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
File size: 669 Bytes
f0fde20 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 | {
"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"
]
}
} |