Add Tab 7: resolution study (segformer_b0 + U-Net at 192/256/512)

.gitignore

@@ -16,6 +16,17 @@ pv_panel_models/*/checkpoints/
 pv_panel_models/predictions/
 pv_panel_models/source_images
 
+# Tab 7 (resolution study) needs the 3 segformer_b0 checkpoints live on the
+# Space so the inference grid produces real masks. 3 × 15 MB = 45 MB.
+# U-Net at 192/256 (~119 MB each) stays ignored, those cells degrade
+# gracefully to a "missing checkpoint" warning. Order matters: un-ignore the
+# directory first, re-ignore everything inside, then whitelist the trio.
+!experiments/resolution_study/checkpoints/
+experiments/resolution_study/checkpoints/*
+!experiments/resolution_study/checkpoints/segformer_b0_res192_best.pth
+!experiments/resolution_study/checkpoints/segformer_b0_res256_best.pth
+!experiments/resolution_study/checkpoints/segformer_b0_res512_best.pth
+
 # ── Hybrid model artifacts (large pretrained) ──
 hybrid_20_epochs_4ep_stop/*.pth
 hybrid_20_epochs_4ep_stop/prediction_*.png

app.py

@@ -37,6 +37,20 @@ BASELINE_CKPT = BASELINE_DIR / "checkpoints"
 GRID_LOGS = GRID_DIR / "logs"
 GRID_CSV = GRID_DIR / "results" / "grid_results.csv"
 
+RES_DIR = ROOT / "experiments" / "resolution_study"
+RES_LOGS = RES_DIR / "logs"
+RES_CKPT = RES_DIR / "checkpoints"
+RES_CSV = RES_DIR / "results" / "resolution_results.csv"
+
+# (model, image_size) pairs covered by the resolution study.
+RES_CONFIGS = [
+    ("segformer_b0", 192),
+    ("segformer_b0", 256),
+    ("segformer_b0", 512),
+    ("unet", 192),
+    ("unet", 256),
+]
+
 # Make the baseline experiment importable so we can load its model registry.
 sys.path.insert(0, str(BASELINE_DIR))
 from models import MODEL_REGISTRY, PRETTY_NAME  # noqa: E402
@@ -169,6 +183,42 @@ def load_baseline_ckpt(model_name: str, share: int, kind: str, device: str):
     return model, output_is_prob
 
 
+@st.cache_resource(show_spinner=False)
+def load_res_ckpt(model_name: str, image_size: int, device: str):
+    """Load a resolution-study checkpoint. Same architectures as the baseline,
+    weights are size-agnostic at inference time (both segformer_b0 and U-Net
+    are fully convolutional / patch-based)."""
+    p = RES_CKPT / f"{model_name}_res{image_size}_best.pth"
+    if not p.is_file():
+        return None, False
+    builder = MODEL_REGISTRY[model_name]
+    model, _, output_is_prob = builder()
+    state = torch.load(p, map_location=device, weights_only=False)
+    if isinstance(state, dict) and "model_state_dict" in state:
+        model.load_state_dict(state["model_state_dict"])
+        output_is_prob = state.get("output_is_prob", output_is_prob)
+    else:
+        model.load_state_dict(state)
+    model.to(device).eval()
+    return model, output_is_prob
+
+
+@st.cache_data(show_spinner=False)
+def load_res_log(model_name: str, image_size: int):
+    p = RES_LOGS / f"{model_name}_res{image_size}.json"
+    if not p.is_file():
+        return None
+    with open(p) as f:
+        return json.load(f)
+
+
+@st.cache_data(show_spinner=False)
+def load_res_csv():
+    if not RES_CSV.is_file():
+        return pd.DataFrame()
+    return pd.read_csv(RES_CSV)
+
+
 def preprocess(image: Image.Image, image_size: int = 128):
     tf = transforms.Compose([
         transforms.Resize((image_size, image_size)),
@@ -218,13 +268,14 @@ if st.button("🔄  Reload from disk"):
 baseline_logs = load_all_baseline_logs()
 grid_df = load_grid_results()
 
-t1, t2, t3, t4, t5, t6 = st.tabs([
+t1, t2, t3, t4, t5, t6, t7 = st.tabs([
     "1 · Baseline curves",
     "2 · Baseline scaling",
     "3 · Baseline inference",
     "4 · Grid heatmap",
     "5 · Top fine-tune configs",
     "6 · Per-config curves",
+    "7 · Resolution study",
 ])
 
