In an earlier post, we saw how to use a pre-trained YOLO model with OpenCV and Python to detect objects present in an image. (also known as running 'inference')
As the word 'pre-trained' implies, the network has already been trained with a dataset containing a certain number of classes (object categories). For example, the model we used in the previous post was trained on the COCO dataset which contains images with 80 different object categories.
If you have been keeping up with the advancements in the area of object detection, you might have got used to hearing this word ‘YOLO’. It has kind of become a buzzword.
YOLO (You Only Look Once) is a method / way to do object detection. It is the algorithm /strategy behind how the code is going to detect objects in the image.
If you are into any sort of image processing, computer vision or machine learning, chances are high that you might have come across/used dlib somewhere in your journey.
According to dlib’s github page, dlib is a toolkit for making real world machine learning and data analysis applications in C++. While the library is originally written in C++, it has good, easy to use Python bindings.
This is from a long time Ubuntu user who recently picked up a MacBook Air for all the portability it has to offer.
I am completely new to macOS. I had to look around and experiment to install all the packages I was enjoying on Ubuntu. I decided to put together a simple setup guide to get the deep learning environment (particularly for Computer Vision) up and running on macOS the easy way.
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