Instructions to use Udit013/brain-tumor-efficientnetb3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use Udit013/brain-tumor-efficientnetb3 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://Udit013/brain-tumor-efficientnetb3") - Notebooks
- Google Colab
- Kaggle
Commit History
14 days ago
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Recalibrate temperature on CPU outputs (T 0.819 -> 0.561; ECE 0.127 -> 0.073)
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Upload temperature.json with huggingface_hub
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Upload EfficientNetB3_model_weights.h5 with huggingface_hub
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Upload efficientnetb3.onnx with huggingface_hub
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Upload README.md with huggingface_hub
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initial commit
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