Dog_vs_Cat_Classification(Transfer Learning)

Project Title: Dog vs Cat Classification with MobileNetV2 Transfer Learning

Description:

This project implements a deep learning model for classifying images of dogs and cats using transfer learning. It leverages the pre-trained MobileNet V2 model for image feature extraction and fine-tunes it on a dedicated dog and cat dataset.

Libraries and Technologies:

Python: As the primary programming language. TensorFlow/Keras: For building and training the deep learning model. NumPy: For numerical computations and data manipulation. Matplotlib/Seaborn: For data visualization. OpenCV: For image processing tasks.

Algorithm:

Transfer Learning: The project employs the MobileNet V2 model, which is pre-trained on a massive image dataset (ImageNet), and fine-tunes it on the dog and cat dataset. This approach capitalizes on the pre-trained features for efficient and accurate classification.

Results:

Test Loss = 0.0812455490231514

Test Accuracy = 0.9775000214576721

potential enhancements

1. Using data augmentation techniques for increased dataset diversity.

2. Implementing hyperparameter tuning for optimization.