diff --git a/models/LICENCE.md b/models/LICENCE.md
index 83235e8..803bf1a 100644
--- a/models/LICENCE.md
+++ b/models/LICENCE.md
@@ -1,9 +1,16 @@
 # Model weights — licensing
 
-`yolo26n-seg.onnx` is exported from Ultralytics YOLO26n-seg
-(`https://huggingface.co/Ultralytics/YOLO26`, `yolo26n-seg.pt`) by
-`tools/export-seg-model.sh`. `yolo26n-seg.classes.json` is that checkpoint's
-class vocabulary, written out by the same script.
+Two Ultralytics checkpoints ship here, both exported by
+`tools/export-seg-model.sh`, each with its class vocabulary written out by the
+same script:
+
+| File | Checkpoint | Trained on | Used by |
+|---|---|---|---|
+| `segment/yolo26n-seg.onnx` | `yolo26n-seg.pt` | COCO, 80 *thing* classes | local adjustments, subject selection |
+| `scene/yolo26s-sem-ade20k.onnx` | `yolo26s-sem-ade20k.pt` | ADE20K, 150 classes | the scene tab's per-category grades |
+
+Both come from `https://huggingface.co/Ultralytics/YOLO26`. The face weights in
+`face/` are a separate matter with a separate grant — see `face/README.md`.
 
 ## The grant
 
@@ -38,17 +45,27 @@ This was decided deliberately (D14), not arrived at by accident, and
 classes include the *stuff* categories that matter most in photography — sky,
 vegetation, water, wall, mountain.
 
-**No such model exists in usable form.** Checked 2026-08-21: Ultralytics ships
-YOLO26-seg trained on **COCO**, whose 80 classes are all *things* — person,
-dog, car, bird, potted plant — and the one HuggingFace repository claiming a
-YOLO/ADE20K combination (`laxmacl/yolov8-ade20k`) is empty. ADE20K semantic
-models do exist, but as SegFormer/OneFormer/MaskFormer transformers, not YOLO.
+**This was true when written and is not any more.** Checked 2026-08-21, no
+YOLO/ADE20K combination existed: Ultralytics shipped YOLO26-seg on **COCO**
+only, and the one HuggingFace repository claiming otherwise
+(`laxmacl/yolov8-ade20k`) was empty. Re-checked 2026-08-30: Ultralytics now
+ships a `semantic` task with ADE20K checkpoints
+(`https://docs.ultralytics.com/tasks/semantic`), and `yolo26s-sem-ade20k` is
+what `scene/` holds.
 
-So the shipped vocabulary selects **subjects**, not **stuff**. "Select the
-person" works; "select the sky" does not come from the model and must come from
-the watershed hierarchy instead. That is a narrower arm B than §4 assumed, and
-it raises rather than lowers the importance of arm C.
+So the two vocabularies divide the work rather than compete:
 
