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
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# Aegis Stage 2 — OncoTree Subtype Classifier (ResNet-50)
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**Aegis Stage 2** is a high-performance deep learning model trained on **10,986 real histopathology tiles** across 8 mapped data sources to perform fine-grained cancer subtype categorization
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- **Architecture**: ResNet-50 (`timm`)
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- **Total Parameters**: 23,526,473 (~23.53 Million)
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# Aegis Stage 2 — OncoTree Subtype Classifier (ResNet-50)
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**Aegis Stage 2** is a high-performance deep learning model trained on **10,986 real histopathology tiles** across 8 mapped data sources to perform fine-grained cancer subtype categorization model.
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- **Architecture**: ResNet-50 (`timm`)
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- **Total Parameters**: 23,526,473 (~23.53 Million)
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