Model card: both graph layouts + browser WebGPU benchmark (Apple GPU, real EM)

README.md

@@ -13,6 +13,11 @@ Every file takes `input` float32 `[1,1,z,y,x]` and returns `output` float32 **ra
 (`[{"name":"MinMaxNormalizer","min_value":0,"max_value":255},{"name":"LambdaNormalizer","expression":"2*x-1"}]`).
 The input block size is fixed per file (`_in<N>` = N³ voxels); valid convolutions, so the output is N−88 per axis.
 
+Two graph layouts of the **same models** (same weights, same outputs up to float rounding):
+
+- `setup_XX/…` — **recommended**: every Conv3d rewritten as Conv2d over a depth-folded batch (~16× faster on WebGPU, see below).
+- `conv3d/setup_XX/…` — native Conv3d nodes, for runtimes with a fast 3D conv (e.g. onnxruntime CUDA/CPU, TensorRT).
+
 ## Models
 
 | folder | target | channels | voxel (nm) | architecture | params | checkpoint | val F1 |
@@ -45,6 +50,88 @@ Both are exact (same math, same weights; verified against the PyTorch model):
 Peak memory is dominated by activations, not weights: the largest single tensor is ~40×N³ (ResUNet) or ~32×(N−2)³ (Unet3dIso)
 values, e.g. 259 MB fp16 / 519 MB fp32 at N=148. Pick a smaller block if your GPU's WebGPU `maxBufferSize` is lower.
 