 
@@ -599,3 +650,193 @@ with t6:
                 legend=dict(orientation="h", y=-0.2),
             )
             st.plotly_chart(fig, use_container_width=True)
+
+
+# ─── Tab 7: Resolution study ──────────────────────────────────────────────
+with t7:
+    st.subheader("Effect of training resolution on segformer_b0 and U-Net")
+    st.caption(
+        "Same architectures, same loss recipe (0.5·BCE + 0.5·Dice), same 100% "
+        "data share. Only the input image size is varied. The 128 px baseline "
+        "carried into each config as `baseline_dice_at_128` is shown as Δ."
+    )
+
+    res_df = load_res_csv()
+
+    # ── Summary table ──────────────────────────────────────────────────
+    if res_df.empty:
+        st.info("No resolution-study results CSV found at "
+                f"`{RES_CSV.relative_to(ROOT)}`.")
+    else:
+        show = res_df.copy()
+        show["best_val_dice"] = show["best_val_dice"].round(4)
+        show["best_val_miou"] = show["best_val_miou"].round(4)
+        show["best_val_iou"] = show["best_val_iou"].round(4)
+        show["delta_vs_128"] = show["delta_vs_128"].apply(
+            lambda v: f"{v:+.4f}" if pd.notna(v) else "—"
+        )
+        show["wall_clock"] = show["wall_clock_seconds"].apply(
+            lambda s: f"{int(s)//60} min {int(s)%60:02d} s" if pd.notna(s) else "—"
+        )
+        st.dataframe(
+            show[["cfg_id", "model", "image_size", "best_epoch",
+                  "best_val_dice", "best_val_miou", "best_val_iou",
+                  "delta_vs_128", "wall_clock"]],
+            hide_index=True, use_container_width=True,
+        )
+
+    # ── Charts ──────────────────────────────────────────────────────────
+    col_left, col_right = st.columns(2)
+
+    with col_left:
+        metric_choices = [
+            ("val_dice", "Val Dice"),
+            ("val_iou", "Val Foreground IoU"),
+            ("val_miou", "Val mIoU"),
+            ("val_loss", "Val Loss"),
+            ("train_dice", "Train Dice"),
+            ("train_loss", "Train Loss"),
+        ]
+        m_key, m_label = st.selectbox(
+            "Curve metric", metric_choices,
+            format_func=lambda x: x[1], key="t7_metric",
+        )
+        archs = st.multiselect(
+            "Architectures", ["segformer_b0", "unet"],
+            default=["segformer_b0", "unet"], key="t7_archs",
+        )
+        fig_curves = go.Figure()
+        for model_name, image_size in RES_CONFIGS:
+            if model_name not in archs:
+                continue
+            log = load_res_log(model_name, image_size)
+            if not log:
+                continue
+            xs = [e["epoch"] for e in log["epochs"]]
+            ys = [e.get(m_key) for e in log["epochs"]]
+            fig_curves.add_trace(go.Scatter(
+                x=xs, y=ys, mode="lines+markers",
+                name=f"{model_name} · {image_size}px",
+                line=dict(dash=("solid" if model_name == "segformer_b0" else "dot")),
+            ))
+        fig_curves.update_layout(
+            xaxis_title="Epoch", yaxis_title=m_label, height=420,
+            margin=dict(l=10, r=10, t=10, b=10),
+            legend=dict(orientation="h", y=-0.22),
+        )
+        st.plotly_chart(fig_curves, use_container_width=True)
+
+    with col_right:
+        st.markdown("**Training cost vs resolution**")
+        rows = []
+        for model_name, image_size in RES_CONFIGS:
+            log = load_res_log(model_name, image_size)
+            if not log:
+                continue
+            sec = [e.get("epoch_seconds") for e in log["epochs"]
+                   if e.get("epoch_seconds") is not None]
+            if not sec:
+                continue
+            rows.append({
+                "cfg": f"{model_name} · {image_size}px",
+                "model": model_name,
+                "image_size": image_size,
+                "sec_per_epoch": sum(sec) / len(sec),
+            })
+        if rows:
+            cost_df = pd.DataFrame(rows)
+            fig_cost = px.bar(
+                cost_df, x="cfg", y="sec_per_epoch", color="model",
+                labels={"cfg": "Config", "sec_per_epoch": "Seconds per epoch"},
+                color_discrete_map={"segformer_b0": "#0e7490", "unet": "#ef4444"},
+                height=420,
+            )
+            fig_cost.update_layout(
+                margin=dict(l=10, r=10, t=10, b=10),
+                legend=dict(orientation="h", y=-0.22),
+                showlegend=True,
+            )
+            st.plotly_chart(fig_cost, use_container_width=True)
+        else:
+            st.info("No `epoch_seconds` field in the logs.")
+
+    st.divider()
+
+    # ── Inference grid: upload an image, run through every res config ──
+    st.markdown("##### Run an image through every resolution checkpoint")
+    st.caption(
+        "Each cell uses the checkpoint at its own training resolution. The "
+        "input is resized to that size before inference. Cells with a missing "
+        "`.pth` on disk show a warning (deployed Spaces strip heavy weights)."
+    )
+
+    a, b, c = st.columns([2, 2, 2])
+    with a:
+        threshold_r = st.slider("Threshold", 0.0, 1.0, 0.5, 0.05, key="t7_thr")
+    with b:
+        view_r = st.radio("View", ["mask", "overlay", "heatmap"],
+                          horizontal=True, key="t7_view")
+    with c:
+        cell_w_r = st.select_slider(
+            "Cell size (px)", options=[140, 180, 220, 260], value=220, key="t7_cell"
+        )
+
+    uploaded_r = st.file_uploader(
+        "Drop an image (jpg/png)", type=["jpg", "jpeg", "png"], key="t7_upload"
+    )
+
+    if uploaded_r is not None:
+        device = "cuda" if torch.cuda.is_available() else "cpu"
+        try:
+            raw_bytes = uploaded_r.getvalue()
+            img_r = Image.open(io.BytesIO(raw_bytes)).convert("RGB")
+        except Exception as e:
+            st.error(f"Could not decode uploaded image: {e}")
+            st.stop()
+
+        st.caption(
+            f"📁 `{uploaded_r.name}` — {img_r.size[0]}×{img_r.size[1]} px, "
+            f"{len(raw_bytes)/1024:.1f} KB"
+        )
+
+        st.image([img_r], width=cell_w_r * 2, caption=["original"])
+
+        def render_cell_r(probs, mask, rgb):
+            if view_r == "mask":
+                return (mask * 255).astype(np.uint8)
+            if view_r == "overlay":
+                return overlay(rgb, mask)
+            return heatmap_rgb(probs)
+
+        # One row per architecture, columns per training resolution.
+        archs_in_grid = [
+            ("segformer_b0", [192, 256, 512]),
+            ("unet", [192, 256]),
+        ]
+        for model_name, sizes in archs_in_grid:
+            st.markdown(f"**{PRETTY_NAME.get(model_name, model_name)}**")
+            cols = st.columns(len(sizes))
+            for col, sz in zip(cols, sizes):
+                with col:
+                    st.markdown(f"<small>trained @ {sz}×{sz}</small>",
+                                unsafe_allow_html=True)
+                    try:
+                        with st.spinner(f"loading {model_name} res{sz}…"):
+                            m, output_is_prob = load_res_ckpt(model_name, sz, device)
+                        if m is None:
+                            st.warning(f"missing `{model_name}_res{sz}_best.pth`")
+                            continue
+                        x_r = preprocess(img_r, image_size=sz)
+                        rgb_small_r = (x_r.squeeze().permute(1, 2, 0).numpy() * 255).astype(np.uint8)
+                        probs, mask = run_inference(m, x_r, device, output_is_prob, threshold_r)
+                        cell_img = render_cell_r(probs, mask, rgb_small_r)
+                        st.image(cell_img, width=cell_w_r)
+                        st.caption(
+                            f"cov={float(mask.mean())*100:.1f}%   "
+                            f"p[{probs.min():.2f},{probs.max():.2f}]"
+                        )
+                    except Exception as e:
+                        st.error(f"{model_name} res{sz} — {type(e).__name__}")
+                        st.exception(e)
+    else:
+        st.info("Upload an image to compare predictions across training resolutions.")

