Instructions to use peng-lab/phoenix with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use peng-lab/phoenix with timm:
import timm model = timm.create_model("hf_hub:peng-lab/phoenix", pretrained=True) - Notebooks
- Google Colab
- Kaggle
| license: cc-by-nc-4.0 | |
| datasets: | |
| - peng-lab/nest | |
| language: | |
| - en | |
| - de | |
| metrics: | |
| - spearmanr | |
| - pearsonr | |
| - mse | |
| base_model: | |
| - bioptimus/H-optimus-1 | |
| - bioptimus/H0-mini | |
| - MahmoodLab/UNI2-h | |
| - paige-ai/Virchow2 | |
| pipeline_tag: zero-shot-classification | |
| tags: | |
| - spatial-transcriptomics | |
| - histology | |
| - pathology | |
| library_name: timm | |