+## Browser WebGPU benchmark
+
+Measured in Chrome 154 on macOS, Apple GPU (WebGPU `metal-3`), onnxruntime-web 1.27.0-dev.20260506; best of 3 runs after one warm-up run.
+
+Real EM (jrc_mus-liver, public OpenOrganelle data) at each model's voxel size. `vs fp32 flip %` = voxels whose `logit > 0`
+decision differs from the same model's fp32 file on WebGPU.
+
+| model | block | variant | ms / block | output voxels / s | max abs vs PyTorch (sampled) | flip % vs fp32 |
+|---|---|---|---|---|---|---|
+| `setup_54` | 148³ | `fp32` | 1203 | 179,551 | 0.001 | — |
+| `setup_54` | 148³ | `fp16` | 905 | 238,674 | 0.082 | 0.0 |
+| `setup_54` | 148³ | `int8w` | 1216 | 177,632 | 0.106 | 0.0 |
+| `setup_54` | 148³ | `int8w_fp16` | 879 | 245,734 | 0.153 | 0.0 |
+| `setup_54` | 132³ | `fp32` | 796 | 107,015 | 0.0 | — |
+| `setup_54` | 132³ | `fp16` | 573 | 148,663 | 0.072 | 0.0 |
+| `setup_54` | 132³ | `int8w` | 1096 | 77,723 | 0.096 | 0.0 |
+| `setup_54` | 132³ | `int8w_fp16` | 558 | 152,659 | 0.09 | 0.0 |
+| `setup_54` | 116³ | `fp32` | 516 | 42,543 | 0.001 | — |
+| `setup_54` | 116³ | `fp16` | 323 | 67,963 | 0.048 | 0.0 |
+| `setup_54` | 116³ | `int8w` | 531 | 41,341 | 0.103 | 0.0 |
+| `setup_54` | 116³ | `int8w_fp16` | 328 | 66,927 | 0.101 | 0.0 |
+| `setup_53` | 172³ | `fp32` | 1137 | 521,288 | 0.001 | — |
+| `setup_53` | 172³ | `fp16` | 741 | 799,870 | 0.071 | 0.0 |
+| `setup_53` | 172³ | `int8w` | 1013 | 585,098 | 0.208 | 0.0 |
+| `setup_53` | 172³ | `int8w_fp16` | 737 | 804,212 | 0.166 | 0.0 |
+| `setup_53` | 140³ | `fp32` | 533 | 263,805 | 0.0 | — |
+| `setup_53` | 140³ | `fp16` | 336 | 418,476 | 0.076 | 0.0 |
+| `setup_53` | 140³ | `int8w` | 494 | 284,632 | 0.268 | 0.0 |
+| `setup_53` | 140³ | `int8w_fp16` | 328 | 428,683 | 0.225 | 0.0 |
+| `setup_53` | 124³ | `fp32` | 348 | 134,069 | 0.0 | — |
+| `setup_53` | 124³ | `fp16` | 157 | 297,172 | 0.073 | 0.0 |
+| `setup_53` | 124³ | `int8w` | 317 | 147,180 | 0.194 | 0.0 |
+| `setup_53` | 124³ | `int8w_fp16` | 160 | 291,600 | 0.201 | 0.0 |
+| `setup_56` | 148³ | `fp32` | 1208 | 178,808 | 0.001 | — |
+| `setup_56` | 148³ | `fp16` | 921 | 234,528 | 0.2 | 0.0 |
+| `setup_56` | 148³ | `int8w` | 1219 | 177,194 | 0.379 | 0.0 |
+| `setup_56` | 148³ | `int8w_fp16` | 880 | 245,455 | 0.34 | 0.0 |
+| `setup_56` | 132³ | `fp32` | 811 | 105,036 | 0.001 | — |
+| `setup_56` | 132³ | `fp16` | 574 | 148,404 | 0.16 | 0.0 |
+| `setup_56` | 132³ | `int8w` | 822 | 103,630 | 0.447 | 0.0 |
+| `setup_56` | 132³ | `int8w_fp16` | 559 | 152,386 | 0.266 | 0.0 |
+| `setup_56` | 116³ | `fp32` | 527 | 41,655 | 0.001 | — |
+| `setup_56` | 116³ | `fp16` | 324 | 67,753 | 0.119 | 0.0 |
+| `setup_56` | 116³ | `int8w` | 526 | 41,734 | 0.342 | 0.0 |
+| `setup_56` | 116³ | `int8w_fp16` | 326 | 67,337 | 0.332 | 0.0 |
+| `setup_55` | 172³ | `fp32` | 1080 | 548,800 | 0.001 | — |
+| `setup_55` | 172³ | `fp16` | 758 | 781,931 | 0.117 | 0.0 |
+| `setup_55` | 172³ | `int8w` | 989 | 599,296 | 0.558 | 0.0 |
+| `setup_55` | 172³ | `int8w_fp16` | 736 | 805,304 | 0.65 | 0.0 |
+| `setup_55` | 140³ | `fp32` | 506 | 277,881 | 0.001 | — |
+| `setup_55` | 140³ | `fp16` | 319 | 440,777 | 0.131 | 0.0 |
+| `setup_55` | 140³ | `int8w` | 481 | 292,324 | 0.531 | 0.0 |
+| `setup_55` | 140³ | `int8w_fp16` | 336 | 418,476 | 0.512 | 0.0 |
+| `setup_55` | 124³ | `fp32` | 331 | 140,955 | 0.001 | — |
+| `setup_55` | 124³ | `fp16` | 156 | 299,077 | 0.103 | 0.0 |
+| `setup_55` | 124³ | `int8w` | 315 | 148,114 | 0.314 | 0.0 |
+| `setup_55` | 124³ | `int8w_fp16` | 160 | 291,600 | 0.402 | 0.0 |
+| `setup_61` | 148³ | `fp32` | 1220 | 177,049 | 0.001 | — |
+| `setup_61` | 148³ | `fp16` | 900 | 240,000 | 0.124 | 0.0019 |
+| `setup_61` | 148³ | `int8w` | 1224 | 176,471 | 0.328 | 0.0032 |
+| `setup_61` | 148³ | `int8w_fp16` | 869 | 248,562 | 0.362 | 0.0037 |
+| `setup_61` | 132³ | `fp32` | 809 | 105,295 | 0.001 | — |
+| `setup_61` | 132³ | `fp16` | 574 | 148,404 | 0.164 | 0.0 |
+| `setup_61` | 132³ | `int8w` | 808 | 105,426 | 0.407 | 0.0 |
+| `setup_61` | 132³ | `int8w_fp16` | 555 | 153,485 | 0.482 | 0.0 |
+| `setup_61` | 116³ | `fp32` | 514 | 42,708 | 0.001 | — |
+| `setup_61` | 116³ | `fp16` | 322 | 68,174 | 0.145 | 0.0 |
+| `setup_61` | 116³ | `int8w` | 525 | 41,813 | 0.303 | 0.0 |
+| `setup_61` | 116³ | `int8w_fp16` | 327 | 67,131 | 0.434 | 0.0 |
+| `setup_67` | 148³ | `fp32` | 1233 | 175,182 | 0.0 | — |
+| `setup_67` | 148³ | `fp16` | 890 | 242,697 | 0.03 | 0.0148 |
+| `setup_67` | 148³ | `int8w` | 1221 | 176,904 | 0.042 | 0.0282 |
+| `setup_67` | 148³ | `int8w_fp16` | 870 | 248,276 | 0.048 | 0.0356 |
+| `setup_67` | 132³ | `fp32` | 809 | 105,295 | 0.0 | — |
+| `setup_67` | 132³ | `fp16` | 569 | 149,708 | 0.035 | 0.0129 |
+| `setup_67` | 132³ | `int8w` | 796 | 107,015 | 0.037 | 0.0399 |
+| `setup_67` | 132³ | `int8w_fp16` | 556 | 153,209 | 0.047 | 0.0434 |
+| `setup_67` | 116³ | `fp32` | 532 | 41,263 | 0.0 | — |
+| `setup_67` | 116³ | `fp16` | 323 | 67,963 | 0.039 | 0.0501 |
+| `setup_67` | 116³ | `int8w` | 529 | 41,497 | 0.043 | 0.0866 |
+| `setup_67` | 116³ | `int8w_fp16` | 327 | 67,131 | 0.066 | 0.0729 |
+
 ## Fidelity vs. fp32 PyTorch (onnxruntime CPU)
 