experiments/resolution_study/dataset.py

@@ -0,0 +1,54 @@
+"""
+SolarPanelDataset for the resolution study. Identical to the clean baseline's
+dataset apart from taking image_size as an argument (which the trainer varies).
+"""
+from pathlib import Path
+import torch
+from torch.utils.data import Dataset
+from PIL import Image
+import torchvision.transforms as transforms
+
+
+class SolarPanelDataset(Dataset):
+    def __init__(self, image_dir, mask_dir, image_size=128, augment=False):
+        self.image_dir = Path(image_dir)
+        self.mask_dir = Path(mask_dir)
+        self.image_size = image_size
+        self.augment = augment
+
+        self.image_files = sorted(p.name for p in self.image_dir.iterdir() if p.suffix == ".jpg")
+
+        self.image_transform = transforms.Compose([
+            transforms.Resize((image_size, image_size)),
+            transforms.ToTensor(),
+        ])
+        self.mask_transform = transforms.Compose([
+            transforms.Resize((image_size, image_size)),
+            transforms.ToTensor(),
+        ])
+        self.augment_transform = transforms.Compose([
+            transforms.RandomHorizontalFlip(p=0.5),
+            transforms.RandomVerticalFlip(p=0.5),
+            transforms.RandomRotation(15),
+        ])
+
+    def __len__(self):
+        return len(self.image_files)
+
+    def __getitem__(self, idx):
+        img_name = self.image_files[idx]
+        image = Image.open(self.image_dir / img_name).convert("RGB")
+        mask = Image.open(self.mask_dir / img_name.replace(".jpg", "_mask.png")).convert("L")
+
+        image = self.image_transform(image)
+        mask = self.mask_transform(mask)
+
+        if self.augment:
+            seed = torch.randint(0, 2**32, (1,)).item()
+            torch.manual_seed(seed)
+            image = self.augment_transform(image)
+            torch.manual_seed(seed)
+            mask = self.augment_transform(mask)
+
+        mask = (mask > 0.5).float()
+        return image, mask

experiments/resolution_study/logs/segformer_b0_res192.json

@@ -0,0 +1,768 @@
+{
+  "cfg_id": "segformer_b0_res192",
+  "model": "segformer_b0",
+  "image_size": 192,
+  "batch_size": 16,
+  "throttle": null,
+  "n_train": 5268,
+  "n_val": 1330,
+  "n_params": 3714401,
+  "baseline_dice_at_128": 0.928,
+  "epochs": [
+    {
+      "epoch": 1,
+      "lr": 0.0001,
+      "train_loss": 0.36348072924848757,
+      "val_loss": 0.17965388927786125,
+      "train_iou": 0.6049228848432731,
+      "train_miou": 0.7843301165995575,
+      "train_dice": 0.7538342066850722,
+      "train_pixel_acc": 0.965644869252839,
+      "val_iou": 0.7735207930145374,
+      "val_miou": 0.8780135101282893,
+      "val_dice": 0.872299660721482,
+      "val_pixel_acc": 0.983492524442615,
+      "epoch_seconds": 20.636714220046997
+    },
+    {
+      "epoch": 2,
+      "lr": 0.0001,
+      "train_loss": 0.1393380452399001,
+      "val_loss": 0.09731947985433397,
+      "train_iou": 0.7941240641679578,
+      "train_miou": 0.8898638568848936,
+      "train_dice": 0.8852498888210838,
+      "train_pixel_acc": 0.9863610344477001,
+      "val_iou": 0.829964213202899,
+      "val_miou": 0.9089634267745623,
+      "val_dice": 0.9070824524488951,
+      "val_pixel_acc": 0.9886306545987344,
+      "epoch_seconds": 19.131412506103516
+    },
+    {
+      "epoch": 3,
+      "lr": 0.0001,
+      "train_loss": 0.09614957363316506,
+      "val_loss": 0.07870335717286382,
+      "train_iou": 0.8173215785508956,
+      "train_miou": 0.902421757754986,
+      "train_dice": 0.8994793086676661,
+      "train_pixel_acc": 0.9881817286581583,
+      "val_iou": 0.8390255757721852,
+      "val_miou": 0.9139000983018752,
+      "val_dice": 0.912467544579839,
+      "val_pixel_acc": 0.9893950982599709,
+      "epoch_seconds": 19.381251573562622
+    },
+    {
+      "epoch": 4,
+      "lr": 0.0001,
+      "train_loss": 0.08036535381141938,
+      "val_loss": 0.06980270808119149,
+      "train_iou": 0.8308762104033839,
+      "train_miou": 0.9097081279529512,
+      "train_dice": 0.9076268572197165,
+      "train_pixel_acc": 0.9891508606569799,
+      "val_iou": 0.8513263966381401,
+      "val_miou": 0.9204617656480956,
+      "val_dice": 0.9196934675420612,
+      "val_pixel_acc": 0.9901819775676151,
+      "epoch_seconds": 19.334835052490234
+    },
+    {
+      "epoch": 5,
+      "lr": 0.0001,
+      "train_loss": 0.07181035382955363,
+      "val_loss": 0.06764538102738914,
+      "train_iou": 0.8420670893894415,
+      "train_miou": 0.9157078493848329,
+      "train_dice": 0.9142632146677645,
+      "train_pixel_acc": 0.989921006614886,
+      "val_iou": 0.8502533772532989,
+      "val_miou": 0.9200331647394764,
+      "val_dice": 0.9190669642397844,
+      "val_pixel_acc": 0.9903699678884692,
+      "epoch_seconds": 19.149539709091187
+    },
+    {
+      "epoch": 6,
+      "lr": 0.0001,
+      "train_loss": 0.06977333224406748,
+      "val_loss": 0.06358121889865115,
+      "train_iou": 0.8422380071501447,
+      "train_miou": 0.9158151960402674,
+      "train_dice": 0.9143639463318284,
+      "train_pixel_acc": 0.989960903720313,
+      "val_iou": 0.8569351745778485,
+      "val_miou": 0.9235020941009311,
+      "val_dice": 0.9229564782978084,
+      "val_pixel_acc": 0.9906265908912888,
+      "epoch_seconds": 19.103957176208496
+    },
+    {
+      "epoch": 7,
+      "lr": 0.0001,
+      "train_loss": 0.06500308307057077,
+      "val_loss": 0.06787777474770944,
+      "train_iou": 0.8508285324692412,
+      "train_miou": 0.9204110897550342,
+      "train_dice": 0.9194028701666139,
+      "train_pixel_acc": 0.9905339071019068,
+      "val_iou": 0.8465105601326729,
+      "val_miou": 0.9179047366712454,
+      "val_dice": 0.9168759479738374,
+      "val_pixel_acc": 0.989895270402567,
+      "epoch_seconds": 19.069132804870605
+    },
+    {
+      "epoch": 8,
+      "lr": 0.0001,
+      "train_loss": 0.06391221561440916,
+      "val_loss": 0.06250155340170577,
+      "train_iou": 0.8516347736181403,
+      "train_miou": 0.9208399099877657,
+      "train_dice": 0.9198733851320202,
+      "train_pixel_acc": 0.9905831502639095,
+      "val_iou": 0.8557021100069498,
+      "val_miou": 0.9228932731313642,
+      "val_dice": 0.9222408115963668,
+      "val_pixel_acc": 0.9906350960408815,
+      "epoch_seconds": 19.111664533615112
+    },
+    {
+      "epoch": 9,
+      "lr": 0.0001,
+      "train_loss": 0.06382298135396205,
+      "val_loss": 0.0582104504684962,
+      "train_iou": 0.851599781682861,
+      "train_miou": 0.9208231574050862,
+      "train_dice": 0.9198529726643937,
+      "train_pixel_acc": 0.9905843397620192,
+      "val_iou": 0.865572246881621,
+      "val_miou": 0.9281632305161429,
+      "val_dice": 0.9279428854373878,
+      "val_pixel_acc": 0.9912737165178552,
+      "epoch_seconds": 19.092104196548462
+    },
+    {
+      "epoch": 10,
+      "lr": 0.0001,
+      "train_loss": 0.06087259976475528,
+      "val_loss": 0.06101861529584442,
+      "train_iou": 0.8574453094554428,
+      "train_miou": 0.9239524328001467,
+      "train_dice": 0.9232522810664339,
+      "train_pixel_acc": 0.9909773169816577,
+      "val_iou": 0.8593176409193786,
+      "val_miou": 0.9248564802163981,
+      "val_dice": 0.924336565208375,
+      "val_pixel_acc": 0.990927575285869,
+      "epoch_seconds": 19.06348752975464
+    },
+    {
+      "epoch": 11,
+      "lr": 0.0001,
+      "train_loss": 0.057844025184485046,
+      "val_loss": 0.057155027763829344,
+      "train_iou": 0.8628749702411782,
+      "train_miou": 0.9268592746218168,
+      "train_dice": 0.9263906424481785,
+      "train_pixel_acc": 0.9913424105118426,
+      "val_iou": 0.8677516588243217,
+      "val_miou": 0.9292756097476914,
+      "val_dice": 0.9291938301589177,
+      "val_pixel_acc": 0.9913233604845427,
+      "epoch_seconds": 19.14251470565796
+    },
+    {
+      "epoch": 12,
+      "lr": 0.0001,
+      "train_loss": 0.0564112450927496,
+      "val_loss": 0.05905941572217714,
+      "train_iou": 0.8666150190496805,
+      "train_miou": 0.9288604114452764,
+      "train_dice": 0.928541783073069,
+      "train_pixel_acc": 0.9915916901806237,
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experiments/resolution_study/logs/segformer_b0_res256.json

