Object Detection
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There are a few different algorithms for object detection and they can be split into two groups:
Algorithms based on classification–they work in two stages. In the first step, we’re selecting from the image interesting regions. Then we’re classifying those regions using convolutional neural networks. This solution could be very slow because we have to run prediction for every selected region. Most known example of this type of algorithms is the Region-based convolutional neural network (RCNN) and their cousins Fast-RCNN and Faster-RCNN.
Algorithms based on regression–instead of selecting interesting parts of an image, we’re predicting classes and bounding boxes for the whole image in one run of the algorithm. Most known example of this type of algorithms is YOLO (You only look once) commonly used for real-time object detection.
YOLO metrics:
Pupil Localization
megabyte model to predict 2D eye, eyebrow and iris geometry from monocular video captured by a front-facing camera on a smartphone in real time.
: YOLO is based on regression not classification
: show text results