 One real EM crop per model (jrc_c-elegans-comma-1 at the model's voxel size). `flip %` = voxels whose `logit > 0` decision
@@ -127,7 +214,8 @@ differs from PyTorch. A consistency check, not a re-run of F1 validation.
 
 ## Use in cellmap-flow serverless
 
-ONNX model → remote URL `https://huggingface.co/cellmap/onnx-web-models/resolve/main/<folder>/<file>.onnx`;
+ONNX model → remote URL `https://huggingface.co/cellmap/onnx-web-models/resolve/main/<folder>/<file>.onnx`
+(e.g. `setup_54/setup_54_mito_affinities_16nm_in148_fp32.onnx`);
 Input size `N,N,N`, Output size `N-88,N-88,N-88`, input/output voxel = the model's voxel size, Channels = number of channels
 (Has channel axis = true for the 3-channel affinity models). Each folder's `manifest.json` has the full metadata.
 Anonymous Hugging Face downloads are rate-limited (HTTP 429), so clients should cache the model bytes.

webgpu_bench.json

@@ -0,0 +1,977 @@
+[
+ {
+  "_device": "Measured in Chrome 154 on macOS, Apple GPU (WebGPU `metal-3`), onnxruntime-web 1.27.0-dev.20260506; best of 3 runs after one warm-up run."
+ },
+ {
+  "m": "setup_54",
+  "n": 148,
+  "v": "fp32",
+  "out": 60,
+  "mb": 42.9,
+  "first_ms": 2727,
+  "run_ms": 1203,
+  "vs_torch_max": 0.001,
+  "pos": 0,
+  "torch_pos": 0
+ },
+ {
+  "m": "setup_54",
+  "n": 148,
+  "v": "fp16",
+  "out": 60,
+  "mb": 21.5,
+  "first_ms": 3505,
+  "run_ms": 905,
+  "vs_torch_max": 0.082,
+  "pos": 0,
+  "torch_pos": 0,
+  "vs_fp32_max": 0.137,
+  "flip_pct": 0.0
+ },
+ {
+  "m": "setup_54",
+  "n": 148,
+  "v": "int8w",
+  "out": 60,
+  "mb": 10.9,
+  "first_ms": 1269,
+  "run_ms": 1216,
+  "vs_torch_max": 0.106,
+  "pos": 0,
+  "torch_pos": 0,
+  "vs_fp32_max": 0.183,
+  "flip_pct": 0.0
+ },
+ {
+  "m": "setup_54",
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+  "v": "int8w_fp16",
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+  "vs_torch_max": 0.153,
+  "pos": 0,
+  "torch_pos": 0,
+  "vs_fp32_max": 0.193,
+  "flip_pct": 0.0
+ },
+ {
+  "m": "setup_54",
+  "n": 132,
+  "v": "fp32",
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+  "mb": 42.9,
+  "first_ms": 1580,
+  "run_ms": 796,
+  "vs_torch_max": 0.0,
+  "pos": 0,
+  "torch_pos": 0
+ },
+ {
+  "m": "setup_54",
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+  "first_ms": 2357,
+  "run_ms": 573,
+  "vs_torch_max": 0.072,
+  "pos": 0,
+  "torch_pos": 0,
+  "vs_fp32_max": 0.126,
+  "flip_pct": 0.0
+ },
+ {
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+  "vs_torch_max": 0.096,
+  "pos": 0,
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+  "flip_pct": 0.0
+ },
+ {
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+  "vs_torch_max": 0.09,
+  "pos": 0,
+  "torch_pos": 0,
+  "vs_fp32_max": 0.191,
+  "flip_pct": 0.0
+ },
+ {
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