-The loader treats the vocabulary as model metadata rather than compiled-in
-knowledge, so adding a stuff-class model later is a file plus a descriptor, not
-a code change.
+- **`segment/`, COCO, 80 things.** Separates *instances* — clicking one of
+  three people selects that person. This is what local adjustments need, and a
+  semantic model cannot do it: it would return one "person" region covering all
+  three.
+- **`scene/`, ADE20K, 150 classes.** Labels every pixel, including the *stuff*
+  COCO has no word for — sky, vegetation, water, mountain, wall. This is what
+  the scene tab's per-category grades need, and it does not care that instances
+  are merged, because a per-category grade applies to the whole category.
+
+Neither replaces the other. Keeping both is the deliberate choice.
+
+The loader treats each vocabulary as model metadata rather than compiled-in
+knowledge, which is what made adding the second model a file plus a descriptor
+rather than a code change — as this document predicted it would be.
diff --git a/models/scene/yolo26s-sem-ade20k.classes.json b/models/scene/yolo26s-sem-ade20k.classes.json
new file mode 100644
index 0000000..e5357c4
--- /dev/null
+++ b/models/scene/yolo26s-sem-ade20k.classes.json
@@ -0,0 +1,152 @@
+[
+ "wall",
+ "building",
+ "sky",
+ "floor",
+ "tree",
+ "ceiling",
+ "road",
+ "bed",
+ "windowpane",
+ "grass",
+ "cabinet",
+ "sidewalk",
+ "person",
+ "earth",
+ "door",
+ "table",
+ "mountain",
+ "plant",
+ "curtain",
+ "chair",
+ "car",
+ "water",
+ "painting",
+ "sofa",
+ "shelf",
+ "house",
+ "sea",
+ "mirror",
+ "rug",
+ "field",
+ "armchair",
+ "seat",
+ "fence",
+ "desk",
+ "rock",
+ "wardrobe",
+ "lamp",
+ "bathtub",
+ "railing",
+ "cushion",
+ "base",
+ "box",
+ "column",
+ "signboard",
+ "chest of drawers",
+ "counter",
+ "sand",
+ "sink",
+ "skyscraper",
+ "fireplace",
+ "refrigerator",
+ "grandstand",
+ "path",
+ "stairs",
+ "runway",
+ "case",
+ "pool table",
+ "pillow",
+ "screen door",
+ "stairway",
+ "river",
+ "bridge",
+ "bookcase",
+ "blind",
+ "coffee table",
+ "toilet",
+ "flower",
+ "book",
+ "hill",
+ "bench",
+ "countertop",
+ "stove",
+ "palm",
+ "kitchen island",
+ "computer",
+ "swivel chair",
+ "boat",
+ "bar",
+ "arcade machine",
+ "hovel",
+ "bus",
+ "towel",
+ "light",
+ "truck",
+ "tower",
+ "chandelier",
+ "awning",
+ "streetlight",
+ "booth",
+ "television receiver",
+ "airplane",
+ "dirt track",
+ "apparel",
+ "pole",
+ "land",
+ "bannister",
+ "escalator",
+ "ottoman",
+ "bottle",
+ "buffet",
+ "poster",
+ "stage",
+ "van",
+ "ship",
+ "fountain",
+ "conveyor belt",
+ "canopy",
+ "washer",
+ "plaything",
+ "swimming pool",
+ "stool",
+ "barrel",
+ "basket",
+ "waterfall",
+ "tent",
+ "bag",
+ "minibike",
+ "cradle",
+ "oven",
+ "ball",
+ "food",
+ "step",
+ "tank",
+ "trade name",
+ "microwave",
+ "pot",
+ "animal",
+ "bicycle",
+ "lake",
+ "dishwasher",
+ "screen",
+ "blanket",
+ "sculpture",
+ "hood",
+ "sconce",
+ "vase",
+ "traffic light",
+ "tray",
+ "ashcan",
+ "fan",
+ "pier",
+ "crt screen",
+ "plate",
+ "monitor",
+ "bulletin board",
+ "shower",
+ "radiator",
+ "glass",
+ "clock",
+ "flag"
+]
\ No newline at end of file
diff --git a/models/scene/yolo26s-sem-ade20k.onnx b/models/scene/yolo26s-sem-ade20k.onnx
new file mode 100644
index 0000000..b5d4946
--- /dev/null
+++ b/models/scene/yolo26s-sem-ade20k.onnx
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:296cbbf983a7dbd53eb551f4bcb2ce28d6503bb5ddd30a60c4a13b44087e01d7
+size 24921749
diff --git a/tools/export-seg-model.sh b/tools/export-seg-model.sh
index 261fad5..de12bce 100755
--- a/tools/export-seg-model.sh
+++ b/tools/export-seg-model.sh
@@ -1,14 +1,25 @@
 #!/usr/bin/env bash
-# Re-export the segmentation model that ships in models/ at the repository root.
+# Re-export the segmentation models that ship in models/ at the repository root.
 #
 # The .onnx is committed (D14), so this is not part of any build — it exists so
 # the committed artefact is reproducible rather than a binary someone once
-# produced and nobody can regenerate. Run it when bumping the model.
+# produced and nobody can regenerate. Run it when bumping a model.
 #
-#   ./tools/export-seg-model.sh
+#   ./tools/export-seg-model.sh                        # instance -> models/segment/
+#   ./tools/export-seg-model.sh yolo26s-sem-ade20k     # semantic -> models/scene/
 #
 # Requires `uv`. Everything else is fetched into a throwaway venv.
 #
+# ## The two models, and why they are both here
+#
+# `yolo26n-seg` is COCO instance segmentation: it separates *things*, so
+# clicking one of three people selects that person. `yolo26s-sem-ade20k` is
+# ADE20K semantic segmentation: it labels every pixel with one of 150 classes
+# including the *stuff* — sky, vegetation, water — that COCO has no word for,
+# but it merges same-class pixels into one region and so cannot tell those
+# three people apart. Neither substitutes for the other; the scene tab wants
+# the second and local adjustments want the first.
+#
 # ## Why these export flags
 #
 # `dynamic=False` is not a default we failed to change: **tract cannot parse
@@ -20,14 +31,38 @@
 # `imgsz` square rather than a rectangle matched to 3:2: one graph has to
 # serve portrait, landscape, square crops and panoramas. A landscape-shaped
 # graph trades letterbox waste on 3:2 for worse waste on everything else.
+#
+# ## Why the semantic export is truncated
+#
+# Ultralytics ends the `-sem-` graph with `Resize -> ArgMax -> Cast`, handing
+# back a `[1, 640, 640]` u8 label map. Two reasons that tail is cut here:
+#
+#  1. **Cost.** The Resize materialises 150 x 640 x 640 x f32 — *246 MB* — and
+#     ArgMax then reduces across the channel axis, which in NCHW strides
+#     409,600 elements per comparison. Measured on one machine it was roughly
+#     four fifths of total runtime, for work the app throws away.
+#  2. **Softness.** ArgMax destroys the per-class scores. The scene tab needs
+#     them: softmax over the 150 channels, summed within each photographic
+#     category, gives per-category weights that sum to 1 at every pixel — a
+#     partition of unity. Feathering those cannot double-grade a boundary,
+#     where feathering hard labels outward from two adjacent categories does.
+#
+# The upsample is not information: the graph's true spatial resolution is the
+# logit grid (80x80 at imgsz=640), and the app can resample from that itself.
 set -euo pipefail
 
