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:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://Udit013/brain-tumor-efficientnetb3") - Notebooks
- Google Colab
- Kaggle
EfficientNetB3 Brain Tumor MRI Classifier
4-class T1-weighted brain MRI classifier (glioma, meningioma, notumor, pituitary).
Reproduces the best model from an IEEE 2024 paper
(DOI: 10.1109/ICC-ROBINS60238.2024.10533941) and extends it with production
evaluation. Input: 256x256x3 RGB, raw [0,255] pixels (EfficientNet rescales
internally). Files: efficientnetb3.onnx (serving), EfficientNetB3_model_weights.h5
(Grad-CAM), temperature.json (calibration T).
Not a medical device — research/education only; dataset has documented train/test leakage. Full evaluation + code: https://github.com/Udit013/Brain_Tumor_Classificatioin
- Downloads last month
- -