| | --- |
| | license: mit |
| | language: |
| | - en |
| | tags: |
| | - histology |
| | - pathology |
| | - vision |
| | - pytorch |
| | - self-supervised |
| | - vit |
| | extra_gated_prompt: >- |
| | link https://huggingface.co/MahmoodLab/UNI |
| | extra_gated_fields: |
| | Full name (first and last): text |
| | Current affiliation (no abbreviations): text |
| | Type of Affiliation: |
| | type: select |
| | options: |
| | - Academia |
| | - Industry |
| | - label: Other |
| | value: other |
| | Current and official institutional email (**this must match your primary email in your Hugging Face account, @gmail/@hotmail/@qq email domains will be denied**): text |
| | Please explain your intended research use: text |
| | I agree to all terms outlined above: checkbox |
| | I agree to use this model for non-commercial, academic purposes only: checkbox |
| | I agree not to distribute the model, if another user within your organization wishes to use the UNI model, they must register as an individual user: checkbox |
| | metrics: |
| | - accuracy |
| | pipeline_tag: image-feature-extraction |
| | library_name: timm |
| | --- |
| | |
| | # Updates: |
| | UNI2, a successor to UNI, trained on over 200 million images from over 350k diverse H&E and IHC slides has been released! Model weights and instructions are available at: \[[Huggingface Repo](https://huggingface.co/MahmoodLab/UNI2-h)\] |
| |
|
| | ``` |
| | Works that use UNI should also attribute ViT and DINOv2. |
