We’re using AI to detect cats! This site is currently in its first of three phases. In this first phase, the AI model detects when a cat (or cats) is in view of a camera that is powered by a Raspberry Pi. A record of each detection is stored in a database in the Oracle Cloud Infrastructure. Those detections are reported in the graph above. Even fancier graphs with more detail will be coming soon!

This project currently uses YOLO (You Only Look Once), a machine learning algorithm designed for object detection in images and real-time video by using features learned by a deep convolutional neural network (CNN). Unlike traditional object detection algorithms that use a two-step process of first proposing regions and then classifying them, YOLO performs both tasks simultaneously in a single pass. It divides the input image into a grid and predicts bounding boxes, class probabilities, and confidence scores for each grid cell. YOLO is known for its speed and efficiency, making it particularly suitable for real-time applications. The algorithm can detect multiple objects in an image and is robust against overlapping instances.