine/yolo11n-poker-table-cards`](https://huggingface.co/ThreeSixNine/yolo11n-poker-table-cards):

| Eval | FT-v1 | FT-mixed |
|---|---|---|
| Synthetic seed-17 (det / cls) | 0.999 / 0.586 | 0.996 / 0.594 |
| gpiosenka crops (det / cls) | 0.571 / 0.329 | 0.650 / 0.469 |
| gunhcolab scenes (det / cls) | 0.929 / 0.316 | 0.990 / **0.735** |

Cut-paste sim2real doubles scene-level real class accuracy with no synthetic
regression. Raw outputs: `eval/{syn_ft1,syn_ftm,real_gp_*,real_gu_*}.json`.

## Reproduce

```bash
python render_tables.py --n 60 --seed 17
python eval_yolo.py --limit 60            # conf 0.1, imgsz 1280 defaults
python eval_mirage.py --limit 60          # cactus-compute==4.0.1
```