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+      "train_pixel_acc": 0.9932626566383631,
+      "val_iou": 0.883111018571925,
+      "val_miou": 0.9375276275422038,
+      "val_dice": 0.9379277268969946,
+      "val_pixel_acc": 0.9924064005227902,
+      "epoch_seconds": 337.05343556404114
+    },
+    {
+      "epoch": 37,
+      "lr": 5e-05,
+      "train_loss": 0.04317868397655812,
+      "val_loss": 0.05178131034509057,
+      "train_iou": 0.8942095004082636,
+      "train_miou": 0.9436117221724916,
+      "train_dice": 0.9441505812483071,
+      "train_pixel_acc": 0.9934034731321107,
+      "val_iou": 0.8790203420240567,
+      "val_miou": 0.9353519052463208,
+      "val_dice": 0.9356155677135322,
+      "val_pixel_acc": 0.9921573724961806,
+      "epoch_seconds": 335.98432779312134
+    },
+    {
+      "epoch": 38,
+      "lr": 5e-05,
+      "train_loss": 0.04256369875123103,
+      "val_loss": 0.05222855029361589,
+      "train_iou": 0.8957032434148426,
+      "train_miou": 0.9444119910361566,
+      "train_dice": 0.9449825509623113,
+      "train_pixel_acc": 0.9935044887487687,
+      "val_iou": 0.8791776196571043,
+      "val_miou": 0.9354226677710467,
+      "val_dice": 0.9357046512905244,
+      "val_pixel_acc": 0.9921440296603313,
+      "epoch_seconds": 336.62293577194214
+    },
+    {
+      "epoch": 39,
+      "lr": 5e-05,
+      "train_loss": 0.04222381215429667,
+      "val_loss": 0.05104621867871001,
+      "train_iou": 0.8962870952115249,
+      "train_miou": 0.9447219522728916,
+      "train_dice": 0.9453073824895138,
+      "train_pixel_acc": 0.9935389108158187,
+      "val_iou": 0.8811081235109721,
+      "val_miou": 0.936495511510617,
+      "val_dice": 0.9367968938079307,
+      "val_pixel_acc": 0.9923434838316483,
+      "epoch_seconds": 336.9724774360657
+    },
+    {
+      "epoch": 40,
+      "lr": 5e-05,
+      "train_loss": 0.041671457655276314,
+      "val_loss": 0.049910214419166245,
+      "train_iou": 0.8975874895742764,
+      "train_miou": 0.9454191204847866,
+      "train_dice": 0.946030150921421,
+      "train_pixel_acc": 0.9936276944912825,
+      "val_iou": 0.8837564876282817,
+      "val_miou": 0.9378992814860798,
+      "val_dice": 0.9382916459026649,
+      "val_pixel_acc": 0.9924960717222733,
+      "epoch_seconds": 337.6996052265167
+    },
+    {
+      "epoch": 41,
+      "lr": 5e-05,
+      "train_loss": 0.041797246851704335,
+      "val_loss": 0.06259610532738623,
+      "train_iou": 0.897003521723989,
+      "train_miou": 0.9451036879013084,
+      "train_dice": 0.9457057000176742,
+      "train_pixel_acc": 0.9935836270621259,
+      "val_iou": 0.8568097556986045,
+      "val_miou": 0.9235594216657617,
+      "val_dice": 0.92288372900781,
+      "val_pixel_acc": 0.9908402407079699,
+      "epoch_seconds": 337.889524936676
+    },
+    {
+      "epoch": 42,
+      "lr": 5e-05,
+      "train_loss": 0.04300476115648494,
+      "val_loss": 0.05414950386399314,
+      "train_iou": 0.8942397675740698,
+      "train_miou": 0.9436265753179,
+      "train_dice": 0.9441674521692806,
+      "train_pixel_acc": 0.9934030907934326,
+      "val_iou": 0.8736877271053841,
+      "val_miou": 0.9325128280031932,
+      "val_dice": 0.9325862730126578,
+      "val_pixel_acc": 0.9918275761425035,
+      "epoch_seconds": 337.9087963104248
+    },
+    {
+      "epoch": 43,
+      "lr": 5e-05,
+      "train_loss": 0.04168323976636836,
+      "val_loss": 0.05129873160538929,
+      "train_iou": 0.8976295390326262,
+      "train_miou": 0.9454414446110331,
+      "train_dice": 0.9460535057756531,
+      "train_pixel_acc": 0.9936301738996799,
+      "val_iou": 0.8804478230578452,
+      "val_miou": 0.9360994722753839,
+      "val_dice": 0.9364235606666566,
+      "val_pixel_acc": 0.9922235359822885,
+      "epoch_seconds": 336.86182498931885
+    },
+    {
+      "epoch": 44,
+      "lr": 5e-05,
+      "train_loss": 0.04165409284107613,
+      "val_loss": 0.04999064467847347,
+      "train_iou": 0.8975923520835848,
+      "train_miou": 0.9454204770031447,
+      "train_dice": 0.9460328516796719,
+      "train_pixel_acc": 0.9936257972804177,
+      "val_iou": 0.8836756110662332,
+      "val_miou": 0.9378594970449272,
+      "val_dice": 0.9382460609192032,
+      "val_pixel_acc": 0.9924968977619819,
+      "epoch_seconds": 337.4358162879944
+    },
+    {
+      "epoch": 45,
+      "lr": 5e-05,
+      "train_loss": 0.04057567070492289,
+      "val_loss": 0.05272769174050717,
+      "train_iou": 0.9001230065946739,
+      "train_miou": 0.9467733011504682,
+      "train_dice": 0.9474365643388942,
+      "train_pixel_acc": 0.9937915613729116,
+      "val_iou": 0.8763727686532983,
+      "val_miou": 0.9339611671903665,
+      "val_dice": 0.9341137148161497,
+      "val_pixel_acc": 0.9920271679871052,
+      "epoch_seconds": 337.4406862258911
+    },
+    {
+      "epoch": 46,
+      "lr": 2.5e-05,
+      "train_loss": 0.041589335583601936,
+      "val_loss": 0.04972087829152033,
+      "train_iou": 0.8981636367038398,
+      "train_miou": 0.9457256425645066,
+      "train_dice": 0.9463500610131808,
+      "train_pixel_acc": 0.9936628146160704,
+      "val_iou": 0.8835186869205802,
+      "val_miou": 0.9377745642787344,
+      "val_dice": 0.9381576015739677,
+      "val_pixel_acc": 0.9924847366218268,
+      "epoch_seconds": 337.7739152908325
+    },
+    {
+      "epoch": 47,
+      "lr": 2.5e-05,
+      "train_loss": 0.03872529609185277,
+      "val_loss": 0.050474510106834625,
+      "train_iou": 0.9037959117072141,
+      "train_miou": 0.9487364260790566,
+      "train_dice": 0.9494672261342785,
+      "train_pixel_acc": 0.9940314846864319,
+      "val_iou": 0.8828951470860359,
+      "val_miou": 0.9374425474380261,
+      "val_dice": 0.9378059616887348,
+      "val_pixel_acc": 0.9924461307382213,
+      "epoch_seconds": 336.1945569515228
+    },
+    {
+      "epoch": 48,
+      "lr": 2.5e-05,
+      "train_loss": 0.03837956857274879,
+      "val_loss": 0.04856898959371306,
+      "train_iou": 0.9052133400783744,
+      "train_miou": 0.9494921591842762,
+      "train_dice": 0.9502487947529665,
+      "train_pixel_acc": 0.9941207202166972,
+      "val_iou": 0.8873949308142448,
+      "val_miou": 0.9398401980986142,
+      "val_dice": 0.9403383640872787,
+      "val_pixel_acc": 0.9927275922961689,
+      "epoch_seconds": 334.29114842414856
+    },
+    {
+      "epoch": 49,
+      "lr": 2.5e-05,
+      "train_loss": 0.03812120704149658,
+      "val_loss": 0.05060118793820342,
+      "train_iou": 0.9053616133892155,
+      "train_miou": 0.9495708608878928,
+      "train_dice": 0.9503304853284813,
+      "train_pixel_acc": 0.9941294242146343,
+      "val_iou": 0.8820821594817134,
+      "val_miou": 0.936980342639076,
+      "val_dice": 0.9373471344360658,
+      "val_pixel_acc": 0.992343667396028,
+      "epoch_seconds": 333.8846809864044
+    },
+    {
+      "epoch": 50,
+      "lr": 2.5e-05,
+      "train_loss": 0.03823907319456339,
+      "val_loss": 0.048359356354922056,
+      "train_iou": 0.9056173367683727,
+      "train_miou": 0.9497073574333565,
+      "train_dice": 0.950471345211581,
+      "train_pixel_acc": 0.9941457923651643,
+      "val_iou": 0.8867443620832248,
+      "val_miou": 0.9394890526974615,
+      "val_dice": 0.9399729819297165,
+      "val_pixel_acc": 0.9926789133172275,
+      "epoch_seconds": 333.76842617988586
+    }
+  ],
+  "start_time_iso": "2026-05-21T14:27:48",
+  "best_epoch": 48,
+  "best_val_dice": 0.9403383640872787,
+  "epochs_trained": 50,
+  "wall_clock_seconds": 16867.168982982635
+}