 HERE="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
 REPO="$(cd "${HERE}/.." && pwd)"
-OUT="${REPO}/models/segment"
 
 MODEL="${1:-yolo26n-seg}"
 IMGSZ="${2:-640}"
+
+# Semantic models are the scene tab's; instance models are the selection path's.
+case "${MODEL}" in
+    *-sem-*|*-sem) OUT="${REPO}/models/scene"; SEMANTIC=1 ;;
+    *)             OUT="${REPO}/models/segment"; SEMANTIC=0 ;;
+esac
+
 # Not `mktemp -d`: the default TMPDIR is `/tmp`, which on most current Linux
 # distributions is a tmpfs — RAM, sized at half of physical memory. The venv
 # below pulls torch, several gigabytes of it, and installing that into RAM
@@ -41,12 +76,13 @@ cd "${WORK}"
 uv venv --python 3.12 venv
 VIRTUAL_ENV="${WORK}/venv" uv pip install ultralytics onnx onnxslim
 
-VIRTUAL_ENV="${WORK}/venv" "${WORK}/venv/bin/python" - "${MODEL}" "${IMGSZ}" <<'PY'
+VIRTUAL_ENV="${WORK}/venv" "${WORK}/venv/bin/python" - "${MODEL}" "${IMGSZ}" "${SEMANTIC}" <<'PY'
 import sys, json
+import onnx
+from onnx import helper
 from ultralytics import YOLO
 
-name = sys.argv[1]
-imgsz = int(sys.argv[2])
+name, imgsz, semantic = sys.argv[1], int(sys.argv[2]), sys.argv[3] == "1"
 m = YOLO(f"{name}.pt")
 path = m.export(format="onnx", opset=17, simplify=True, imgsz=imgsz, dynamic=False)
 print("ONNX:", path)
@@ -57,6 +93,25 @@ print("ONNX:", path)
 with open("classes.json", "w") as f:
     json.dump([m.names[i] for i in range(len(m.names))], f, indent=1)
 print("classes:", len(m.names))
+
+if semantic:
+    # Drop `Resize -> ArgMax -> Cast` and expose the classifier's logits. See
+    # the header for why. Matched by op type rather than by node name so a
+    # re-export under a different naming scheme still works, and asserted
+    # rather than assumed so an upstream graph change fails loudly here
+    # instead of silently shipping a differently-shaped model.
+    g = onnx.load(path).graph
+    tail = [n.op_type for n in g.node[-3:]]
+    assert tail == ["Resize", "ArgMax", "Cast"], f"unexpected graph tail: {tail}"
+    logits = g.node[-3].input[0]
+    del g.node[-3:]
+    del g.output[:]
+    g.output.extend([helper.make_tensor_value_info(logits, onnx.TensorProto.FLOAT, None)])
+    model = onnx.shape_inference.infer_shapes(helper.make_model(g, opset_imports=[helper.make_opsetid("", 17)]))
+    onnx.checker.check_model(model)
+    onnx.save(model, path)
+    shape = [d.dim_value for d in model.graph.output[0].type.tensor_type.shape.dim]
+    print("truncated to logits:", logits, shape)
 PY
 
 mkdir -p "${OUT}"
@@ -66,4 +121,4 @@ cp "${WORK}/classes.json" "${OUT}/${MODEL}.classes.json"
 echo "==> wrote:"
 ls -la "${OUT}"
 echo
-echo "Remember: these weights are AGPL-3.0 (see ${OUT}/LICENCE.md)."
+echo "Remember: these weights are AGPL-3.0 (see ${REPO}/models/LICENCE.md)."