Yolo is a deep learning algorythm for object detcetion. With yolo we can detect objects at a relatively high speed. With a GPU we would be able to process over 45 frames/second while with a CPU around a frame per second.
3 most used and known frameworks compatible with YOLO and the advantages and disadvantages of each one:
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Darknet : it’s the framework built from the developer of YOLO and made specifically for yolo. Advantage: it’s fast, it can work with GPU or CPU Disadvantage: it olny works with Linux os
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Darkflow: it’s the adaptation of darknet to Tensorflow (another deep leanring framework). Advantage: it’s fast, it can work with GPU or CPU, and it’s also compatible with Linux, Windows and Mac. Disadvantage: the installation it’s really complex, especially on windows
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Opencv: also opencv has a deep learning framework that works with YOLO. Just make sure you have opencv 3.4.2 at least. Advantage: it works without needing to install anything except opencv. Disadvantage: it only works with CPU, so you can’t get really high speed to process videos in real time.
- Weight file: it’s the trained model, the core of the algorythm to detect the objects.
- Cfg file: it’s the configuration file, where there are all the settings of the algorythm.
- Name files: contains the name of the objects that the algorythm can detect.
For weights and cfg https://hackmd.io/NFj2NrmqTcefjc2l94KjpQ