Project Details
This project focuses on solving a real-world problem using modern technology. It streamlines and improves processes, delivering better user experiences and efficiency. The solution highlights practical use of the tech stack, with potential for future upgrades and broader application.
Technologies Used
Live Demo/Github
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Image classification model
I developed an image classification model to predict if an uploaded image is of a dog or cat. The model was built using CNNs and deployed with a Flask web application for real-time predictions. I tackled overfitting issues by using data augmentation and regularization techniques to improve accuracy.
- How to prevent overfitting with regularization and data augmentation.
- Improved image preprocessing and resizing for better model input.
- Enhanced my understanding of CNNs and how to tune hyperparameters to optimize mo
Key Learnings from the Project
Future Scope and Enhancements
In the future, I plan to expand the model by training on a larger, more diverse dataset, which will help improve its accuracy and robustness in identifying different breeds or animals. Additionally, incorporating transfer learning could speed up the training process and improve predictions with limited data.
Another improvement could be integrating the app with cloud services like AWS or GCP to handle more complex image data and scale the app for real-time predictions across multiple users. This would also enable deploying the model on a wider range of platforms.