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MODEL_RUN_COMMANDS.md

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+# Per-model run commands — `foldsrunner_simplified_after_ablation.py`
+
+Base config is already set in the file: `STRATEGIES=[2,3]`, 10 phases (`PHASE_EXECUTION_MODE="auto"`),
+100% data, `BATCH_SIZE=48`, `NUM_WORKERS=12`, `STRATEGY_2_MAX_EPOCHS=100`, `STRATEGY_3_MAX_EPOCHS=120`,
+`RUN_OPTUNA=False`, `STRATEGY2_CHECKPOINT_MODE="best"` (strategy 3 uses the strategy-2 base trained in
+the same run — no checkpoint wiring needed).
+
+**Validated at 128px:** DPT+ViT-tiny and UPerNet+Swin-tiny were dropped (weights locked to 224px) and
+replaced with **PVTv2 + UPerNet** (b1 and b2), which run natively at 128px. Re-run
+`python validate_models.py` to confirm all 6 pass on your box before launching.
+
+---
+
+## 1. SegFormer-B0  (transformer → SegFormer params, proj 192)
+
+```bash
+cd /workspace
+sed -i -E \
+  -e 's/^SMP_ENCODER_NAME = .*/SMP_ENCODER_NAME = "mit_b0"/' \
+  -e 's/^SMP_DECODER_TYPE = .*/SMP_DECODER_TYPE = "Segformer"/' \
+  -e 's/^SMP_ENCODER_PROJ_DIM = .*/SMP_ENCODER_PROJ_DIM = 192/' \
+  -e 's/^SMP_ENCODER_DEPTH = .*/SMP_ENCODER_DEPTH = 5/' \
+  -e 's/^MODEL_NAME = .*/MODEL_NAME = "Segformer_B0"/' \
+  -e 's#"2:100": "[^"]*"#"2:100": "params/segformer_b0/best_params_strat2.json"#' \
+  -e 's#"3:100": "[^"]*"#"3:100": "params/segformer_b0/best_params_strat3.json"#' \
+  foldsrunner_simplified_after_ablation.py
+python foldsrunner_simplified_after_ablation.py
+```
+
+## 2. U-Net + EfficientNet-B0  (CNN → U-Net params, proj 256)
+
+```bash
+cd /workspace
+sed -i -E \
+  -e 's/^SMP_ENCODER_NAME = .*/SMP_ENCODER_NAME = "efficientnet-b0"/' \
+  -e 's/^SMP_DECODER_TYPE = .*/SMP_DECODER_TYPE = "Unet"/' \
+  -e 's/^SMP_ENCODER_PROJ_DIM = .*/SMP_ENCODER_PROJ_DIM = 256/' \
+  -e 's/^SMP_ENCODER_DEPTH = .*/SMP_ENCODER_DEPTH = 5/' \
+  -e 's/^MODEL_NAME = .*/MODEL_NAME = "Unet_EffB0"/' \
+  -e 's#"2:100": "[^"]*"#"2:100": "params/unet_effb0/best_params_strat2.json"#' \
+  -e 's#"3:100": "[^"]*"#"3:100": "params/unet_effb0/best_params_strat3.json"#' \
+  foldsrunner_simplified_after_ablation.py
+python foldsrunner_simplified_after_ablation.py
+```
+
+## 3. DeepLabV3+ + ResNet34  (CNN → U-Net params, proj 256)
+
+```bash
+cd /workspace
+sed -i -E \
+  -e 's/^SMP_ENCODER_NAME = .*/SMP_ENCODER_NAME = "resnet34"/' \
+  -e 's/^SMP_DECODER_TYPE = .*/SMP_DECODER_TYPE = "DeepLabV3Plus"/' \
+  -e 's/^SMP_ENCODER_PROJ_DIM = .*/SMP_ENCODER_PROJ_DIM = 256/' \
+  -e 's/^SMP_ENCODER_DEPTH = .*/SMP_ENCODER_DEPTH = 5/' \
+  -e 's/^MODEL_NAME = .*/MODEL_NAME = "DeepLabV3Plus_ResNet34"/' \
+  -e 's#"2:100": "[^"]*"#"2:100": "params/deeplabv3plus_r34/best_params_strat2.json"#' \
+  -e 's#"3:100": "[^"]*"#"3:100": "params/deeplabv3plus_r34/best_params_strat3.json"#' \
+  foldsrunner_simplified_after_ablation.py
+python foldsrunner_simplified_after_ablation.py
+```
+
+## 4. LinkNet + MobileNetV3-Large  (CNN → U-Net params, proj 256)
+
+```bash
+cd /workspace
+sed -i -E \
+  -e 's/^SMP_ENCODER_NAME = .*/SMP_ENCODER_NAME = "timm-mobilenetv3_large_100"/' \
+  -e 's/^SMP_DECODER_TYPE = .*/SMP_DECODER_TYPE = "Linknet"/' \
+  -e 's/^SMP_ENCODER_PROJ_DIM = .*/SMP_ENCODER_PROJ_DIM = 256/' \
+  -e 's/^SMP_ENCODER_DEPTH = .*/SMP_ENCODER_DEPTH = 5/' \
+  -e 's/^MODEL_NAME = .*/MODEL_NAME = "Linknet_MobileNetV3"/' \
+  -e 's#"2:100": "[^"]*"#"2:100": "params/linknet_mbv3/best_params_strat2.json"#' \
+  -e 's#"3:100": "[^"]*"#"3:100": "params/linknet_mbv3/best_params_strat3.json"#' \
+  foldsrunner_simplified_after_ablation.py
+python foldsrunner_simplified_after_ablation.py
+```
+
+## 5. UPerNet + PVTv2-b1  (transformer → SegFormer params, proj 192)
+
+```bash
+cd /workspace
+sed -i -E \
+  -e 's/^SMP_ENCODER_NAME = .*/SMP_ENCODER_NAME = "tu-pvt_v2_b1"/' \
+  -e 's/^SMP_DECODER_TYPE = .*/SMP_DECODER_TYPE = "UPerNet"/' \
+  -e 's/^SMP_ENCODER_PROJ_DIM = .*/SMP_ENCODER_PROJ_DIM = 192/' \
+  -e 's/^SMP_ENCODER_DEPTH = .*/SMP_ENCODER_DEPTH = 5/' \
+  -e 's/^MODEL_NAME = .*/MODEL_NAME = "UPerNet_PVTv2_b1"/' \
+  -e 's#"2:100": "[^"]*"#"2:100": "params/upernet_pvtv2_b1/best_params_strat2.json"#' \
+  -e 's#"3:100": "[^"]*"#"3:100": "params/upernet_pvtv2_b1/best_params_strat3.json"#' \
+  foldsrunner_simplified_after_ablation.py
+python foldsrunner_simplified_after_ablation.py
+```
+
+## 6. UPerNet + PVTv2-b2  (transformer → SegFormer params, proj 192)
+
+```bash
+cd /workspace
+sed -i -E \
+  -e 's/^SMP_ENCODER_NAME = .*/SMP_ENCODER_NAME = "tu-pvt_v2_b2"/' \
+  -e 's/^SMP_DECODER_TYPE = .*/SMP_DECODER_TYPE = "UPerNet"/' \
+  -e 's/^SMP_ENCODER_PROJ_DIM = .*/SMP_ENCODER_PROJ_DIM = 192/' \
+  -e 's/^SMP_ENCODER_DEPTH = .*/SMP_ENCODER_DEPTH = 5/' \
+  -e 's/^MODEL_NAME = .*/MODEL_NAME = "UPerNet_PVTv2_b2"/' \
+  -e 's#"2:100": "[^"]*"#"2:100": "params/upernet_pvtv2_b2/best_params_strat2.json"#' \
+  -e 's#"3:100": "[^"]*"#"3:100": "params/upernet_pvtv2_b2/best_params_strat3.json"#' \
+  foldsrunner_simplified_after_ablation.py
+python foldsrunner_simplified_after_ablation.py
+```
+
+---
+
+## After the runs — plots
+
+```bash
+cd /workspace
+python plot_s2_vs_s3.py
+```
+
+One titled figure per model → `runs/_plots/<MODEL_NAME>__s2_vs_s3.png` (6 panels: BIoU band, BIoU
+contour, inference time, total training time, time per epoch, time to best checkpoint — S2 vs S3,
+mean ± std across the 10 phases, Δ annotated), plus `runs/_plots/summary_s2_vs_s3.csv`.

