https://yyn-anson.github.io/tags/web-development/Web DevelopmentHugo Blox Builder (https://hugoblox.com)en-usSun, 01 Oct 2023 00:00:00 +0000https://yyn-anson.github.io/media/icon_hu7729264130191091259.pnghttps://yyn-anson.github.io/tags/web-development/https://yyn-anson.github.io/project/quick-draw/Sun, 01 Oct 2023 00:00:00 +0000https://yyn-anson.github.io/project/quick-draw/<h1 id="quick-draw">Quick Draw</h1> <p><em>1 Oct 2023 – 25 Nov 2023</em></p> <h2 id="project-overview">Project Overview</h2> <p>The Quick Draw project is a collaborative initiative developed during the MAIS202 Bootcamp. The project focuses on implementing a machine learning model capable of predicting animal labels based on user-uploaded images. Leveraging the Google Quick Draw dataset, the application accurately classifies images into 10 distinct animal categories.</p> <h2 id="demo">Demo</h2> <iframe src="https://1drv.ms/v/c/891814256f52dec4/IQTE3lJvJRQYIICJpxQAAAAAAR4Qr2RjQvGbjIFmN2lFUBo" width="640" height="360" frameborder="0" scrolling="no" allowfullscreen> </iframe> <h2 id="key-features">Key Features</h2> <ul> <li><strong>Real-Time Prediction</strong>: Upload an image and receive immediate, accurate predictions of the animal depicted.</li> <li><strong>High Accuracy</strong>: Achieves approximately 83% accuracy across 10 animal classes.</li> <li><strong>Collaborative Development</strong>: Developed by a two-member team, integrating strong model development with full-stack web application expertise.</li> </ul> <h2 id="technical-approach">Technical Approach</h2> <ul> <li><strong>Model Development</strong>: Utilized CNN architecture, training on the Quick Draw dataset to optimize performance.</li> <li><strong>Web Integration</strong>: Engineered a responsive and user-friendly web interface that seamlessly integrates with the machine learning model for real-time predictions.</li> <li><strong>Optimization</strong>: Fine-tuned both the model and web application to ensure efficient and smooth operation under live conditions.</li> </ul> <h2 id="contributions">Contributions</h2> <ul> <li><strong>Machine Learning</strong>: Contributed 50% towards model development, ensuring robust performance and accuracy.</li> <li><strong>Web Application</strong>: Took full responsibility for designing and implementing the web app, from UI/UX design to backend integration.</li> </ul>