The only thing you need to know is that Yolo is lightweight and fast and decently accurate. I will have to skip the technical detail here because the paper explained everything. Again, what does it mean, is that Yolo3 now does a better job predicting small objects than its previous version! Don't let the technical term scare you away! What does this implies is that YoloV3 does not care about the input image size anymore! As long as the height and width are integer times 32 (such as 224x224, 288x288, 608x288, etc), YoloV3 will work fine! Another major improvement of YoloV3 is that it gives predictions in the intermediate layers as well. The biggest change is that YoloV3 now uses only convolutional layers and no more fully-connected layer. YoloV3 is one of the latest updates of Yolo algorithm. Yolo, standing for "You Only Look Once" (not You Only Live Once), smartly avoids those heavy computations by directly predicting object category and their bounding boxes simultaneously. This method is of course very time consuming because there are many different ways to place the window, and many computations need to be done repeatedly.
INSTALL PTHREAD C RASPBERRY PI WINDOWS
Traditionally people used moving windows to scan an image, and then try to recognize each snapshot in every possible window locations. So, what is Yolo? Yolo is a cutting-edge object detection algorithm, i.e., it detects objects from images. BTW, their recent "paper" ( Yolo v3: an incremental Improvement) is an interesting read as well.
INSTALL PTHREAD C RASPBERRY PI SERIES
Redmon & Farhadi's famous Yolo series work had big impacts on the deep learning society.
I am a big fan of Yolo (You Only Look Once, Yolo website). I have modified the Makefile, added the two Python nonblocking wrapper, and made some other minor modification. Having been really busy in the last several months, I finally found some spare time completing this blog with detailed instruction! All the information can be found in my GitHub repos which was forked from shizukachan/darknet-nnpack. Since I posted this article late Aug, I have been inquired many times on the detailed instruction and also the python wrapper.
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