Image Classification
Keras
TF-Keras
English
mobilevit
tensorflow
computer-vision
medical-imaging
brain-tumor
Eval Results (legacy)
Instructions to use abdo1176/brain-model-test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use abdo1176/brain-model-test with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://abdo1176/brain-model-test") - Notebooks
- Google Colab
- Kaggle
MobileViT Brain Tumor Classifier
This MobileViT model classifies brain MRI scans into:
- Healthy
- Tumor
Accuracy: 98.5%
⚠️ Note: For research/educational purposes only. Not for clinical use.
Model Files
model.keras: Native Keras format (recommended)model.h5: Legacy H5 formatsaved_model/: TensorFlow SavedModel formatmodel.weights.h5: Model weights onlymodel_config.json: Model architecture configurationclass_names.json: Class label mappings
Usage
import tensorflow as tf
from huggingface_hub import hf_hub_download
# Download and load model
model_path = hf_hub_download(repo_id="abdo1176/brain-model-test", filename="model.keras")
model = tf.keras.models.load_model(model_path)
# Or load weights only
weights_path = hf_hub_download(repo_id="abdo1176/brain-model-test", filename="model.weights.h5")
# model.load_weights(weights_path)
- Downloads last month
- 5
Evaluation results
- Accuracy on Brain Tumor MRI Imagesself-reported0.985