Deep learning has transformed many aspects of computer vision, including object detection, enabling accurate and efficient identification of objects in images and videos. However, choosing the right deep neural network-based object detector for your project, particularly when deploying on lightweight hardware, requires consideration of trade-offs between accuracy, speed and computational efficiency. In this talk we introduce the fundamental types of DNN-based object detectors. We’ll cover models such as Faster R-CNN for high-accuracy applications and single-stage models such as YOLO and SSD for faster processing. We will discuss lightweight architectures, including MobileNet, EfficientDet and vision transformers, which optimize object detection for resource-constrained environments. By the end of this talk, you will understand the trade-offs between object detection models for your computer vision applications, enabling informed choices for optimal performance and deployment.