Image Classification
timm
ONNX
PyTorch
English
medical
histopathology
cancer-detection
binary-classification
efficientnet
int8
Eval Results (legacy)
Instructions to use AegisOSS/stage-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use AegisOSS/stage-1 with timm:
import timm model = timm.create_model("hf_hub:AegisOSS/stage-1", pretrained=True) - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 77b2d718ecd4f023ba86dbf74601534af31d3e1e33f333d9d53df09b4d320d14
- Size of remote file:
- 16 MB
- SHA256:
- 0683b498a93e237bbc98d609a13e6b0f18fd95039bcb88f71c5f8ee31f4dbe46
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