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
| { | |
| "model_name": "Brain Tumor Segmentation (CNN)", | |
| "library_name": "keras", | |
| "model_type": "cnn-segmentation", | |
| "input_shape": [224, 224, 1], | |
| "output_shape": [224, 224, 1], | |
| "task": "image-segmentation", | |
| "language": "python", | |
| "framework": "tensorflow-keras", | |
| "threshold": 0.5, | |
| "loss": "binary_crossentropy", | |
| "metrics": [ | |
| "accuracy", | |
| "precision", | |
| "recall", | |
| "dice_coef" | |
| ], | |
| "trainable_parameters": 487009, | |
| "license": "mit", | |
| "tags": [ | |
| "brain-tumor", | |
| "medical-imaging", | |
| "segmentation", | |
| "keras", | |
| "cnn", | |
| "tensorflow", | |
| "dice-loss", | |
| "binary-segmentation" | |
| ], | |
| "repo_url": "https://huggingface.co/yaraa11/brain-tmor-segmentation", | |
| "description": "Convolutional Neural Network for brain tumor segmentation trained on the 2017 Brain Tumor Dataset by Jun Cheng." | |
| } | |