Model Zoo

Model Zoo#

This section provides an overview of models in LazySlide.

Important

Disclaimer: The usage of any model in LazySlide is subject to the terms and conditions of the respective model’s license. Please ensure you comply with the license terms before using any model. If you use a model in your research, please cite the original paper or repository as appropriate. LazySlide do not redistribute any source code that’s not compatible with LazySlide’s MIT license.

Get model names#

In most of the cases, you only need to pass the model name as string to the function, for example, to use the UNI model in feature extraction, you can do: zs.tl.feature_extraction(wsi, model="uni").

To get all available models, you can use the list_models function:

from lazyslide.models import list_models

models = list_models()

You can also filter models by type:

from lazyslide.models import list_models

vision_models = list_models("vision")  # for vision models only
multimodal_models = list_models("multimodal")  # for multimodal models only
segmentation_models = list_models("segmentation")  # for segmentation models only
tile_prediction_models = list_models("tile_prediction")  # for tile_prediction models only

For feature extraction, we also support all timm models with feature extraction head. You can list them with:

from timm import list_models

timm_models = list_models()

Get access to gated models#

indicates the model is publicly available. You can use it without a Hugging Face account or requesting access.

indicates the model is gated and requires permission. You must apply for access via the Hugging Face model card or the model’s repository.

To access gated models, follow these steps:

Create a Hugging Face account: https://huggingface.co/
Visit the model card page and request access. You can also use the Hugging Face button provided for each model below.
In your account settings, go to Access Tokens and create a new token with the required permissions (read access is sufficient). This token will grant access to any new models you gain permission for in the future.
Log in using your token to access gated models. Run the following command:
```
huggingface-cli login --token YOUR_TOKEN
```

Below is a list of available models categorized by their type:

Vision models#

`UNI`	🤗Hugging Face GitHub Paper [Chen et al., 2024] General-purpose self-supervised model for pathology
`UNI2`	🤗Hugging Face GitHub Paper [Chen et al., 2024] An improved version of UNI
`GigaPath`	🤗Hugging Face GitHub Paper [Xu et al., 2024] A whole-slide foundation model for digital pathology
`PLIPVision`	🤗Hugging Face GitHub Paper [Huang et al., 2023] Pathology Language-Image Pretraining (PLIP)
`CONCHVision`	🤗Hugging Face GitHub Paper [Lu et al., 2024] CONtrastive learning from Captions for Histopathology (CONCH)
`Virchow`	🤗Hugging Face Paper [Vorontsov et al., 2024] A foundation model for clinical-grade computational pathology and rare cancers detection
`Virchow2`	🤗Hugging Face Paper [Zimmermann et al., 2024] Scaling self-supervised mixed magnification models in pathology
`Phikon`	🤗Hugging Face GitHub Paper [Filiot et al., 2023] Scaling self-Supervised Learning for histopathology with Masked Image Modeling
`PhikonV2`	🤗Hugging Face GitHub Paper [Filiot et al., 2024] A large and public feature extractor for biomarker prediction
`HOptimus0`	🤗Hugging Face GitHub [Saillard et al., 2024] Vision foundation model
`HOptimus1`	🤗Hugging Face GitHub [Bioptimus, 2025] Vision foundation model
`H0Mini`	🤗Hugging Face GitHub Paper [Filiot et al., 2025] A distilled version of H-optimus-0
`HibouB`	🤗Hugging Face GitHub Paper [Nechaev et al., 2024] A family of foundational vision transformers for pathology
`HibouL`	🤗Hugging Face GitHub Paper [Nechaev et al., 2024] A family of foundational vision transformers for pathology

Multimodal Models#

`PLIP`	🤗Hugging Face GitHub Paper [Huang et al., 2023] Pathology Language-Image Pretraining (PLIP)
`CONCH`	🤗Hugging Face GitHub Paper [Lu et al., 2024] CONtrastive learning from Captions for Histopathology (CONCH)
`Titan`	🤗Hugging Face GitHub Paper [Ding et al., 2024] Multimodal whole slide foundation model for pathology
`Prism`	🤗Hugging Face Paper [Shaikovski et al., 2024] A multi-modal generative foundation model for slide-level histopathology, the Prism models encode slide-level embeddings from `Virchow`.
`OmiCLIP`	🤗Hugging Face GitHub Paper [Chen et al., 2025] A visual-omics foundation model to bridge histopathology with spatial transcriptomics

Segmentation Models#

`Instanseg`	GitHub Paper [Goldsborough et al., 2024] An embedding-based instance segmentation algorithm optimized for accurate, efficient and portable cell segmentation.
`NuLite`	GitHub Paper [Tommasino et al., 2024] Nuclei instance segmentation and classification
`GrandQCTissue`	GitHub Paper [Weng et al., 2024] Tissue segmentation model from GrandQC
`GrandQCArtifact`	GitHub Paper [Weng et al., 2024] Artifact segmentation model from GrandQC
`SMPBase`	This is a base class for any models from segmentation models pytorch

Base Models#

`ModelBase`
`ImageModel`
`ImageTextModel`
`SegmentationModel`
`SlideEncoderModel`
`TimmModel`