cellmap/onnx-web-models

CellMap ONNX web models

Small 3D U-Nets for FIB-SEM organelle segmentation (mitochondria, nuclei, ER), exported to ONNX for in-browser inference with onnxruntime-web in the cellmap-flow serverless client.

Every file takes input float32 [1,1,z,y,x] and returns output float32 raw logits [1,C,z',y',x'] (apply sigmoid, or threshold at 0). Input normalization: uint8 / 255 * 2 - 1 ([{"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
setup_54	mito_affinities	mito_aff_z, mito_aff_y, mito_aff_x	16	ResUNet3DValid	10.7M	260000	0.958
setup_53	mito_affinities	mito_aff_z, mito_aff_y, mito_aff_x	16	Unet3dIso	5.6M	340000	0.955
setup_56	nuc	nuc	128	ResUNet3DValid	10.7M	350000	0.765
setup_55	nuc	nuc	128	Unet3dIso	5.6M	330000	0.645
setup_61	nuc	nuc	256	ResUNet3DValid	10.7M	20000	0.738
setup_67	er	er	8	ResUNet3DValid	10.7M	20000	0.806

Download links and speed (recommended 2D-rewrite layout)

Times are steady-state ms per block on WebGPU (Chrome, Apple M-series GPU, real EM; see benchmark below). Lower ms is better; out Mvox/s = output voxels per second (higher is better). fp16 is 1.3–2.2× faster than fp32, halves download and GPU memory, and changes ≤0.05% of logit > 0 decisions. fp32 is exact and also runs on the WASM (CPU) fallback. The largest block of each model gives the best throughput (88 voxels of context are lost per axis per block).

model	target	voxel	in → out	fp32	fp32 ms	fp16	fp16 ms	fp16 speedup	out Mvox/s fp32 / fp16
setup_54	mito_affinities (3 ch)	16 nm	148³ → 60³	fp32 (43 MB)	1203	fp16 (22 MB)	905	1.33×	0.18 / 0.24
setup_54	mito_affinities (3 ch)	16 nm	132³ → 44³	fp32 (43 MB)	796	fp16 (22 MB)	573	1.39×	0.11 / 0.15
setup_54	mito_affinities (3 ch)	16 nm	116³ → 28³	fp32 (43 MB)	516	fp16 (22 MB)	323	1.60×	0.04 / 0.07
setup_53	mito_affinities (3 ch)	16 nm	172³ → 84³	fp32 (23 MB)	1137	fp16 (11 MB)	741	1.53×	0.52 / 0.80
setup_53	mito_affinities (3 ch)	16 nm	140³ → 52³	fp32 (23 MB)	533	fp16 (11 MB)	336	1.59×	0.26 / 0.42
setup_53	mito_affinities (3 ch)	16 nm	124³ → 36³	fp32 (23 MB)	348	fp16 (11 MB)	157	2.22×	0.13 / 0.30
setup_56	nuc (1 ch)	128 nm	148³ → 60³	fp32 (43 MB)	1208	fp16 (22 MB)	921	1.31×	0.18 / 0.23
setup_56	nuc (1 ch)	128 nm	132³ → 44³	fp32 (43 MB)	811	fp16 (22 MB)	574	1.41×	0.11 / 0.15
setup_56	nuc (1 ch)	128 nm	116³ → 28³	fp32 (43 MB)	527	fp16 (22 MB)	324	1.63×	0.04 / 0.07
setup_55	nuc (1 ch)	128 nm	172³ → 84³	fp32 (23 MB)	1080	fp16 (11 MB)	758	1.42×	0.55 / 0.78
setup_55	nuc (1 ch)	128 nm	140³ → 52³	fp32 (23 MB)	506	fp16 (11 MB)	319	1.59×	0.28 / 0.44
setup_55	nuc (1 ch)	128 nm	124³ → 36³	fp32 (23 MB)	331	fp16 (11 MB)	156	2.12×	0.14 / 0.30
setup_61	nuc (1 ch)	256 nm	148³ → 60³	fp32 (43 MB)	1220	fp16 (22 MB)	900	1.36×	0.18 / 0.24
setup_61	nuc (1 ch)	256 nm	132³ → 44³	fp32 (43 MB)	809	fp16 (22 MB)	574	1.41×	0.11 / 0.15
setup_61	nuc (1 ch)	256 nm	116³ → 28³	fp32 (43 MB)	514	fp16 (22 MB)	322	1.60×	0.04 / 0.07
setup_67	er (1 ch)	8 nm	148³ → 60³	fp32 (43 MB)	1233	fp16 (22 MB)	890	1.39×	0.18 / 0.24
setup_67	er (1 ch)	8 nm	132³ → 44³	fp32 (43 MB)	809	fp16 (22 MB)	569	1.42×	0.11 / 0.15
setup_67	er (1 ch)	8 nm	116³ → 28³	fp32 (43 MB)	532	fp16 (22 MB)	323	1.65×	0.04 / 0.07

