Today, we’re diving into the story of Eric, a seasoned software engineer, whose journey through the MosquitoAlert Challenge is an example of the power of continuous learning and practical application in the realm of artificial intelligence. Eric’s unique approach to tackling machine learning challenges combines effective problem-solving with tangible, hands-on experience. This challenge has enhanced his expertise in areas such as algorithm development, data analysis, and model optimization. Let’s hear how he ranked third in the competition and dive deeper into his solutions.
Discovering the MosquitoAlert Challenge
It all started when Eric was looking for a new project to sink his teeth into during the summer and found the MosquitoAlert challenge. The detailed problem statement, coupled with an engaging townhall event featuring seasoned experts, sparked his curiosity.
In this challenge, Team MPWare led by Eric embarked on a two-pronged solution to efficiently detect and classify mosquitoes.
Stage 1: Tackling Mosquito Detection Using Yolo
The first stage was critical for the initial identification and localization of mosquitoes in images, employing a Yolo-based model to detect mosquito bounding boxes.
Stage 2: The Ensemble Approach for Mosquito Classification
In the second stage, the team utilized an ensemble of two distinct models for the classification task. One model was built on the Vision Transformer (ViT) architecture, renowned for its efficacy in image recognition tasks. The second model leveraged the EfficientNetv2 architecture, celebrated for its efficiency and accuracy in image classification.
To read the complete breakdown of MPWare’s solution read the blog over here