experiments/resolution_study/metrics.py

@@ -0,0 +1,36 @@
+"""Confusion-matrix-based metrics. Same as the other experiments."""
+import torch
+
+
+class SegMetrics:
+    def __init__(self, threshold=0.5):
+        self.threshold = threshold
+        self.reset()
+
+    def reset(self):
+        self.tp = 0.0
+        self.fp = 0.0
+        self.fn = 0.0
+        self.tn = 0.0
+
+    @torch.no_grad()
+    def update(self, logits_or_probs, target, output_is_prob=False):
+        pred = logits_or_probs if output_is_prob else torch.sigmoid(logits_or_probs)
+        pred = (pred > self.threshold).float()
+        target = target.float()
+        self.tp += (pred * target).sum().item()
+        self.fp += (pred * (1 - target)).sum().item()
+        self.fn += ((1 - pred) * target).sum().item()
+        self.tn += ((1 - pred) * (1 - target)).sum().item()
+
+    def compute(self):
+        eps = 1e-7
+        tp, fp, fn, tn = self.tp, self.fp, self.fn, self.tn
+        fg_iou = tp / (tp + fp + fn + eps)
+        bg_iou = tn / (tn + fp + fn + eps)
+        return {
+            "iou": float(fg_iou),
+            "miou": float(0.5 * (fg_iou + bg_iou)),
+            "dice": float((2 * tp) / (2 * tp + fp + fn + eps)),
+            "pixel_acc": float((tp + tn) / (tp + tn + fp + fn + eps)),
+        }

