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ArrojoeDrigoLab/
quantem

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QuantEM — organelle segmentation for electron microscopy

Eight segmentation models covering mitochondria, endoplasmic reticulum, nucleus and lipid droplets, each in two encoder sizes:

Family	Encoder	Params	Notes
QuantEM	ViT-B/16	86 M	Trained from scratch on an EM corpus using the DINOv3 recipe.
OmniEM	ViT-L/14	302 M	Heads trained on the published OmniEM (EM-DINO) encoder.

Recommended defaults: QuantEM for mitochondria; OmniEM for ER, nucleus and lipid droplets.

Use

These files are not loaded directly. Install the core library, which resolves, downloads, verifies and assembles them for you:

pip install quantem-core

python

from quantem_em.api import load_model, segment

model = load_model("quantem/mito")          # downloads on first use, then cached
labels = segment(model, image, pixel_size_nm=8.0)

For a GUI, install the napari plugin or the application instead — they incorporate segmentation, fine-tuning, and downstream metrics. See https://github.com/ArrojoDrigoLab/QuantEM

Files

Encoders are split to avoid inefficient duplicate weight downloads.

File	Size	Contents	SHA-256
`quantem-vitb-trunk.safetensors`	227.7 MB	QuantEM ViT-B/16 blocks 0-7 + embeddings + final norm. Shared by the mitochondria, nucleus and lipid-droplet heads, whose own artifacts carry the fine-tuned blocks 8-11.	`637a8c321a7b2172…`
`omniem-vitl.safetensors`	1.2 GB	OmniEM (EM-DINO) ViT-L/14 encoder. Untouched by LoRA, so genuinely shared by all four OmniEM heads.	`d7f2dffe2ec23138…`
`quantem-mito.safetensors`	136.5 MB	model	`a897bf322872d1ae…`
`quantem-nucleus.safetensors`	136.5 MB	model	`d5152b6c2b5ccdbf…`
`quantem-ld.safetensors`	136.5 MB	model	`765d1a8e281edf7e…`
`quantem-er.safetensors`	465.0 MB	Self-contained: adapt=full replaces the entire encoder, so this needs no trunk.	`50bcdfedc497041f…`
`omniem-mito.safetensors`	25.7 MB	model	`7e5c2c8b6ffede26…`
`omniem-nucleus.safetensors`	25.7 MB	model	`3142057d3b36b482…`
`omniem-ld.safetensors`	25.7 MB	model	`ae5b0c356e0fb48d…`
`omniem-er.safetensors`	135.2 MB	model	`3e3a693ea757d500…`

Every file is verified against the SHA-256 above on download.

Licence

The weights are released under CC BY 4.0. If using OmniEM-based models, you should cite its original publication in addition to ours: https://www.biorxiv.org/content/10.1101/2025.04.13.648639

Attribution

Full per-source tables, with tile and crop counts and a DOI for every entry, are published as datasets alongside these weights:

ArrojoeDrigoLab/quantem-organelle-model-sources — the annotated ground truth behind these eight models, per organelle.
ArrojoeDrigoLab/quantem-base-model-sources — all 655 datasets in the corpus the base encoder was pretrained on.

Citation

Acree et al., QuantEM: An optimized platform of vision transformer-based models for segmentation and analysis of electron microscopy data. Citation details on publication.

ArrojoeDrigoLab/quantem