Vertika-1312/dog_vs_cat_classifier

🐶🐱 Dog vs Cat Image Classifier

📌 Overview

This repository contains a Convolutional Neural Network (CNN) developed using TensorFlow/Keras for binary image classification. The model classifies input images as either Dog or Cat.

This project was created as part of my deep learning portfolio to demonstrate CNN design, model training, evaluation, and deployment practices.

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🧠 Model Details

• Framework: TensorFlow / Keras
• Architecture: Convolutional Neural Network (CNN)
• Task: Binary Image Classification
• Classes: Dog, Cat
• Input Size: 256 × 256 × 3
• Epochs: 10
• Validation Accuracy: ~95–96%

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📊 Training

The model was trained on the Kaggle Dogs vs Cats dataset.

Training included:

• Image preprocessing
• CNN feature extraction
• Binary classification using a sigmoid output layer
• Model evaluation using accuracy and loss metrics

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🚀 Usage

from tensorflow.keras.models import load_model

model = load_model("cnn_model.keras")

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📈 Results

• Validation Accuracy: 95–96%
• Binary Classification
• TensorFlow/Keras Implementation

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📚 Future Improvements

• Transfer Learning (EfficientNet / ResNet50)
• Data Augmentation
• Hyperparameter Tuning
• Grad-CAM Visualization
• Streamlit Deployment

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👨‍💻 Author

Vertika

GitHub: https://github.com/vertika13122007-tech