This repository contains a deep learning model that classifies animal images into one of 40 predefined classes. The model leverages transfer learning using the ResNet50 architecture, pretrained on ImageNet, and fine-tuned to adapt to the animal classification task. The model uses image preprocessing, data augmentation, and optimization techniques to improve accuracy and generalization.
The primary goal of this project was to create an efficient image classification model capable of identifying various animals based on their visual features. The project involved:
Preprocessing the dataset for consistency and augmentation, Leveraging transfer learning to use a pretrained ResNet50 model, Fine-tuning the model for the animal classification task.