This project demonstrates an image classification system that distinguishes between dog and cat images using transfer learning. Instead of training a deep learning model from scratch, a pre trained convolutional neural network is used to extract meaningful visual features from images.
The project highlights how transfer learning can significantly reduce training time while achieving strong classification performance on real world image data.
Project Objective
The objective of this project is to build an accurate and efficient binary image classification system. By leveraging a pre trained deep learning model, the project aims to classify dog and cat images with high reliability while minimizing computational cost.
This project also showcases practical application of deep learning in computer vision tasks.
What This Project Does
The system processes input images and learns distinguishing visual features such as shape texture and spatial patterns. Based on these features the model predicts whether an image contains a dog or a cat.
The workflow covers image preprocessing model training evaluation and real time prediction.
Dataset Description
The dataset consists of labeled images of dogs and cats organized into class specific directories. Images vary in resolution background and orientation which introduces real world complexity into the classification task.
Transfer Learning Approach
A pre trained convolutional neural network trained on the ImageNet dataset is used as a feature extractor. The convolutional base is frozen to preserve learned representations while custom dense layers are added for classification.
This approach allows the model to learn faster and perform better with limited training data.
Model Architecture
The architecture includes the frozen convolutional base followed by fully connected layers. Nonlinear activation functions are used to capture complex patterns that differentiate dog and cat images.
Binary classification is achieved using a sigmoid activated output layer.
Training and Evaluation
The model is trained using a training dataset with validation monitoring to prevent overfitting. Performance is evaluated on unseen images to measure generalization accuracy.
Training results demonstrate stable learning and effective feature transfer.
Key Highlights
- Binary image classification using deep learning
- Transfer learning with a pre trained CNN
- Efficient image preprocessing pipeline
- Reduced training time with strong accuracy
- End to end deep learning workflow
Technologies Used
Python for implementation
TensorFlow and Keras for deep learning modeling
NumPy for numerical operations
Image data generators for efficient loading
Matplotlib for result visualization
Use Case and Applications
This project demonstrates a common real world computer vision use case. Similar systems are used in animal recognition content moderation image filtering and visual search applications.
The approach can be extended to multi class image classification problems.