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README.md

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 base_model:
 - CernovaAI/CANet-v1.1
 - CernovaAI/CANet-v1
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+---
+
+
+
+
+# 🖼️ 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.
+
+