Native-3D versions of every file are under conv3d/ with the same names (e.g. https://huggingface.co/cellmap/onnx-web-models/resolve/main/conv3d/setup_54/setup_54_mito_affinities_16nm_in148_fp32.onnx). On WebGPU they are ~16× slower (setup_54 148³ fp32: 20.9 s vs 1.2 s per block) and fp16 does not help there (17–25 s), because onnxruntime-web runs Conv3d with a naive shader; use them for onnxruntime CUDA/CPU or TensorRT.

Compression variants

variant	weights / compute	file vs fp32	notes
fp32	fp32 / fp32	1×	reference; best accuracy, works on WebGPU and WASM (CPU)
fp16	fp16 / fp16	½	halves download and activation memory; WebGPU only (poor fp16 support on WASM)
int8w	int8 per-channel / fp32	¼	smaller download only (weights dequantized at load)
int8w_fp16	int8 per-channel / fp16	¼	smallest download + fp16 activations

Graph rewrites for WebGPU

Both are exact (same math, same weights; verified against the PyTorch model):

• Conv3d → Conv2d over a depth-folded batch. Each 3D conv is computed as a sum of 2D convs, one per depth tap, on the volume reshaped to [depth, C, H, W]. onnxruntime-web runs 3D convs with a naive WebGPU shader but 2D convs with tuned matmul kernels, so this is ~16× faster (setup_54, 148³ block, Apple M-series GPU: 20.9 s → 1.2 s).
• 3D ConvTranspose → 1×1×1 conv + depth-to-space (Unet3dIso decoders); the WebGPU EP has no 3D ConvTranspose.

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 differs from PyTorch. A consistency check, not a re-run of F1 validation.

