DINOv3
Paper • 2508.10104 • Published • 319
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H&E-12 Cellpose Starter
A starting model for training HistoForge to separate nuclei, cells and other individual objects, including irregular or elongated shapes. It uses Cellpose's flow-based method.
It retains the H&E-12 nucleus-pretrained model, including its segmentation layers.
Compare starter models
H&E-12 starters include histology pretraining. DINOv3 starters use a pretrained image encoder with newly initialized segmentation layers.
| Method | Start here for | H&E-12 weights | DINOv3 weights |
|---|---|---|---|
| Cellpose | Nuclei, cells and irregular or elongated objects | H&E-12 Cellpose | DINOv3 Cellpose |
| HoVer | Large individual tissue structures | H&E-12 HoVer | DINOv3 HoVer |
| Dense semantic | Tissue compartments, rather than separate cells | H&E-12 Semantic | DINOv3 Semantic |
Cellpose and HoVer can separate touching nuclei. These are starting recommendations, not an accuracy or speed ranking; compare trained results on held-out images.
Using the module
Install histoforge/starter-he12-cellpose and select it as the starting model when training from your QuPath annotations. Define your target classes and image resolution, train, then validate the resulting model on held-out images before using its predictions.
| Output class | What it marks |
|---|---|
| Object | Individual segmented objects |
This starter has no instance-classification head. Classification can be added when defining your training task.
Measurements
No task-specific measurements are included.
Limitations
- Objects spanning many pixels may need more Cellpose flow iterations to form complete objects. HistoForge fixes this inference setting internally; increasing training epochs does not change it.
- The flow head was trained for nuclei. Adapt and validate it for your tissue, stain and target structures.
- This starter has no task-specific accuracy benchmark or prediction gallery.
- Input images must be RGB with valid pixel-size calibration. The initial recipe uses 0.25 microns per pixel; choose the resolution appropriate for your training data.
Models and training
| Component | Initialization |
|---|---|
| DINOv3 ViT-L/16 backbone | H&E nucleus-pretrained |
| Spatial adapter and feature readout | H&E nucleus-pretrained |
| Task head | H&E nucleus-pretrained flow head |
The model uses the shared 256-channel spatial adapter and four CNN-transformer interaction blocks. Neural tiles are 256 pixels with 10% overlap; the complete settings are in recipe.json.
H&E-12 used 5,022 eligible training images from twelve nucleus datasets. See training data for sources and licences. These datasets have already influenced the initializer and cannot automatically serve as independent downstream holdouts.
Licences and citations
- Weights: DINOv3 licence.
- Foundation model: Simeoni et al., DINOv3 (2025).
Model tree for histoforge/starter-he12-cellpose
Base model
timm/vit_large_patch16_dinov3.lvd1689m