experiments/resolution_study/models.py

@@ -0,0 +1,89 @@
+"""
+Builders for the resolution study. Same 4 architectures, same loss recipe,
+loaded via importlib so this experiment's dataset.py is not shadowed.
+"""
+import importlib.util
+from pathlib import Path
+
+import torch
+import torch.nn as nn
+
+REPO_ROOT = Path(__file__).resolve().parents[2]
+PV_DIR = REPO_ROOT / "pv_panel_models"
+
+
+def _load(module_name: str, file_path: Path):
+    spec = importlib.util.spec_from_file_location(module_name, file_path)
+    module = importlib.util.module_from_spec(spec)
+    spec.loader.exec_module(module)
+    return module
+
+
+_segnet_mod = _load("_pv_segnet_model", PV_DIR / "cnn_model" / "cnn_segmenter.py")
+_unet_mod = _load("_pv_unet_model", PV_DIR / "unet_model" / "unet_model.py")
+_segformer_b0_mod = _load("_pv_segformer_b0_model", PV_DIR / "vit_model" / "segformer_model.py")
+_segformer_b5_mod = _load("_pv_segformer_b5_model",
+                          PV_DIR / "segformer_b5_model" / "segformer_model.py")
+
+
+class _SegNetDiceLoss(nn.Module):
+    def __init__(self, smooth=1e-6):
+        super().__init__()
+        self.smooth = smooth
+
+    def forward(self, pred, target):
+        pred = pred.view(-1)
+        target = target.view(-1)
+        intersection = (pred * target).sum()
+        dice = (2.0 * intersection + self.smooth) / (pred.sum() + target.sum() + self.smooth)
+        return 1 - dice
+
+
+class _SegNetCombinedLoss(nn.Module):
+    def __init__(self, bce_weight=0.5):
+        super().__init__()
+        self.bce = nn.BCELoss()
+        self.dice = _SegNetDiceLoss()
+        self.bce_weight = bce_weight
+
+    def forward(self, pred, target):
+        return self.bce_weight * self.bce(pred, target) + (1 - self.bce_weight) * self.dice(pred, target)
+
+
+def build_segnet():
+    return _segnet_mod.SegNet(in_channels=3, out_channels=1), _SegNetCombinedLoss(bce_weight=0.5), True
+
+
+def build_unet():
+    return _unet_mod.UNet(in_channels=3, out_channels=1), _unet_mod.CombinedLoss(bce_weight=0.5), False
+
+
+def build_segformer_b0():
+    return (
+        _segformer_b0_mod.SegformerModel(pretrained_name="nvidia/mit-b0", num_classes=1),
+        _segformer_b0_mod.CombinedLoss(bce_weight=0.5),
+        False,
+    )
+
+
+def build_segformer_b5():
+    return (
+        _segformer_b5_mod.SegformerModel(pretrained_name="nvidia/mit-b5", num_classes=1),
+        _segformer_b5_mod.CombinedLoss(bce_weight=0.5),
+        False,
+    )
+
+
+MODEL_REGISTRY = {
+    "segnet":       build_segnet,
+    "unet":         build_unet,
+    "segformer_b0": build_segformer_b0,
+    "segformer_b5": build_segformer_b5,
+}
+
+PRETTY_NAME = {
+    "segnet":       "SegNet (CNN)",
+    "unet":         "U-Net",
+    "segformer_b0": "SegFormer-B0",
+    "segformer_b5": "SegFormer-B5",
+}

experiments/resolution_study/results/resolution_results.csv

@@ -0,0 +1,6 @@
+cfg_id,model,image_size,batch_size,best_epoch,best_val_dice,best_val_miou,best_val_iou,best_val_pixel_acc,baseline_dice_at_128,delta_vs_128,wall_clock_seconds
+segformer_b0_res192,segformer_b0,192,16,47,0.9393846042240084,0.9389258268871529,0.8856976883092615,0.9926036404487762,0.928,0.011384604224008332,962.2275054454803
+segformer_b0_res256,segformer_b0,256,16,48,0.9447013970333826,0.9440237140925567,0.8951981878661379,0.9932608697647427,0.928,0.01670139703338258,1755.2901220321655
+unet_res192,unet,192,16,50,0.9382585872059005,0.9378575204673147,0.8836978344253904,0.9924737176600336,0.937,0.001258587205900441,9444.264706611633
+unet_res256,unet,256,16,48,0.9403383640872787,0.9398401980986142,0.8873949308142448,0.9927275922961689,0.937,0.0033383640872786113,16867.168982982635
+segformer_b0_res512,segformer_b0,512,16,44,0.9470830922501384,0.9463243945370107,0.8994851210757973,0.9935577822807137,0.928,0.019083092250138378,10060.271508693695

experiments/resolution_study/run_resolution.sh

@@ -0,0 +1,73 @@
+#!/usr/bin/env bash
+# Resolution study. Trains one (model, image_size) cell at a time.
+#
+# Usage:
+#   ./run_resolution.sh segformer_b0 192 256              # one model, two resolutions
+#   ./run_resolution.sh unet 192 256
+#   ./run_resolution.sh segformer_b5 192 256              # B5 at 256 auto-drops bs to 8
+#   THROTTLE=0.4 ./run_resolution.sh segformer_b0 192 256 # optional duty-cycle cap
+#
+# First arg is the model. Remaining args are the image sizes to sweep.
+# Batch size is 16 unless the (model, image_size) combo is in the override
+# list below (currently: segformer_b5 at 256 -> bs=8 for VRAM headroom).
+
+set -euo pipefail
+DIR="$(cd "$(dirname "$0")" && pwd)"
+cd "$DIR"
+
+OVERALL_T0=$(date +%s)
+fmt_hms() {
+    local s=$1
+    printf '%d:%02d:%02d' $((s/3600)) $(((s%3600)/60)) $((s%60))
+}
+
+if [ $# -lt 2 ]; then
+    echo "usage: $0 <model> <image_size> [image_size ...]"
+    echo "example: $0 segformer_b0 192 256"
+    exit 1
+fi
+
+MODEL="$1"
+shift
+RESOLUTIONS=("$@")
+
+mkdir -p logs results
+
+THROTTLE_ARG=()
+if [ -n "${THROTTLE:-}" ] && [ "$THROTTLE" != "0" ]; then
+    THROTTLE_ARG=(--throttle "$THROTTLE")
+    echo "[grid] GPU duty-cycle cap: ${THROTTLE}"
+fi
+
+pick_bs() {
+    local model="$1"
+    local size="$2"
+    # SegFormer-B5 at 256 needs smaller batch for VRAM.
+    if [ "$model" = "segformer_b5" ] && [ "$size" -ge 256 ]; then
+        echo 8
+    else
+        echo 16
+    fi
+}
+
+for SIZE in "${RESOLUTIONS[@]}"; do
+    BS=$(pick_bs "$MODEL" "$SIZE")
+    cfg_id="${MODEL}_res${SIZE}"
+    t0=$(date +%s)
+    echo ""
+    echo "════════════════════════════════════════════════════════════"
+    echo "  $cfg_id   batch_size=$BS"
+    echo "════════════════════════════════════════════════════════════"
+    "${PYTHON:-python}" train.py \
+        --model "$MODEL" \
+        --image-size "$SIZE" \
+        --batch-size "$BS" \
+        "${THROTTLE_ARG[@]}" \
+        2>&1 | tee "logs/${cfg_id}.stdout.log"
+    dt=$(( $(date +%s) - t0 ))
+    echo "  → $cfg_id finished in $(fmt_hms $dt)"
+done
+
+OVERALL_DT=$(( $(date +%s) - OVERALL_T0 ))
+echo ""
+echo "[grid] complete in $(fmt_hms $OVERALL_DT). Results in results/resolution_results.csv"

