What are the methods for object detection?

Object detection is a supervised machine learning problem , which means you must train your models on labeled examples. Each image in the training dataset must be accompanied with a file that includes the boundaries and classes of the objects it contains.

Popular algorithms used to perform object detection include convolutional neural networks (R-CNN, Region-Based Convolutional Neural Networks), Fast R-CNN, and YOLO (You Only Look Once) . The R-CNN’s are in the R-CNN family, while YOLO is part of the single-shot detector family.

Popular algorithms used to perform object detection include convolutional neural networks (R-CNN, Region-Based Convolutional Neural Networks), Fast R-CNN, and YOLO (You Only Look Once) . The R-CNN’s are in the R-CNN family, while YOLO is part of the single-shot detector family.

Object detection is a supervised machine learning problem , which means you must train your models on labeled examples. Each image in the training dataset must be accompanied with a file that includes the boundaries and classes of the objects it contains.