metadata
title: CellMap
emoji: 🔬
colorFrom: blue
colorTo: green
CellMap - HHMI Janelia
The CellMap project team at HHMI Janelia Research Campus develops machine learning models for segmentation of cellular structures in volume electron microscopy (vEM) data.
Models
We provide pre-trained deep learning models for 3D organelle segmentation, available in multiple formats:
| Format | Extension | Use Case |
|---|---|---|
| PyTorch | .pt |
Full model for Python workflows |
| TorchScript | .ts |
Deployment without Python dependencies |
| ONNX | .onnx |
Cross-framework inference |
Available Segmentation Targets
- Mitochondria
- Endoplasmic Reticulum (ER)
- Nucleus
- Peroxisomes
- Plasma Membrane
- Vesicles
- And more...
Quick Start
pip install cellmap-models
from cellmap_models.model_export.cellmap_model import CellmapModel
from huggingface_hub import snapshot_download
# Download and load any model
path = snapshot_download(repo_id="cellmap/your-model-name")
model = CellmapModel(path)
# Inference
output = model.ts_model(input_tensor)
# Finetuning
trainable_model = model.train()
Resources
- cellmap-models (GitHub) - Model architectures, weights, and export tools
- CellMap Project Page
- OpenOrganelle - Browse our EM datasets
Publications
- Heinrich, L. et al. Whole-cell organelle segmentation in volume electron microscopy. Nature (2021)
Contact
For questions or collaborations, visit our GitHub or reach out through the Janelia CellMap project page.