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
- Xet hash:
- 29c075c4503d99041dbbb644fd471ac3947dc9e5a3ee584a0bfe686e6e07f573
- Size of remote file:
- 283 MB
- SHA256:
- 7869c80391b271eaa141e8da3daf656e26573ca96ded63232062128c1647e2b1
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