params/models_manifest.json

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+{
+  "segformer_b0": {
+    "family": "transformer",
+    "model_name": "Segformer_B0",
+    "arch": "Segformer",
+    "encoder": "mit_b0",
+    "proj_dim": 192,
+    "params_dir": "params/segformer_b0"
+  },
+  "unet_effb0": {
+    "family": "cnn",
+    "model_name": "Unet_EffB0",
+    "arch": "Unet",
+    "encoder": "efficientnet-b0",
+    "proj_dim": 256,
+    "params_dir": "params/unet_effb0"
+  },
+  "deeplabv3plus_r34": {
+    "family": "cnn",
+    "model_name": "DeepLabV3Plus_ResNet34",
+    "arch": "DeepLabV3Plus",
+    "encoder": "resnet34",
+    "proj_dim": 256,
+    "params_dir": "params/deeplabv3plus_r34"
+  },
+  "linknet_mbv3": {
+    "family": "cnn",
+    "model_name": "Linknet_MobileNetV3",
+    "arch": "Linknet",
+    "encoder": "timm-mobilenetv3_large_100",
+    "proj_dim": 256,
+    "params_dir": "params/linknet_mbv3"
+  },
+  "upernet_pvtv2_b1": {
+    "family": "transformer",
+    "model_name": "UPerNet_PVTv2_b1",
+    "arch": "UPerNet",
+    "encoder": "tu-pvt_v2_b1",
+    "proj_dim": 192,
+    "params_dir": "params/upernet_pvtv2_b1"
+  },
+  "upernet_pvtv2_b2": {
+    "family": "transformer",
+    "model_name": "UPerNet_PVTv2_b2",
+    "arch": "UPerNet",
+    "encoder": "tu-pvt_v2_b2",
+    "proj_dim": 192,
+    "params_dir": "params/upernet_pvtv2_b2"
+  }
+}

