Dogs and Cat Classifier in Real Time

Classifier Banner

🐾 Problem Statement

In many contexts—such as animal shelters, smart surveillance systems, or educational tools—automatically identifying whether an animal is a cat or a dog from images or a live webcam feed can be both useful and challenging. Manually labeling images is inefficient and prone to human error, especially when working with large datasets or real-time video.

💡 Solution

To address this, we built a real-time image classification system capable of distinguishing between dogs and cats using Convolutional Neural Networks (CNNs) and TensorFlow. The system processes input either from static images or a live webcam stream, and instantly classifies the detected object as either a dog or a cat.

Using Python and TensorFlow, we trained a CNN on a dataset of labeled dog and cat images. The model extracts and learns spatial hierarchies of features from the input images, improving its accuracy in distinguishing subtle differences between the two classes.

Once trained, the model was exported and integrated into a lightweight web application using JavaScript, HTML, and CSS, allowing users to upload images or use their webcam directly from a browser. This frontend communicates with the model via TensorFlow.js or a backend Python API, enabling predictions in real time.

⚙️ Technologies Used

Python – for model training, preprocessing, and export
TensorFlow – for building and training the CNN
CNN (Convolutional Neural Networks) – core architecture for image classification
HTML + CSS – for building the web interface
JavaScript – for handling frontend interactivity and webcam integration

🚀 Highlights

Real-time classification using a webcam
Lightweight and responsive frontend UI
Custom-trained CNN model with TensorFlow
End-to-end integration of machine learning with a web interface

📝 Notes

This project demonstrates how machine learning models can be deployed and used in interactive environments, bridging the gap between AI research and user-friendly applications. It also showcases practical use of TensorFlow and Python for real-time inference on the web.