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---
language:
- en
license: apache-2.0
tags:
- tensorflow
- keras
- computer-vision
- medical-imaging
- brain-tumor
- mobilevit
- image-classification
datasets:
- brain-tumor-mri
metrics:
- accuracy
model-index:
- name: MobileViT Brain Tumor Classifier
results:
- task:
type: image-classification
name: Brain Tumor Classification
dataset:
type: brain-tumor-mri
name: Brain Tumor MRI Images
metrics:
- type: accuracy
value: 0.9850
name: Accuracy
---
# 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 format
- `saved_model/`: TensorFlow SavedModel format
- `model.weights.h5`: Model weights only
- `model_config.json`: Model architecture configuration
- `class_names.json`: Class label mappings
## Usage
```python
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)
```
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