  This paper focuses on YOLO-LITE, a real-time object detection model developed
to run on portable devices such as a laptop or cellphone lacking a Graphics
Processing Unit (GPU). The model was first trained on the PASCAL VOC dataset
then on the COCO dataset, achieving a mAP of 33.81% and 12.26% respectively.
YOLO-LITE runs at about 21 FPS on a non-GPU computer and 10 FPS after
implemented onto a website with only 7 layers and 482 million FLOPS. This speed
is 3.8x faster than the fastest state of art model, SSD MobilenetvI. Based on
the original object detection algorithm YOLOV2, YOLO- LITE was designed to
create a smaller, faster, and more efficient model increasing the accessibility
of real-time object detection to a variety of devices.