experiments/resolution_study/train.py

@@ -0,0 +1,294 @@
+"""
+Resolution-study trainer.
+
+Trains one (model, image_size) cell at 100% data on final_data_clean/.
+
+Usage:
+    python train.py --model segformer_b0 --image-size 192
+    python train.py --model segformer_b0 --image-size 256
+
+Each run produces:
+    checkpoints/{model}_res{image_size}_best.pth   (state at highest val Dice)
+    logs/{model}_res{image_size}.json              (per-epoch metrics + timing)
+    one row appended to results/resolution_results.csv
+
+Hyperparameters held identical to the clean baseline at 128:
+    Adam, lr=1e-4, ReduceLROnPlateau(mode='max', patience=5, factor=0.5),
+    50 epochs, CombinedLoss(0.5*BCE + 0.5*Dice), HFlip+VFlip+Rot15.
+Only image_size and (optionally) batch_size vary.
+"""
+import argparse
+import csv
+import json
+import time
+from pathlib import Path
+from threading import Lock
+
+import torch
+import torch.optim as optim
+from torch.utils.data import DataLoader
+from tqdm import tqdm
+
+from dataset import SolarPanelDataset
+from metrics import SegMetrics
+from models import MODEL_REGISTRY
+
+THIS_DIR = Path(__file__).resolve().parent
+REPO_ROOT = THIS_DIR.parents[1]
+CLEAN = REPO_ROOT / "final_data_clean"
+TRAIN_IMG = CLEAN / "train" / "images"
+TRAIN_MSK = CLEAN / "train" / "masks"
+VAL_IMG = CLEAN / "val" / "images"
+VAL_MSK = CLEAN / "val" / "masks"
+
+LOG_DIR = THIS_DIR / "logs"
+CKPT_DIR = THIS_DIR / "checkpoints"
+RESULTS_DIR = THIS_DIR / "results"
+RESULTS_CSV = RESULTS_DIR / "resolution_results.csv"
+
+# Reference val-Dice values at 128 (from clean_data_scaling_study at 100% data).
+BASELINE_128 = {
+    "segnet":       0.9291,
+    "unet":         0.9370,
+    "segformer_b0": 0.9280,
+    "segformer_b5": 0.9371,
+}
+
+CSV_FIELDS = [
+    "cfg_id", "model", "image_size", "batch_size",
+    "best_epoch", "best_val_dice", "best_val_miou", "best_val_iou", "best_val_pixel_acc",
+    "baseline_dice_at_128", "delta_vs_128",
+    "wall_clock_seconds",
+]
+
+_csv_lock = Lock()
+
+
+def _fmt(seconds: float) -> str:
+    seconds = int(round(seconds))
+    h, rem = divmod(seconds, 3600)
+    m, s = divmod(rem, 60)
+    return f"{h:d}:{m:02d}:{s:02d}" if h else f"{m:d}:{s:02d}"
+
+
+def append_csv_row(row: dict):
+    RESULTS_DIR.mkdir(parents=True, exist_ok=True)
+    with _csv_lock:
+        write_header = not RESULTS_CSV.is_file()
+        with open(RESULTS_CSV, "a", newline="") as f:
+            writer = csv.DictWriter(f, fieldnames=CSV_FIELDS)
+            if write_header:
+                writer.writeheader()
+            writer.writerow({k: row.get(k) for k in CSV_FIELDS})
+
+
+def run_epoch(model, loader, criterion, optimizer, device, train: bool,
+              output_is_prob: bool, throttle: float | None = None):
+    model.train(mode=train)
+    metrics = SegMetrics()
+    total_loss = 0.0
+    n_batches = 0
+
+    do_throttle = train and throttle is not None and 0 < throttle < 1.0
+    sleep_factor = (1.0 - throttle) / throttle if do_throttle else 0.0
+
+    desc = "Train" if train else "Val"
+    ctx = torch.enable_grad() if train else torch.no_grad()
+    with ctx:
+        for images, masks in tqdm(loader, desc=desc, leave=False):
+            batch_t0 = time.perf_counter() if do_throttle else None
+
+            images = images.to(device, non_blocking=True)
+            masks = masks.to(device, non_blocking=True)
+
+            if train:
+                optimizer.zero_grad()
+            outputs = model(images)
+            loss = criterion(outputs, masks)
+            if train:
+                loss.backward()
+                optimizer.step()
+
+            total_loss += loss.item()
+            n_batches += 1
+            metrics.update(outputs.detach(), masks, output_is_prob=output_is_prob)
+
+            if do_throttle:
+                if torch.cuda.is_available():
+                    torch.cuda.synchronize()
+                step_dt = time.perf_counter() - batch_t0
+                time.sleep(step_dt * sleep_factor)
+
+    return total_loss / max(n_batches, 1), metrics.compute()
+
+
+def parse_args():
+    p = argparse.ArgumentParser()
+    p.add_argument("--model", required=True, choices=list(MODEL_REGISTRY.keys()))
+    p.add_argument("--image-size", required=True, type=int,
+                   help="square input resolution (e.g. 192, 256)")
+    p.add_argument("--batch-size", type=int, default=16)
+    p.add_argument("--epochs", type=int, default=50)
+    p.add_argument("--lr", type=float, default=1e-4)
+    p.add_argument("--num-workers", type=int, default=4)
+    p.add_argument("--seed", type=int, default=42)
+    p.add_argument("--throttle", type=float, default=None,
+                   help="optional duty-cycle cap, e.g. 0.4 for 40%% average util")
+    return p.parse_args()
+
+
+def main():
+    args = parse_args()
+    torch.manual_seed(args.seed)
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    cfg_id = f"{args.model}_res{args.image_size}"
+    print(f"[run] {cfg_id}  image_size={args.image_size}  batch_size={args.batch_size}  device={device}")
+
+    LOG_DIR.mkdir(parents=True, exist_ok=True)
+    CKPT_DIR.mkdir(parents=True, exist_ok=True)
+
+    train_set = SolarPanelDataset(TRAIN_IMG, TRAIN_MSK, image_size=args.image_size, augment=True)
