Worms and Eggs Detection Using YOLOv7

Clients

Techstack

Purpose

Counting worms and eggs under a microscope by hand is tedious and inconsistent, and the tallies drift as fatigue sets in. The project set out to automate that identification and counting so researchers get consistent results from every sample.

Description

We trained a deep-learning detection model to find and label worms and eggs in captured images, drawing a marked box around each one so specimens can be identified and counted automatically. The image dataset was carefully annotated and expanded to train the model for reliable detection across varied samples. It gives researchers a hands-off way to monitor specimens for agricultural and diagnostic work — the kind of detection pipeline we build and ship.