Instructions to use yaraa11/brain-tumor-segmentation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use yaraa11/brain-tumor-segmentation with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://yaraa11/brain-tumor-segmentation") - Notebooks
- Google Colab
- Kaggle
Create config.json
Browse files- config.json +32 -0
config.json
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{
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"model_name": "Brain Tumor Segmentation (CNN)",
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"library_name": "keras",
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"model_type": "cnn-segmentation",
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"input_shape": [224, 224, 1],
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"output_shape": [224, 224, 1],
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"task": "image-segmentation",
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"language": "python",
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"framework": "tensorflow-keras",
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"threshold": 0.5,
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"loss": "binary_crossentropy",
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"metrics": [
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"accuracy",
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"precision",
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"recall",
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"dice_coef"
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],
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"trainable_parameters": 487009,
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"license": "mit",
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"tags": [
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"brain-tumor",
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"medical-imaging",
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"segmentation",
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"keras",
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"cnn",
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"tensorflow",
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"dice-loss",
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"binary-segmentation"
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],
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"repo_url": "https://huggingface.co/yaraa11/brain-tmor-segmentation",
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"description": "Convolutional Neural Network for brain tumor segmentation trained on the 2017 Brain Tumor Dataset by Jun Cheng."
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}
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