dataset card: add phase-2 fine-tune results and benchmark contents

README.md

@@ -32,7 +32,13 @@ surface, fully redistributable.
 - `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 eval.
+  — 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)
 
@@ -57,6 +63,21 @@ Findings:
    (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).
+
 ## Reproduce
 
 ```bash