Yolov3 Object Detection Tutorial #2 - Training Part 1 | OpenCV Python | Computer Vision 2020

Yolo v3 Tutorial #2 - Object Detection Training Part 1 - Create a Supervisely Cluster. In this Computer Vision tutorial series we use OpenCV and Python: ⭐6-in-1 AI MEGA Course - https://augmentedstartups.info/AugmentedAICVPRO ►YOLOV4 Course - https://augmentedstartups.info/yolov4release

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Hey guys and welcome back, so in this lecture, we are going to setup up our deep learning capable PC as a cluster for Supervisely.

Now we are using Ubuntu 16.4 because it is required by Supervisely, otherwise we would be using windows 10. But what is nice about this setup is that you can access this PC train your CNN models from the otherside of the planet if you really wanted to. Ideally you would want your cluster to be a server rather than your desktop PC, but hey if you have to train, you have to train.

So my PC that I am using, has an NVidia 1080 GTX graphics card and I freshly partitioned my hard drive alongside windows. We won’t cover the installation of Ubuntu 16.04 in this series as there are tons of free tuts on how to do this. Make sure when that you Ubuntu partition has tons of space because Supervise.ly creates and saves weight checkpoints at every epoch which can easily consume around 20 to 30 gigs of space. I made my partition around 200Gb to allow for enough training epochs.

The whole process of setting up a cluster is quite laborious and involves a lot of copy-and-pasting of command lines but once you set it up, its done for good, unless you ran out of space like I did and had to set it up twice on two computers. So,ya! without further A due, let’s get started with creating a Supervisely cluster:

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