file	MB	in → out	max abs logit diff	mean abs diff	flip %
setup_54/setup_54_mito_affinities_16nm_in148_fp32.onnx	42.9	148³ → 60³	0.0005	0.0001	0.0000
setup_54/setup_54_mito_affinities_16nm_in148_fp16.onnx	21.6	148³ → 60³	0.0142	0.0024	0.0009
setup_54/setup_54_mito_affinities_16nm_in148_int8w.onnx	10.9	148³ → 60³	0.1366	0.0198	0.0094
setup_54/setup_54_mito_affinities_16nm_in148_int8w_fp16.onnx	10.9	148³ → 60³	0.1139	0.0191	0.0080
setup_54/setup_54_mito_affinities_16nm_in132_fp32.onnx	42.9	132³ → 44³	0.0003	0.0000	0.0000
setup_54/setup_54_mito_affinities_16nm_in132_fp16.onnx	21.6	132³ → 44³	0.0149	0.0025	0.0000
setup_54/setup_54_mito_affinities_16nm_in132_int8w.onnx	10.9	132³ → 44³	0.1452	0.0203	0.0192
setup_54/setup_54_mito_affinities_16nm_in132_int8w_fp16.onnx	10.9	132³ → 44³	0.1202	0.0191	0.0160
setup_54/setup_54_mito_affinities_16nm_in116_fp32.onnx	42.9	116³ → 28³	0.0001	0.0000	0.0000
setup_54/setup_54_mito_affinities_16nm_in116_fp16.onnx	21.6	116³ → 28³	0.0134	0.0026	0.0015
setup_54/setup_54_mito_affinities_16nm_in116_int8w.onnx	10.9	116³ → 28³	0.1302	0.0213	0.0137
setup_54/setup_54_mito_affinities_16nm_in116_int8w_fp16.onnx	10.9	116³ → 28³	0.1188	0.0209	0.0076
setup_53/setup_53_mito_affinities_16nm_in172_fp32.onnx	22.5	172³ → 84³	0.0000	0.0000	0.0000
setup_53/setup_53_mito_affinities_16nm_in172_fp16.onnx	11.3	172³ → 84³	0.0618	0.0072	0.0044
setup_53/setup_53_mito_affinities_16nm_in172_int8w.onnx	5.8	172³ → 84³	0.4799	0.0488	0.0380
setup_53/setup_53_mito_affinities_16nm_in172_int8w_fp16.onnx	5.8	172³ → 84³	0.5517	0.0509	0.0351
setup_53/setup_53_mito_affinities_16nm_in140_fp32.onnx	22.5	140³ → 52³	0.0000	0.0000	0.0000
setup_53/setup_53_mito_affinities_16nm_in140_fp16.onnx	11.3	140³ → 52³	0.0618	0.0073	0.0009
setup_53/setup_53_mito_affinities_16nm_in140_int8w.onnx	5.8	140³ → 52³	0.3368	0.0430	0.0085
setup_53/setup_53_mito_affinities_16nm_in140_int8w_fp16.onnx	5.8	140³ → 52³	0.3255	0.0451	0.0092
setup_53/setup_53_mito_affinities_16nm_in124_fp32.onnx	22.5	124³ → 36³	0.0000	0.0000	0.0000
setup_53/setup_53_mito_affinities_16nm_in124_fp16.onnx	11.3	124³ → 36³	0.0396	0.0069	0.0007
setup_53/setup_53_mito_affinities_16nm_in124_int8w.onnx	5.8	124³ → 36³	0.2672	0.0384	0.0129
setup_53/setup_53_mito_affinities_16nm_in124_int8w_fp16.onnx	5.8	124³ → 36³	0.2747	0.0395	0.0157
setup_56/setup_56_nuc_128nm_in148_fp32.onnx	42.9	148³ → 60³	0.0035	0.0003	0.0005
setup_56/setup_56_nuc_128nm_in148_fp16.onnx	21.6	148³ → 60³	0.0539	0.0065	0.0069
setup_56/setup_56_nuc_128nm_in148_int8w.onnx	10.9	148³ → 60³	0.5656	0.0669	0.0755
setup_56/setup_56_nuc_128nm_in148_int8w_fp16.onnx	10.9	148³ → 60³	0.4653	0.0600	0.0560
setup_56/setup_56_nuc_128nm_in132_fp32.onnx	42.9	132³ → 44³	0.0019	0.0002	0.0000
setup_56/setup_56_nuc_128nm_in132_fp16.onnx	21.6	132³ → 44³	0.0543	0.0064	0.0012
setup_56/setup_56_nuc_128nm_in132_int8w.onnx	10.9	132³ → 44³	0.4437	0.0661	0.0153
setup_56/setup_56_nuc_128nm_in132_int8w_fp16.onnx	10.9	132³ → 44³	0.4508	0.0606	0.0117
setup_56/setup_56_nuc_128nm_in116_fp32.onnx	42.9	116³ → 28³	0.0004	0.0001	0.0000
setup_56/setup_56_nuc_128nm_in116_fp16.onnx	21.6	116³ → 28³	0.0405	0.0067	0.0046