plot_s2_vs_s3.py

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+"""Scan runs/ and, for every model, plot Strategy 2 (supervised baseline) vs
+Strategy 3 (refinement), AVERAGED ACROSS ALL PHASES found for that model.
+
+Six measures per model (mean across phases, error bars = std across phases):
+
+    * BIoU (band, d-pixel)       evaluation.json -> metrics.biou.mean
+    * BIoU (contour, 1-pixel)    evaluation.json -> metrics.biou_contour.mean
+    * Total training time (min)  summary.json    -> elapsed_seconds
+    * Inference time (ms/image)  evaluation.json -> timing.mean_per_image_inference_ms
+    * Time per epoch (s)         summary.json    -> seconds_per_epoch
+    * Time to best ckpt (min)    best.pt.meta.json -> epoch  x  seconds_per_epoch
+                                 (falls back to argmax of selection_metric_value in history.json)
+
+Usage:
+    python plot_s2_vs_s3.py                 # scans ./runs
+    python plot_s2_vs_s3.py --runs-root X
+"""
+from __future__ import annotations
+
+import argparse
+import json
+import pathlib
+import re
+from collections import defaultdict
+
+import matplotlib
+matplotlib.use("Agg")
+import matplotlib.pyplot as plt
+import numpy as np
+
+STRATEGY_LABEL = {2: "S2 (baseline)", 3: "S3 (refinement)"}
+BAR_COLORS = {2: "#4C78A8", 3: "#F58518"}
+
+# key, title, ylabel, higher_is_better, value format
+PANELS = [
+    ("biou",             "Boundary IoU (band)",     "BIoU",         True,  "{:.4f}"),
+    ("biou_contour",     "Boundary IoU (contour)",  "BIoU contour", True,  "{:.4f}"),
+    ("infer_ms",         "Inference time",          "ms / image",   False, "{:.2f}"),
+    ("train_min",        "Total training time",     "minutes",      False, "{:.1f}"),
+    ("epoch_sec",        "Time per epoch",          "seconds",      False, "{:.1f}"),
+    ("time_to_best_min", "Time to best checkpoint", "minutes",      False, "{:.1f}"),
+]
+
+
+def load_json(path: pathlib.Path):
+    try:
+        return json.loads(path.read_text(encoding="utf-8"))
+    except Exception:
+        return None
+
+
+def best_epoch_for(final_dir: pathlib.Path) -> int | None:
+    """Epoch at which the best checkpoint was saved."""
+    meta = load_json(final_dir / "checkpoints" / "best.pt.meta.json")
+    if meta and meta.get("epoch") is not None:
+        return int(meta["epoch"])
+
+    # fallback: argmax of the selection metric recorded in history
+    hist = load_json(final_dir / "history.json")
+    if isinstance(hist, list) and hist:
+        best_ep, best_val = None, None
+        for row in hist:
+            val = row.get("selection_metric_value")
+            if val is None:
+                continue
+            if best_val is None or float(val) > float(best_val):
+                best_val, best_ep = float(val), int(row.get("epoch", 0))
+        return best_ep
+    return None
+
+
+def collect(runs_root: pathlib.Path) -> dict[str, dict[int, dict[str, list[float]]]]:
+    """model -> strategy -> metric -> [one value per phase]"""
+    data: dict[str, dict[int, dict[str, list[float]]]] = defaultdict(
+        lambda: defaultdict(lambda: defaultdict(list))
+    )
+    for eval_path in runs_root.glob("*/**/strategy_*/final/evaluation.json"):
+        final_dir = eval_path.parent
+        m = re.search(r"strategy_(\d+)", final_dir.parent.name)
+        if not m:
+            continue
+        strategy = int(m.group(1))
+        if strategy not in (2, 3):
+            continue
+        try:
+            model = eval_path.relative_to(runs_root).parts[0]
+        except ValueError:
+            continue
+
+        ev = load_json(eval_path)
+        if not ev:
+            continue
+        bucket = data[model][strategy]
+        metrics = ev.get("metrics", {}) or {}
+
+        for key in ("biou", "biou_contour"):
+            val = (metrics.get(key) or {}).get("mean")
+            if val is not None:
+                bucket[key].append(float(val))
+
+        infer = (ev.get("timing", {}) or {}).get("mean_per_image_inference_ms")
+        if infer is not None:
+            bucket["infer_ms"].append(float(infer))
+
+        summary = load_json(final_dir / "summary.json")
+        if summary:
+            if summary.get("elapsed_seconds") is not None:
