Instructions to use Ken2707/Breast-Cancer-Detect with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use Ken2707/Breast-Cancer-Detect with Keras:
# !pip install -U keras tensorflow huggingface_hub # Keras needs TensorFlow installed to read "hf://" paths, so the tensorflow backend is selected here; # "jax" and "torch" also work for computation once TensorFlow is installed. import os os.environ["KERAS_BACKEND"] = "tensorflow" import keras model = keras.saving.load_model("hf://Ken2707/Breast-Cancer-Detect") - Notebooks
- Google Colab
- Kaggle
Upload 4 files
Browse files
.gitattributes
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DenseNet121/Overlay/DenseNet121_approach2.keras filter=lfs diff=lfs merge=lfs -text
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EfficientNet-B7/Original/EfficientNet_approach1.keras filter=lfs diff=lfs merge=lfs -text
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EfficientNet-B7/Overlay/EfficientNet_approach2.keras filter=lfs diff=lfs merge=lfs -text
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DenseNet121/Overlay/DenseNet121_approach2.keras filter=lfs diff=lfs merge=lfs -text
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EfficientNet-B7/Original/EfficientNet_approach1.keras filter=lfs diff=lfs merge=lfs -text
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EfficientNet-B7/Overlay/EfficientNet_approach2.keras filter=lfs diff=lfs merge=lfs -text
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ResNet50/Overlay/ResNet50_Overlay_TrainingHistory.png filter=lfs diff=lfs merge=lfs -text
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ResNet50/Overlay/ResNet50_Overlay_ConfusionMatrix.png
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ResNet50/Overlay/ResNet50_Overlay_TrainingHistory.png
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ResNet50/Overlay/ResNet50_approach2.pth
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ResNet50/Overlay/ResNet50_overlay_results.json
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{
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"model": "ResNet50",
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"scenario": "overlay",
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"timestamp": "2026-03-14 20:35:35",
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"metrics": {
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"f1-score": 0.9774436090225563,
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"malignant": {
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"precision": 0.9666666666666667,
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}
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