🖼️ Image Classifier with TensorFlow & Keras

🚀 Features

• CNN-based architecture: Efficient feature extraction using Conv2D and MaxPooling layers.
• Flexible dataset handling: Uses ImageDataGenerator with automatic train/validation split (90% training / 10% validation).
• Easy deployment: Trained model is saved in .h5 format for reuse.
• Prediction function: Quickly classify a single image with visualization support.
• Matplotlib integration: Displays the predicted class directly on the image.

📂 Project Structure

project/
│── dataset/               # Training & validation images
│── image_classifier.h5    # Saved trained model
│── main.py                # Model training & prediction script
│── README.md              # Project documentation

🧠 Model Architecture

• Conv2D (32 filters, 3x3) → ReLU
• MaxPooling2D (2x2)
• Conv2D (64 filters, 3x3) → ReLU
• MaxPooling2D (2x2)
• Conv2D (128 filters, 3x3) → ReLU
• MaxPooling2D (2x2)
• Flatten
• Dense (512 neurons, ReLU)
• Dense (number of classes, Softmax)

⚡ Usage

1️⃣ Train the Model

2️⃣ Run Predictions

🎯 Conclusion