+                bucket["train_min"].append(float(summary["elapsed_seconds"]) / 60.0)
+            spe = summary.get("seconds_per_epoch")
+            if spe is not None:
+                bucket["epoch_sec"].append(float(spe))
+                be = best_epoch_for(final_dir)
+                if be:
+                    bucket["time_to_best_min"].append(be * float(spe) / 60.0)
+
+    return data
+
+
+def plot_model(model: str, per_strategy: dict[int, dict[str, list[float]]],
+               out_dir: pathlib.Path) -> pathlib.Path | None:
+    strategies = [s for s in (2, 3) if per_strategy.get(s)]
+    if not strategies:
+        return None
+
+    n_phases = max((len(v.get("biou", [])) for v in per_strategy.values()), default=0)
+    fig, axes = plt.subplots(2, 3, figsize=(16, 8.5))
+    fig.suptitle(f"{model}   —   Strategy 2 vs Strategy 3\n"
+                 f"mean across {n_phases} phase(s), error bars = std across phases",
+                 fontsize=14, fontweight="bold")
+
+    for ax, (key, title, ylabel, higher_better, fmt) in zip(axes.ravel(), PANELS):
+        xs, means, stds, labels, colors = [], [], [], [], []
+        for i, s in enumerate(strategies):
+            vals = per_strategy[s].get(key, [])
+            if not vals:
+                continue
+            xs.append(i)
+            means.append(float(np.mean(vals)))
+            stds.append(float(np.std(vals)) if len(vals) > 1 else 0.0)
+            labels.append(STRATEGY_LABEL[s])
+            colors.append(BAR_COLORS[s])
+
+        if not means:
+            ax.set_title(f"{title}  (no data)")
+            ax.axis("off")
+            continue
+
+        bars = ax.bar(xs, means, yerr=stds, capsize=5, color=colors, width=0.55)
+        ax.set_xticks(xs)
+        ax.set_xticklabels(labels, fontsize=9)
+        ax.set_ylabel(ylabel)
+        ax.set_title(f"{title}   ({'higher' if higher_better else 'lower'} is better)", fontsize=11)
+        ax.grid(axis="y", alpha=0.3, linestyle="--")
+        ax.margins(y=0.18)
+
+        for b, mval in zip(bars, means):
+            ax.annotate(fmt.format(mval), (b.get_x() + b.get_width() / 2, b.get_height()),
+                        textcoords="offset points", xytext=(0, 4), ha="center", fontsize=9)
+
+        if len(means) == 2:
+            delta = means[1] - means[0]
+            pct = (delta / means[0] * 100.0) if means[0] else 0.0
+            good = (delta > 0) if higher_better else (delta < 0)
+            ax.text(0.5, 0.02, f"Δ(S3−S2) = {delta:+.4g}  ({pct:+.1f}%)",
+                    transform=ax.transAxes, ha="center", fontsize=9,
+                    color=("green" if good else "red"))
+
+    fig.tight_layout(rect=(0, 0, 1, 0.93))
+    out_dir.mkdir(parents=True, exist_ok=True)
+    out_path = out_dir / f"{model}__s2_vs_s3.png"
+    fig.savefig(out_path, dpi=150)
+    plt.close(fig)
+    return out_path
+
+
+def main() -> int:
+    ap = argparse.ArgumentParser()
+    ap.add_argument("--runs-root", default="runs")
+    args = ap.parse_args()
+
+    runs_root = pathlib.Path(args.runs_root).resolve()
+    if not runs_root.is_dir():
+        print(f"[plot] runs root not found: {runs_root}")
+        return 1
+
+    data = collect(runs_root)
+    if not data:
+        print(f"[plot] no evaluation.json found under {runs_root}")
+        return 1
+
+    out_dir = runs_root / "_plots"
+    csv_rows = ["model,strategy,metric,mean,std,n_phases"]
+    made = []
+
+    for model in sorted(data):
+        p = plot_model(model, data[model], out_dir)
+        if p:
+            made.append(p)
+            n = max((len(v.get("biou", [])) for v in data[model].values()), default=0)
+            print(f"[plot] {model:34s} n_phases={n:<3d} -> {p.name}")
+        for s in sorted(data[model]):
+            for key, *_ in PANELS:
+                vals = data[model][s].get(key, [])
+                if vals:
+                    csv_rows.append(
+                        f"{model},{s},{key},{np.mean(vals):.6f},"
+                        f"{(np.std(vals) if len(vals) > 1 else 0.0):.6f},{len(vals)}"
+                    )
+
+    (out_dir / "summary_s2_vs_s3.csv").write_text("\n".join(csv_rows) + "\n", encoding="utf-8")
+    print(f"\n[plot] {len(made)} figure(s) + summary_s2_vs_s3.csv written to {out_dir}")
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())

