Update README.md

README.md

@@ -1,3 +1,48 @@
 ---
 license: apache-2.0
+datasets:
+- benschill/brain-tumor-collection
+metrics:
+- accuracy
+pipeline_tag: image-classification
 ---
+
+# Brain Tumor Classification Model
+
+## Overview
+This repository contains a deep learning model for brain tumor classification using Hugging Face Transformers. The model has been trained on a brain tumor dataset consisting of 5712 training samples and validated on 1311 samples. It is designed to classify brain tumor images into four classes: 'glioma', 'meningioma', 'notumor', and 'pituitary'.
+
+## Model Details
+- **Framework**: Hugging Face Transformers
+- **Dataset**: Brain Tumor Dataset
+- **Training Data**: 5712 samples
+- **Validation Data**: 1311 samples
+- **Input Shape**: 130x130 pixels with 3 channels (RGB)
+- **Data Preprocessing**: Data is normalized
+- **Validation Accuracy**: 72%
+
+## Classes
+The model classifies brain tumor images into the following classes:
+- 'glioma' (Class 0)
+- 'meningioma' (Class 1)
+- 'notumor' (Class 2)
+- 'pituitary' (Class 3)
+
+## Usage
+You can use this model for brain tumor classification tasks. Here's an example of how to load and use the model for predictions in Python:
+
+```python
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+import tensorflow as tf
+import numpy as np
+
+# Load the pre-trained model
+model_name = "model/brain_tumor_model."  # Replace with the actual model name
+model = tf.keras.models.load_model(model_name)
+
+# to get prediction
+x = numpy array image
+pred = np.argmax(model.predict(x),axis=-1)
+
+# class label
+class_labels = {0: 'glioma', 1: 'meningioma', 2: 'notumor', 3: 'pituitary'}