Image Classification
timm
ONNX
PyTorch
English
medical
histopathology
cancer-classification
oncotree
resnet50
int8
Eval Results (legacy)
Instructions to use AegisOSS/stage-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use AegisOSS/stage-2 with timm:
import timm model = timm.create_model("hf_hub:AegisOSS/stage-2", pretrained=True) - Notebooks
- Google Colab
- Kaggle
| { | |
| "split": "test", | |
| "metrics": { | |
| "accuracy": 0.9953488372093023, | |
| "balanced_accuracy": 0.9935483870967742, | |
| "macro_f1": 0.9935483870967742, | |
| "weighted_f1": 0.9953488372093023 | |
| }, | |
| "data": { | |
| "total": 430, | |
| "per_subtype": { | |
| "CRC": 66, | |
| "BRCA": 172, | |
| "GB": 68, | |
| "LUAD": 62, | |
| "LUSC": 62 | |
| }, | |
| "per_dataset": { | |
| "lc25000": 190, | |
| "breast_histopathology": 74, | |
| "brain_tumor_mri_hemg": 68, | |
| "breast_histopathology_patches": 98 | |
| }, | |
| "num_classes_present": 5, | |
| "trainable_oncotree": [ | |
| "BRCA", | |
| "CRC", | |
| "GB", | |
| "LUAD", | |
| "LUSC" | |
| ] | |
| } | |
| } |