requirements_runpod.txt

@@ -0,0 +1,13 @@
+# Use this with a RunPod PyTorch image that already includes torch/torchvision + CUDA.
+# This avoids wasting setup time reinstalling large GPU wheels.
+
+numpy>=1.24.0
+scipy>=1.10.0
+pillow>=10.0.0
+tqdm>=4.66.0
+matplotlib>=3.8.0
+pandas>=2.2.0
+openpyxl>=3.1.0
+optuna>=3.6.0
+segmentation-models-pytorch>=0.5.0
+timm>=1.0.0

runs/UPerNet_PVTv2_b2/repeated_holdout/stratified_holdout_v1/exports/aggregated_metrics_by_percent_strategy.csv

@@ -0,0 +1,3 @@
+biou_contour_mean,biou_contour_std,biou_contour_within_run_std_mean,biou_mean,biou_std,biou_within_run_std_mean,completed_phase_count,completed_phases,dataset_percent,dice_mean,dice_std,dice_within_run_std_mean,hd95_mean,hd95_std,hd95_within_run_std_mean,iou_mean,iou_std,iou_within_run_std_mean,n_runs,ppv_mean,ppv_std,ppv_within_run_std_mean,sen_mean,sen_std,sen_within_run_std_mean,split_generation_mode,strategy
+0.11267025768756866,0.01116209279237299,0.0995235505203406,0.38434433937072754,0.022536722070910047,0.22202273458242416,6,"1,5,6,7,8,10",100,0.7864547371864319,0.02149925340002417,0.22156523168087006,12.715197722117106,2.2338706674300965,18.017226537068684,0.691642701625824,0.022486285897609478,0.2372153401374817,6,0.8157435357570648,0.009324625967154894,0.22683528065681458,0.8086498379707336,0.02765815555591703,0.22532529880603155,fixed_stratified_phases_8_1_1,2
+0.15048336051404476,0.01915635312112696,0.12087828759104013,0.4385542683303356,0.03076334973979934,0.24263359420001507,8,"1,2,4,5,6,7,8,9",100,0.7923366129398346,0.02120004327926994,0.2358646746724844,12.652392745018005,1.7277778612627843,17.322163224220276,0.7052389979362488,0.022461725431659713,0.25003683380782604,8,0.8194693028926849,0.016414306160995807,0.24514583125710487,0.8116263225674629,0.025946707469260203,0.23434872552752495,fixed_stratified_phases_8_1_1,3

runs/UPerNet_PVTv2_b2/repeated_holdout/stratified_holdout_v1/exports/raw_run_metrics.csv

@@ -0,0 +1,15 @@
+biou_contour_mean,biou_contour_std,biou_mean,biou_std,dataset_percent,dice_mean,dice_std,evaluation_path,hd95_mean,hd95_std,iou_mean,iou_std,phase_index,phase_test_fold_index,phase_val_fold_index,ppv_mean,ppv_std,run_dir,sen_mean,sen_std,split_generation_mode,split_manifest_path,split_repeat_index,strategy,subset_manifest_path,subset_repeat_index
+0.13350984454154968,0.12062076479196548,0.40272775292396545,0.2544459402561188,100,0.7665221691131592,0.2621297836303711,/workspace/runs/UPerNet_PVTv2_b2/repeated_holdout/stratified_holdout_v1/phase_001/pct_100/repeat_01/strategy_2/final/evaluation.json,14.112260818481445,19.74285316467285,0.6796557307243347,0.2779415249824524,1,1,2,0.8215616345405579,0.2620636820793152,/workspace/runs/UPerNet_PVTv2_b2/repeated_holdout/stratified_holdout_v1/phase_001/pct_100/repeat_01/strategy_2/final,0.778515636920929,0.26182082295417786,fixed_stratified_phases_8_1_1,/workspace/runs/UPerNet_PVTv2_b2/repeated_holdout/stratified_holdout_v1/manifests/splits/phase_001.json,1,2,/workspace/runs/UPerNet_PVTv2_b2/repeated_holdout/stratified_holdout_v1/manifests/subsets/phase_001_pct_100_repeat_01.json,1
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runs/UPerNet_PVTv2_b2/repeated_holdout/stratified_holdout_v1/exports/raw_run_metrics.json

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runs/UPerNet_PVTv2_b2/repeated_holdout/stratified_holdout_v1/phase_008/pct_100/repeat_01/norm_stats_BUSI_with_classes_stratified_80_10_10_100pct_phase008_repeat01.json

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runs/UPerNet_PVTv2_b2/repeated_holdout/stratified_holdout_v1/phase_008/pct_100/repeat_01/strategy_3/final/checkpoints/best.pt.meta.json

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runs/UPerNet_PVTv2_b2/repeated_holdout/stratified_holdout_v1/phase_008/pct_100/repeat_01/strategy_3/final/evaluation.json

@@ -0,0 +1,824 @@
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+}

runs/UPerNet_PVTv2_b2/repeated_holdout/stratified_holdout_v1/phase_008/pct_100/repeat_01/strategy_3/final/evaluation_summary.json

@@ -0,0 +1,4 @@
+{
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+  "mean_iou": 0.69790118932724
+}

runs/UPerNet_PVTv2_b2/repeated_holdout/stratified_holdout_v1/phase_008/pct_100/repeat_01/strategy_3/final/low_iou_samples.csv

@@ -0,0 +1,2 @@
+sample_id
+BUSI_0192_M

runs/UPerNet_PVTv2_b2/repeated_holdout/stratified_holdout_v1/phase_008/pct_100/repeat_01/strategy_3/final/run_config.json

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+  "strategy3_mc_disk_cache_read": true,
+  "strategy3_mc_disk_cache_write": true,
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+}

runs/UPerNet_PVTv2_b2/repeated_holdout/stratified_holdout_v1/phase_008/pct_100/repeat_01/strategy_3/final/summary.json

