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Vertika-1312/
dog_vs_cat_classifier

Name: dog_vs_cat_classifier
Availability: InStock

binary-classification

mit

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🐶🐱 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.

🧠 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%

📊 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

🚀 Usage

python

from tensorflow.keras.models import load_model

model = load_model("cnn_model.keras")

📈 Results

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

📚 Future Improvements

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

👨‍💻 Author

Vertika

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

Vertika-1312/dog_vs_cat_classifier