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cellmap/ld-aff-unet-setup-48
ld-aff-unet-setup-48 is a machine learning model from cellmap. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for cellmap-models. The card lists the license as bsd-3-clause.
Generalist affinities for lipids segmentation using a UNet architecture trained on setup 48 with 380k iterations.
Downloads · 30 days
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Updated Mar 19, 2026
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.pt23.2 GB · 33%
From the Hugging Face model README
Generalist affinities for lipids segmentation using a UNet architecture trained on setup 48 with 380k iterations.
| 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 |
| 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 |
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)
Marwan Zouinkhi