ONNX Model Zoo

cellmap/ld-aff-unet-setup-48

By cellmap · ↓ 0 · ♥ 0 · Updated 2026-03-19

What's in this repo

This page lists the files in cellmap/ld-aff-unet-setup-48.

  • No .onnx files in this repository
  • 6 files, including 1 .md, 1 .json, 1 .pt, 1 .pt2, 1 .ts
  • Last updated 2026-03-19

ONNX files

This repository has no .onnx files. See the full file list on the right for other formats.

How to use

These ONNX files can be loaded with ONNX Runtime. To inspect a model graph, upload the file to the Netron viewer. Download links point to the original files on Hugging Face.

README

ld_aff_unet_setup_48

Generalist affinities for lipids segmentation using a UNet architecture trained on setup 48 with 380k iterations.

Model Details

Architecture UNet
Framework torch
Spatial Dims 3
Input Channels 1
Output Channels 3
Channel Names ld_aff_1, ld_aff_2, ld_aff_3
Iteration 380000
Input Voxel Size 16, 16, 16 nm
Output Voxel Size 16, 16, 16 nm
Inference Input Shape 378, 378, 378
Inference Output Shape 256, 256, 256

Available Formats

File Format Usage
model.pt PyTorch pickle torch.load("model.pt")
model.ts TorchScript torch.jit.load("model.ts")
model.onnx ONNX onnxruntime.InferenceSession("model.onnx")
metadata.json JSON Model metadata

Usage

pip install cellmap-models
from cellmap_models.model_export.cellmap_model import CellmapModel

model = CellmapModel("path/to/model/folder")

# Inference
output = model.ts_model(input_tensor)

# Finetuning
trainable_model = model.train()

Or download from this repo and load directly:

from huggingface_hub import snapshot_download
from cellmap_models.model_export.cellmap_model import CellmapModel

path = snapshot_download(repo_id="ld_aff_unet_setup_48")
model = CellmapModel(path)

Author

Marwan Zouinkhi

Links

Model info

cellmap-models
cellmap-models pytorch onnx torchscript 3d segmentation electron-microscopy cellmap ld_aff_1 ld_aff_2 ld_aff_3 license:bsd-3-clause
↓ 0 ♥ 0

Created: 2026-03-19

Updated: 2026-03-19

View on Hugging Face

Files (6)

.gitattributes
README.md
metadata.json
model.pt
model.pt2
model.ts

Related models