setup_56/setup_56_nuc_128nm_in116_int8w.onnx	10.9	116³ → 28³	0.4412	0.0668	0.0820
setup_56/setup_56_nuc_128nm_in116_int8w_fp16.onnx	10.9	116³ → 28³	0.3753	0.0611	0.0638
setup_55/setup_55_nuc_128nm_in172_fp32.onnx	22.5	172³ → 84³	0.0000	0.0000	0.0000
setup_55/setup_55_nuc_128nm_in172_fp16.onnx	11.3	172³ → 84³	0.1344	0.0191	0.0235
setup_55/setup_55_nuc_128nm_in172_int8w.onnx	5.8	172³ → 84³	0.5892	0.0901	0.0995
setup_55/setup_55_nuc_128nm_in172_int8w_fp16.onnx	5.8	172³ → 84³	0.5302	0.0846	0.0957
setup_55/setup_55_nuc_128nm_in140_fp32.onnx	22.5	140³ → 52³	0.0000	0.0000	0.0000
setup_55/setup_55_nuc_128nm_in140_fp16.onnx	11.3	140³ → 52³	0.0801	0.0183	0.0036
setup_55/setup_55_nuc_128nm_in140_int8w.onnx	5.8	140³ → 52³	0.4925	0.0885	0.0220
setup_55/setup_55_nuc_128nm_in140_int8w_fp16.onnx	5.8	140³ → 52³	0.4770	0.0798	0.0171
setup_55/setup_55_nuc_128nm_in124_fp32.onnx	22.5	124³ → 36³	0.0000	0.0000	0.0000
setup_55/setup_55_nuc_128nm_in124_fp16.onnx	11.3	124³ → 36³	0.0660	0.0168	0.0021
setup_55/setup_55_nuc_128nm_in124_int8w.onnx	5.8	124³ → 36³	0.4925	0.0864	0.0000
setup_55/setup_55_nuc_128nm_in124_int8w_fp16.onnx	5.8	124³ → 36³	0.4770	0.0763	0.0000
setup_61/setup_61_nuc_256nm_in148_fp32.onnx	42.9	148³ → 60³	0.0541	0.0053	0.0000
setup_61/setup_61_nuc_256nm_in148_fp16.onnx	21.6	148³ → 60³	0.0977	0.0085	0.0000
setup_61/setup_61_nuc_256nm_in148_int8w.onnx	10.9	148³ → 60³	1.2152	0.0847	0.0000
setup_61/setup_61_nuc_256nm_in148_int8w_fp16.onnx	10.9	148³ → 60³	1.4691	0.0912	0.0000
setup_61/setup_61_nuc_256nm_in132_fp32.onnx	42.9	132³ → 44³	0.0133	0.0015	0.0000
setup_61/setup_61_nuc_256nm_in132_fp16.onnx	21.6	132³ → 44³	0.0425	0.0061	0.0000
setup_61/setup_61_nuc_256nm_in132_int8w.onnx	10.9	132³ → 44³	0.7255	0.0742	0.0012
setup_61/setup_61_nuc_256nm_in132_int8w_fp16.onnx	10.9	132³ → 44³	0.8130	0.0792	0.0012
setup_61/setup_61_nuc_256nm_in116_fp32.onnx	42.9	116³ → 28³	0.0023	0.0003	0.0000
setup_61/setup_61_nuc_256nm_in116_fp16.onnx	21.6	116³ → 28³	0.0283	0.0052	0.0000
setup_61/setup_61_nuc_256nm_in116_int8w.onnx	10.9	116³ → 28³	0.4741	0.0666	0.0000
setup_61/setup_61_nuc_256nm_in116_int8w_fp16.onnx	10.9	116³ → 28³	0.4942	0.0685	0.0000
setup_67/setup_67_er_8nm_in148_fp32.onnx	42.9	148³ → 60³	0.0004	0.0000	0.0000
setup_67/setup_67_er_8nm_in148_fp16.onnx	21.6	148³ → 60³	0.0127	0.0017	0.0019
setup_67/setup_67_er_8nm_in148_int8w.onnx	10.9	148³ → 60³	0.1386	0.0136	0.0171
setup_67/setup_67_er_8nm_in148_int8w_fp16.onnx	10.9	148³ → 60³	0.1204	0.0131	0.0153
setup_67/setup_67_er_8nm_in132_fp32.onnx	42.9	132³ → 44³	0.0003	0.0000	0.0000
setup_67/setup_67_er_8nm_in132_fp16.onnx	21.6	132³ → 44³	0.0091	0.0020	0.0000
setup_67/setup_67_er_8nm_in132_int8w.onnx	10.9	132³ → 44³	0.0754	0.0144	0.0047
setup_67/setup_67_er_8nm_in132_int8w_fp16.onnx	10.9	132³ → 44³	0.0841	0.0145	0.0012
setup_67/setup_67_er_8nm_in116_fp32.onnx	42.9	116³ → 28³	0.0001	0.0000	0.0000
setup_67/setup_67_er_8nm_in116_fp16.onnx	21.6	116³ → 28³	0.0090	0.0022	0.0364
setup_67/setup_67_er_8nm_in116_int8w.onnx	10.9	116³ → 28³	0.0902	0.0194	0.2870
setup_67/setup_67_er_8nm_in116_int8w_fp16.onnx	10.9	116³ → 28³	0.0838	0.0206	0.2824

Use in cellmap-flow serverless

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.