---
license: cc-by-4.0
library_name: quantem-core
pipeline_tag: image-segmentation
tags:
  - electron-microscopy
  - image-segmentation
  - organelle
  - mitochondria
  - vision-transformer
  - napari
---

# 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:

```bash
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](https://creativecommons.org/licenses/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`](https://huggingface.co/datasets/ArrojoeDrigoLab/quantem-organelle-model-sources)**
  — the annotated ground truth behind these eight models, per organelle.
* **[`ArrojoeDrigoLab/quantem-base-model-sources`](https://huggingface.co/datasets/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.