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

Finetuning in the browser and PyTorch originals

Each folder's manifest.json has a finetune block listing the modes the model offers; a model without the block, or without a mode, is not offered that mode. source_repo points to the PyTorch twin (model_state_dict.pt + module code model_def.py, plus model.ts / metadata.json for the cellmap-flow server catalog); 2D students also name their 3D teacher in distilled_from_repo.

head: train the final 1×1 conv on frozen features (features tensor, weight / bias initializers).
full: train the whole network with TF.js on WebGPU: finetune/train_graph.json (op list, NHWC for the 2D students, NDHWC for the 3D models) + the reference runtime finetune/cellmap_tfjs_train.js. 2D: parameters named as the ONNX initializers (weight_map identity). 3D: parameters are the PyTorch state_dict (Conv3d [O,I,kd,kh,kw], ConvTranspose3d [I,O,2,2,2]), traced from a native-Conv3d export (BatchNorm unfused) and checked by replaying the graph in PyTorch against the original model.

finetune/initializers.json gives every initializer's byte offset in each .onnx (raw_data inline, fixed shapes), so trained weights are written back in place (as fp16 in the fp16 files) and served as before; int8w files are not writable. In the 3D folders the served graph is rewritten for onnxruntime-web (Conv3d split into convs.<k> Conv2d per depth tap, ConvTranspose3d k=s=2 as a 1×1 conv to Cout·8 channels + depth-to-space, channel c·8 + a·4 + b·2 + e), so: finetune/torch_params.json rebuilds each PyTorch param from the .onnx (from_onnx), and finetune/weight_map.json maps each ONNX initializer back from its PyTorch param (to_onnx). Both are JSON op lists — index [axis,k], permute, reshape, repeat_interleave [axis,n], stack — e.g. ConvTranspose3d weight permute [1,2,3,4,0] + reshape [Cout·8,Cin,1,1], bias repeat_interleave [0,8]; the older Python-syntax transform string is kept.

folder	source_repo	modes	full: step at 556² / 300² (peak)
`setup_54`	cellmap/mito-aff-resunet-setup-54	head, full	—
`setup_53`	cellmap/mito-aff-unetiso-setup-53	head, full	—
`setup_56`	cellmap/nuc-resunet-setup-56	head, full	—
`setup_55`	cellmap/nuc-unetiso-setup-55	head, full	—
`setup_61`	cellmap/nuc-resunet-setup-61	head, full	—
`setup_67`	cellmap/er-resunet-setup-67	head, full	—
`setup_53_2d`	cellmap/mito-aff-unetiso-setup-53-2d	head, full	1189 ms (3.9 GB) / 308 ms (1.0 GB)
`setup_54_2d`	cellmap/mito-aff-resunet-setup-54-2d	head, full	1963 ms (10.7 GB) / 490 ms (2.7 GB)

Full-mode step times (2D students): TF.js 4.22 WebGPU, Chrome, Apple M-series GPU, batch 1, fp32, masked BCE + Adam; peak = TF.js tracked tensors (manifest.json → finetune.full.spike). No timings published for the 3D models: with plain TF.js autodiff (as in the reference runtime) every intermediate is kept until the backward pass, so only small blocks fit.

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.

Downloads last month: -; Downloads are not tracked for this model. How to track

Inference Providers NEW

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support