Object recognition is a tool that is often used in today's digital era. Object recognition can identify an object. However, we cannot identify every single object unless the object has been tagged and studied by the machine. Our goal in this research is to create a program that can detect bottles with the YOLOv3 and COCO datasets and a simple architectural model that can be easily practiced. In this research, we will use YOLO, and the dataset is taken, or the objects that can be identified are only objects in the COCO dataset. Then we do object recognition of the bottles that we collect ourselves as a real case data test. We found that YOLOv3 is better at detecting objects than YOLOv2 with the same dataset.