@@ -0,0 +1,34 @@
+{
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+  "gpu_name": "NVIDIA A100-SXM4-80GB",
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+  "strategy3_encoder_state": "frozen",
+  "strategy3_freeze_active": true,
+  "strategy3_freeze_requested": true,
+  "strategy3_segmentation_head_state": "frozen",
+  "time_to_best_seconds": 776.7906698454171
+}

runs/UPerNet_PVTv2_b2/repeated_holdout/stratified_holdout_v1/phase_008/pct_100/repeat_01/strategy_3/final/timing.csv

@@ -0,0 +1,2 @@
+scope,strategy,tmax,device,num_batches,num_samples,total_inference_ms,avg_batch_inference_ms,std_batch_inference_ms,avg_sample_inference_ms,std_sample_inference_ms,mean_per_image_inference_ms,std_per_image_inference_ms,mean_per_image_inference_seconds,std_per_image_inference_seconds
+test_set_evaluation,3,8,cuda,2,64,609.033437,304.516719,140.384431,9.516147,0.428463,9.516147,0.428463,0.009516147,0.000428463

runs/UPerNet_PVTv2_b2/repeated_holdout/stratified_holdout_v1/phase_009/pct_100/repeat_01/norm_stats_BUSI_with_classes_stratified_80_10_10_100pct_phase009_repeat01.json

@@ -0,0 +1,527 @@
+{
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runs/UPerNet_PVTv2_b2/repeated_holdout/stratified_holdout_v1/phase_009/pct_100/repeat_01/strategy_2/final/checkpoints/best.pt.meta.json

@@ -0,0 +1,39 @@
+{
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+  "checkpoint_path": "/workspace/runs/UPerNet_PVTv2_b2/repeated_holdout/stratified_holdout_v1/phase_009/pct_100/repeat_01/strategy_2/final/checkpoints/best.pt",
+  "dataset_percent": 1.0,
+  "epoch": 20,
+  "run_type": "final",
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+    "optimizer_state_dict",
+    "patience_counter",
+    "run_config",
+    "run_type",
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+  "state_presence": {
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+    "best_metric_name": true,
+    "best_metric_value": true,
+    "elapsed_seconds": true,
+    "epoch_metrics": true,
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+    "resume_source": false,
+    "run_config": true,
+    "scaler_state_dict": false,
+    "scheduler_state_dict": true
+  },
+  "strategy": 2
+}

runs/UPerNet_PVTv2_b2/repeated_holdout/stratified_holdout_v1/phase_009/pct_100/repeat_01/strategy_2/final/run_config.json

@@ -0,0 +1,70 @@
+{
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runs/UPerNet_PVTv2_b2/repeated_holdout/stratified_holdout_v1/phase_009/pct_100/repeat_01/strategy_3/final/checkpoints/best.pt.meta.json

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runs/UPerNet_PVTv2_b2/repeated_holdout/stratified_holdout_v1/phase_009/pct_100/repeat_01/strategy_3/final/checkpoints/latest.pt.meta.json

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runs/UPerNet_PVTv2_b2/repeated_holdout/stratified_holdout_v1/phase_009/pct_100/repeat_01/strategy_3/final/evaluation.json

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runs/UPerNet_PVTv2_b2/repeated_holdout/stratified_holdout_v1/phase_009/pct_100/repeat_01/strategy_3/final/evaluation_summary.json

@@ -0,0 +1,4 @@
+{
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+  "mean_iou": 0.6731811761856079
+}

runs/UPerNet_PVTv2_b2/repeated_holdout/stratified_holdout_v1/phase_009/pct_100/repeat_01/strategy_3/final/low_iou_samples.csv

@@ -0,0 +1,3 @@
+sample_id
+BUSI_0424_B
+BUSI_0116_B

runs/UPerNet_PVTv2_b2/repeated_holdout/stratified_holdout_v1/phase_009/pct_100/repeat_01/strategy_3/final/run_config.json

@@ -0,0 +1,112 @@
+{
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+  "backbone_family": "smp",
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+  "strategy3_exploration_eps_epochs": 20,
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+  "strategy3_mc_disk_cache_enabled": true,
+  "strategy3_mc_disk_cache_read": true,
+  "strategy3_mc_disk_cache_write": true,
+  "strategy3_mc_dropout_enabled": true,
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+  "strategy3_mc_dropout_samples": 5,
+  "strategy3_r1_progress_weight": 1.0,
+  "strategy3_rl_grad_clip_norm": 0.25,
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+  "subset_repeat_index": 1,
+  "subset_sampling_source": "incremental_chain",
+  "subset_seed": 121180609,
+  "threshold": 0.5922320465492188,
+  "tmax": 8,
+  "train_resume_mode": "off",
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+  "train_subset_key": "100",
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+}

runs/UPerNet_PVTv2_b2/repeated_holdout/stratified_holdout_v1/phase_009/pct_100/repeat_01/strategy_3/final/summary.json

