Project Overview
This project demonstrates how I built an image classification model to distinguish between cats and dogs using TensorFlow, Keras, and Streamlit.
The workflow covers the entire deep learning lifecycle:
Data preprocessing with ImageDataGenerator
Model building using Convolutional Neural Networks (CNNs)
Training & evaluation on filtered Cats vs Dogs dataset
Saving the model & label mapping for reproducibility
Deployment as an interactive Streamlit app where users upload an image and instantly get predictions
Key Features
✅ End-to-end Deep Learning pipeline with TensorFlow
✅ Custom CNN trained on Cats vs Dogs dataset
✅ Model + class mapping stored (cnn_model.h5 + class_indices.json)
✅ Interactive web app built with Streamlit
✅ Real-time predictions with confidence scores
✅ SEO-ready write-up for portfolio visibility
Tech Stack
Outcomes
The trained model achieves solid performance in classifying cats vs dogs and can be extended to other binary/multi-class image classification tasks.
This project shows my ability to:
Work with real-world datasets
Implement CNN architectures
Train, evaluate, and fine-tune models
Build practical AI apps for end-users
Live Demo & Code
