cellmap/mito-aff-unet-setup-16

mito_aff_unet_setup_16

Generated affinities for mitochondria segmentation using a UNet architecture trained on setup 16 with 400k iterations.

Model Details

Architecture	UNet
Framework	torch
Spatial Dims	3
Input Channels	1
Output Channels	3
Channel Names	mito_aff_1, mito_aff_2, mito_aff_3
Iteration	400000
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="mito_aff_unet_setup_16")
model = CellmapModel(path)

Author

Marwan Zouinkhi

Links

• cellmap-models
• CellMap Project