+    val_set = SolarPanelDataset(VAL_IMG, VAL_MSK, image_size=args.image_size, augment=False)
+    print(f"[data] train={len(train_set)}  val={len(val_set)}")
+
+    train_loader = DataLoader(
+        train_set, batch_size=args.batch_size, shuffle=True,
+        num_workers=args.num_workers, pin_memory=True,
+    )
+    val_loader = DataLoader(
+        val_set, batch_size=args.batch_size, shuffle=False,
+        num_workers=args.num_workers, pin_memory=True,
+    )
+
+    builder = MODEL_REGISTRY[args.model]
+    model, criterion, output_is_prob = builder()
+    model = model.to(device)
+    n_params = sum(p.numel() for p in model.parameters())
+    print(f"[model] {args.model}  params={n_params:,}  "
+          f"output={'prob' if output_is_prob else 'logits'}")
+
+    optimizer = optim.Adam(model.parameters(), lr=args.lr)
+    scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode="max", patience=5, factor=0.5)
+
+    baseline = BASELINE_128.get(args.model)
+    history = {
+        "cfg_id": cfg_id,
+        "model": args.model,
+        "image_size": args.image_size,
+        "batch_size": args.batch_size,
+        "throttle": args.throttle,
+        "n_train": len(train_set),
+        "n_val": len(val_set),
+        "n_params": n_params,
+        "baseline_dice_at_128": baseline,
+        "epochs": [],
+    }
+
+    best_dice = -1.0
+    best_epoch = -1
+    best_path = CKPT_DIR / f"{cfg_id}_best.pth"
+    log_path = LOG_DIR / f"{cfg_id}.json"
+
+    val_m = {"dice": 0.0, "iou": 0.0, "miou": 0.0, "pixel_acc": 0.0}
+
+    if args.throttle is not None and 0 < args.throttle < 1:
+        print(f"[throttle] training duty cycle capped at {args.throttle*100:.0f}%")
+
+    t0 = time.time()
+    history["start_time_iso"] = time.strftime("%Y-%m-%dT%H:%M:%S", time.localtime(t0))
+
+    for epoch in range(args.epochs):
+        epoch_t0 = time.time()
+        train_loss, train_m = run_epoch(
+            model, train_loader, criterion, optimizer, device,
+            train=True, output_is_prob=output_is_prob, throttle=args.throttle,
+        )
+        val_loss, val_m = run_epoch(
+            model, val_loader, criterion, optimizer, device,
+            train=False, output_is_prob=output_is_prob, throttle=None,
+        )
+        scheduler.step(val_m["dice"])
+
+        epoch_seconds = time.time() - epoch_t0
+        elapsed = time.time() - t0
+        eta = (elapsed / (epoch + 1)) * (args.epochs - epoch - 1)
+
+        improved = val_m["dice"] > best_dice
+        if improved:
+            best_dice = val_m["dice"]
+            best_epoch = epoch + 1
+            torch.save({
+                "epoch": epoch + 1,
+                "model_state_dict": model.state_dict(),
+                "val_metrics": val_m,
+                "cfg": {
+                    "cfg_id": cfg_id,
+                    "model": args.model,
+                    "image_size": args.image_size,
+                },
+                "output_is_prob": output_is_prob,
+            }, best_path)
+
+        history["epochs"].append({
+            "epoch": epoch + 1,
+            "lr": optimizer.param_groups[0]["lr"],
+            "train_loss": train_loss,
+            "val_loss": val_loss,
+            **{f"train_{k}": v for k, v in train_m.items()},
+            **{f"val_{k}": v for k, v in val_m.items()},
+            "epoch_seconds": epoch_seconds,
+        })
+
+        marker = "★" if improved else " "
+        print(
+            f"  ep {epoch+1:>2}/{args.epochs}  "
+            f"trL={train_loss:.4f}  vL={val_loss:.4f}  "
+            f"vDice={val_m['dice']:.4f} {marker}  vIoU={val_m['iou']:.4f}  "
+            f"({_fmt(elapsed)}/ETA {_fmt(eta)})"
+        )
+
+        with open(log_path, "w") as f:
+            json.dump(history, f, indent=2)
+
+    total = time.time() - t0
+    history["best_epoch"] = best_epoch
+    history["best_val_dice"] = best_dice
+    history["epochs_trained"] = args.epochs
+    history["wall_clock_seconds"] = total
+    with open(log_path, "w") as f:
+        json.dump(history, f, indent=2)
+
+    if best_path.is_file():
+        st = torch.load(best_path, map_location="cpu", weights_only=False)
+        bvm = st.get("val_metrics", {})
+        best_val_iou = bvm.get("iou")
+        best_val_miou = bvm.get("miou")
+        best_val_pa = bvm.get("pixel_acc")
+    else:
+        best_val_iou = best_val_miou = best_val_pa = None
+
+    delta = (best_dice - baseline) if baseline is not None else None
+    append_csv_row({
+        "cfg_id": cfg_id,
+        "model": args.model,
+        "image_size": args.image_size,
+        "batch_size": args.batch_size,
+        "best_epoch": best_epoch,
+        "best_val_dice": best_dice,
+        "best_val_miou": best_val_miou,
+        "best_val_iou": best_val_iou,
+        "best_val_pixel_acc": best_val_pa,
+        "baseline_dice_at_128": baseline,
+        "delta_vs_128": delta,
+        "wall_clock_seconds": total,
+    })
+
+    print(f"\n[done] {cfg_id}  best ep {best_epoch}  vDice={best_dice:.4f}")
+    if baseline is not None:
+        sign = "+" if delta >= 0 else ""
+        print(f"       baseline at 128 = {baseline:.4f}  delta = {sign}{delta:+.4f}")
+    print(f"       wall {_fmt(total)}")
+
+
+if __name__ == "__main__":
+    main()