Classifying my chickens using YOLOv5
Image credit:
As a personal introduction to machine learning, this project involved building a system to identify each of my four chickens—Pi, Omega, Omikron, and Theta—using the YOLO (You Only Look Once) object detection model. My goal was to gain hands-on experience with machine learning concepts by creating a practical tool for identifying individual animals in my backyard flock.
I trained the YOLO model on a custom dataset of about 100 images, all of which I captured and labeled myself. Each image includes a variety of angles and settings to help the model distinguish between the chickens with high accuracy. One of the main challenges was differentiating between Omega and Omikron, as I typically identify them by behavior rather than appearance. Surprisingly, YOLO was able to identify each chicken correctly in still images by picking up on subtle visual differences that even I might miss at a glance.
Through this project, I gained foundational skills in dataset creation, labeling, and model deployment, as well as a deeper understanding of how machine learning can be applied in creative ways—even in a backyard setting.
