---
license: mit
language:
- en
metrics:
- accuracy
new_version: CernovaAI/CANetv1.2
pipeline_tag: image-classification
tags:
- medi
- code
base_model:
- CernovaAI/CANet-v1.1
- CernovaAI/CANet-v1
---




# 🖼️ Image Classifier with TensorFlow & Keras

This project demonstrates a **Convolutional Neural Network (CNN)** built with TensorFlow and Keras for **image classification**. The model is designed to learn from labeled datasets and classify unseen images with high accuracy.

## 🚀 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

```bash
python main.py
```

### 2️⃣ Run Predictions

```python
guess("test_image.jpg", model, train_generator.class_indices)
```

The predicted class will be displayed on the image itself.

## 🎯 Conclusion

This project provides a **versatile CNN-based image classifier** that can be applied to a wide range of domains—from **medical imaging to natural scene recognition**. By integrating your own dataset, you can easily adapt this model to your specific use case.