@@ -0,0 +1,34 @@
+{
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+  "gpu_name": "NVIDIA A100-SXM4-80GB",
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+  "seconds_per_epoch_measured_mean": 11.304952571811597,
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+  "strategy3_decoder_state": "frozen",
+  "strategy3_encoder_state": "frozen",
+  "strategy3_freeze_active": true,
+  "strategy3_freeze_requested": true,
+  "strategy3_segmentation_head_state": "frozen",
+  "time_to_best_seconds": 610.9146690480411
+}

runs/UPerNet_PVTv2_b2/repeated_holdout/stratified_holdout_v1/phase_009/pct_100/repeat_01/strategy_3/final/timing.csv

@@ -0,0 +1,2 @@
+scope,strategy,tmax,device,num_batches,num_samples,total_inference_ms,avg_batch_inference_ms,std_batch_inference_ms,avg_sample_inference_ms,std_sample_inference_ms,mean_per_image_inference_ms,std_per_image_inference_ms,mean_per_image_inference_seconds,std_per_image_inference_seconds
+test_set_evaluation,3,8,cuda,2,64,608.788148,304.394074,140.442558,9.512315,0.424153,9.512315,0.424153,0.009512315,0.000424153

runs/UPerNet_PVTv2_b2/repeated_holdout/stratified_holdout_v1/phase_010/pct_100/repeat_01/norm_stats_BUSI_with_classes_stratified_80_10_10_100pct_phase010_repeat01.json

@@ -0,0 +1,526 @@
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runs/UPerNet_PVTv2_b2/repeated_holdout/stratified_holdout_v1/phase_010/pct_100/repeat_01/strategy_2/final/checkpoints/best.pt.meta.json

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runs/UPerNet_PVTv2_b2/repeated_holdout/stratified_holdout_v1/phase_010/pct_100/repeat_01/strategy_2/final/checkpoints/latest.pt.meta.json

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runs/UPerNet_PVTv2_b2/repeated_holdout/stratified_holdout_v1/phase_010/pct_100/repeat_01/strategy_2/final/evaluation.json

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+}

runs/UPerNet_PVTv2_b2/repeated_holdout/stratified_holdout_v1/phase_010/pct_100/repeat_01/strategy_2/final/evaluation_summary.json

@@ -0,0 +1,4 @@
+{
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+  "mean_iou": 0.7179535627365112
+}

runs/UPerNet_PVTv2_b2/repeated_holdout/stratified_holdout_v1/phase_010/pct_100/repeat_01/strategy_2/final/low_iou_samples.csv

@@ -0,0 +1,2 @@
+sample_id
+BUSI_0302_B

runs/UPerNet_PVTv2_b2/repeated_holdout/stratified_holdout_v1/phase_010/pct_100/repeat_01/strategy_2/final/run_config.json

@@ -0,0 +1,70 @@
+{
+  "backbone_family": "smp",
+  "base_split_manifest_path": "/workspace/runs/UPerNet_PVTv2_b2/repeated_holdout/stratified_holdout_v1/manifests/splits/phase_010.json",
+  "best_checkpoint_metric_name": "val_iou",
+  "best_checkpoint_metrics": {
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+    "3": "val_refine_score"
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+  "data_root": "/workspace/BUSI_with_classes",
+  "dataset_name": "BUSI_with_classes",
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+  "entropy_lr": 0.0003,
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+  "phase_test_fold_index": 10,
+  "phase_val_fold_index": 1,
+  "project_dir": "/workspace",
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+  "smp_decoder_type": "UPerNet",
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+  "smp_encoder_name": "tu-pvt_v2_b2",
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+  "split_type": "80_10_10",
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+  "subset_repeat_index": 1,
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+  "subset_seed": 1653428179,
+  "tmax": 5,
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+  "train_subset_key": "100",
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+}

runs/UPerNet_PVTv2_b2/repeated_holdout/stratified_holdout_v1/phase_010/pct_100/repeat_01/strategy_2/final/summary.json

@@ -0,0 +1,28 @@
+{
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+  "best_model_metric": 0.6820726724181333,
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+}

runs/UPerNet_PVTv2_b2/repeated_holdout/stratified_holdout_v1/phase_010/pct_100/repeat_01/strategy_2/final/timing.csv

@@ -0,0 +1,2 @@
+scope,strategy,tmax,device,num_batches,num_samples,total_inference_ms,avg_batch_inference_ms,std_batch_inference_ms,avg_sample_inference_ms,std_sample_inference_ms,mean_per_image_inference_ms,std_per_image_inference_ms,mean_per_image_inference_seconds,std_per_image_inference_seconds
+test_set_evaluation,2,5,cuda,2,64,119.301686,59.650843,44.638273,1.864089,0.534513,1.864089,0.534513,0.001864089,0.000534513

runs/UPerNet_PVTv2_b2/repeated_holdout/stratified_holdout_v1/phase_010/pct_100/repeat_01/strategy_3/final/checkpoints/best.pt.meta.json

