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/jepa_trade.dat from cloudkites/her2-reader: direct link, hf CLI and curl.
- Browser
- Download file 508 Bytes
-
https://huggingface.co/cloudkites/her2-reader/resolve/main/data/paper/paper-data/jepa_trade.dat
- Command line
-
hf download hf://cloudkites/her2-reader/data/paper/paper-data/jepa_trade.dat
-
curl -L -o jepa_trade.dat https://huggingface.co/cloudkites/her2-reader/resolve/main/data/paper/paper-data/jepa_trade.dat
508 Bytes
| dyy dty cls label | |
| -0.0160 0.0270 s {M6.D (2 seeds)} | |
| -0.0220 0.0260 s {} | |
| 0.0100 0.0210 s {v5} | |
| -0.0100 0.0360 s {M5.G 25k (3 seeds)} | |
| -0.0060 0.0300 s {} | |
| -0.0080 0.0330 s {} | |
| -0.0140 0.0500 r2 {M5.G 25k, round 2} | |
| -0.0090 -0.0060 s {augmented student} | |
| -0.0170 0.0140 v {LR 3e-5} | |
| -0.0390 0.0200 v {2,000 steps (2 seeds)} | |
| -0.0410 0.0190 v {} | |
| 0.0100 0.0110 b {B average (2 seeds)} | |
| 0.0030 0.0070 b {} | |
| -0.0080 0.0090 b {B 25k} | |
| -0.0500 -0.0160 bv {B, 2,000 steps (2 seeds)} | |
| -0.0360 -0.0170 bv {} | |
| 0.0300 0.0300 s {100k} | |