Image Feature Extraction
timm
Safetensors
pathology
histopathology
her2
breast-cancer
h-and-e
vision-transformer
multiple-instance-learning
Instructions to use cloudkites/her2-reader with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use cloudkites/her2-reader with timm:
import timm model = timm.create_model("hf-hub:cloudkites/her2-reader", pretrained=True) - Notebooks
- Google Colab
- Kaggle
Download data/paper/paper-data/rank_ty.dat from cloudkites/her2-reader: direct link, hf CLI and curl.
- Browser
- Download file 374 Bytes
-
https://huggingface.co/cloudkites/her2-reader/resolve/main/data/paper/paper-data/rank_ty.dat
- Command line
-
hf download hf://cloudkites/her2-reader/data/paper/paper-data/rank_ty.dat
-
curl -L -o rank_ty.dat https://huggingface.co/cloudkites/her2-reader/resolve/main/data/paper/paper-data/rank_ty.dat
374 Bytes
| y ty sd params label own | |
| 0 0.5890 0.0160 22 {DINOv2-S, natural images (22M)} 0 | |
| 1 0.6380 0.0220 26 {ResNet-50, ImageNet (26M)} 0 | |
| 2 0.7270 0.0290 1100 {Midnight-12k, ViT-g/14 (1.1B)} 0 | |
| 3 0.7490 0.0270 86 {Hibou-B, ViT-B/14 (86M)} 0 | |
| 4 0.8050 0.0200 1100 {H-optimus-0, ViT-g/14 (1.1B)} 0 | |
| 5 0.8110 0.0140 86 {Phikon, ViT-B/16 (86M)} 0 | |
| 6 0.8120 0.0150 22 {Ours, ViT-S/16 (22M)} 1 | |