@@ -0,0 +1,39 @@
+{
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+  "checkpoint_path": "/workspace/runs/UPerNet_PVTv2_b2/repeated_holdout/stratified_holdout_v1/phase_010/pct_100/repeat_01/strategy_3/final/checkpoints/best.pt",
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+    "best_metric_value",
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+    "optimizer_state_dict",
+    "patience_counter",
+    "run_config",
+    "run_type",
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+    "run_config": true,
+    "scaler_state_dict": false,
+    "scheduler_state_dict": true
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+  "strategy": 3
+}

runs/UPerNet_PVTv2_b2/repeated_holdout/stratified_holdout_v1/phase_010/pct_100/repeat_01/strategy_3/final/run_config.json

@@ -0,0 +1,112 @@
+{
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+  "phase_test_fold_index": 10,
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+  "strategy3_r1_progress_weight": 1.0,
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+  "threshold": 0.5922320465492188,
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+  "weight_decay": 2.7012557725439094e-06,
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+}

runs/UPerNet_PVTv2_b2/repeated_holdout/stratified_holdout_v1/phase_timing_summary.json

@@ -0,0 +1,235 @@
+{
+  "definition": "training elapsed_seconds from summary.json, falling back to the ledger checkpoint elapsed_seconds",
+  "phase_count": 9,
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+}

runs/_plots/summary_s2_vs_s3.csv

@@ -0,0 +1,13 @@
+model,strategy,metric,mean,std,n_phases
+UPerNet_PVTv2_b2,2,biou,0.384344,0.022537,6
+UPerNet_PVTv2_b2,2,biou_contour,0.112670,0.011162,6
+UPerNet_PVTv2_b2,2,infer_ms,1.066058,0.380270,6
+UPerNet_PVTv2_b2,2,train_min,2.132565,1.261769,6
+UPerNet_PVTv2_b2,2,epoch_sec,1.381017,0.727824,6
+UPerNet_PVTv2_b2,2,time_to_best_min,1.656541,1.307134,6
+UPerNet_PVTv2_b2,3,biou,0.438554,0.030763,8
+UPerNet_PVTv2_b2,3,biou_contour,0.150483,0.019156,8
+UPerNet_PVTv2_b2,3,infer_ms,9.584029,0.286293,8
+UPerNet_PVTv2_b2,3,train_min,21.534848,2.234972,8
+UPerNet_PVTv2_b2,3,epoch_sec,12.220154,2.118897,8
+UPerNet_PVTv2_b2,3,time_to_best_min,15.924827,2.351597,8

runs/_stats/ALL_MODELS_summary.csv

@@ -0,0 +1,15 @@
+Model,Metric,Level,n_pairs,S3_minus_S2,Wilcoxon_p_raw,p_Holm_global,Sig_global,Better_arm,Cohens_dz,boot_CI_low,boot_CI_high
+UPerNet_PVTv2_b2,biou_contour,per_image,324,0.029002373573579782,9.806315403966584e-15,3.7263998535073017e-13,Yes,S3 (refinement),0.3858993424349746,0.021059697492529294,0.03734722150563875
+UPerNet_PVTv2_b2,biou_contour,phase_level,5,0.028981601867805094,0.0625,1.0,No,n.s.,1.9460941710152966,0.017803722033805196,0.041113209055383355
+UPerNet_PVTv2_b2,biou,per_image,324,0.045190756039878446,2.975134068750509e-28,1.2495563088752138e-26,Yes,S3 (refinement),0.6506698384029238,0.03775307226390859,0.052803533511573464
+UPerNet_PVTv2_b2,biou,phase_level,5,0.045181233122264186,0.0625,1.0,No,n.s.,4.217318733168282,0.036379081460651684,0.052357987480219095
+UPerNet_PVTv2_b2,dice,per_image,324,0.014845270150340595,3.884405506189508e-15,1.514918147413908e-13,Yes,S3 (refinement),0.344554932627431,0.01036604853621482,0.019818316920430065
+UPerNet_PVTv2_b2,dice,phase_level,5,0.014838919485950952,0.0625,1.0,No,n.s.,3.421754885431272,0.010952776874905035,0.01781145563511788
+UPerNet_PVTv2_b2,iou,per_image,324,0.02109088036639489,3.1522920588587057e-16,1.2609168235434823e-14,Yes,S3 (refinement),0.4006818550908331,0.015605541345471383,0.027048547370950467
+UPerNet_PVTv2_b2,iou,phase_level,5,0.0210843617057781,0.0625,1.0,No,n.s.,3.928913431768461,0.016494917325397984,0.024785413754785268
+UPerNet_PVTv2_b2,ppv,per_image,324,0.009473546536372808,2.7118831779629925e-11,9.491591122870473e-10,Yes,S3 (refinement),0.12036951036671074,0.00029822184811289246,0.017297756571879024
+UPerNet_PVTv2_b2,ppv,phase_level,5,0.00944887285677185,0.0625,1.0,No,n.s.,1.0720147978255332,0.003004568362387783,0.01665772672812411
+UPerNet_PVTv2_b2,sen,per_image,324,0.016055054810744724,3.1260781381060746e-07,1.0316057855750046e-05,Yes,S3 (refinement),0.2469039097090848,0.009528804407140788,0.023532531557287035
+UPerNet_PVTv2_b2,sen,phase_level,5,0.01607541641791581,0.0625,1.0,No,n.s.,1.9169221135015138,0.00928269335231735,0.022082928837453795
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