A Overview This project implements a machine learning model designed to recognize and classify dog images. The model utilizes transfer learning techniques, leveraging pre-trained weights from existing sources and fine-tuning them for our specific use case. Features

Accurately identifies whether a given image contains a dog Uses transfer learning to improve efficiency and accuracy Based on a robust deep learning architecture

Extensibility While the current implementation focuses on dog classification, the model's architecture allows for easy extension to other animal categories, such as cats. Additional classification capabilities can be added with minimal code modifications. Technical Details

Utilizes pre-trained weights from established computer vision models Fine-tuned on a custom dataset of dog images for improved accuracy Implemented using FAST.ai freamwork

Future Improvements

Extend classification to include other animals (e.g., cats) Implement multi-class classification for different dog breeds Enhance the model's performance through further fine-tuning and data augmentation

Note The current version is optimized for dog image classification. While the model architecture supports multi-class classification, additional code and training would be required to extend its capabilities to other animals or objects. hu bhai T_