Face Detection in Python using YOLO: A Practical Guide
Source: tai_blog

This tutorial introduces you to YOLO  one of the most powerful and efficient object detection algorithms in Computer Vision.   Youll learn how to leverage YOLO in Python for face detection with just a few lines of code.   Whether youre new to Computer Vision or looking to expand your knowledge  this guide provides a hands-on approach to mastering one of the industrys leading tools.   Before diving into this tutorial  I recommend checking out my LinkedIn and Medium profiles  where I often discuss these topics. Ive written about Computer Vision in these two articles: A Gentle Introduction to Computer Vision and Unlock the Power of Computer Vision using Python: 7 Essential OpenCV Features You Need to Know.   I am planning to start a Substack project. So if youre interested  please consider signing up for my profile. I would be very grateful U+1F642   Face Detection: an Object Detection taskWe can define Object Detection as the recognition of one or more objects within an image. In the case of Face Detection  as you can easily imagine  the object the algorithm will try to recognize is one or more faces.   There are many algorithms to perform these tasks  some more heuristic and others more intelligent. The algorithm Im discussing today is part of a very famous and important CV model called YOLO.   YOLO and Computer Vision in PythonYoure probably already familiar with this name. Its one of the most well-known models in the world  and youve likely heard about it even if you havent worked directly in CV.   YOLO  which stands for You Only Look Once  is an Object Detection algorithm used in Machine Learning. The goal of YOLO is to identify and classify objects within an image in real-time.   Over time  it has also been trained for other tasks  such as Image Classification and Image Segmentation. For example  its used to recognize the emotion on a person's face (in IC) or to cut out a photo (in IS).   The main characteristic of YOLO is that it performs object detection in a single pass over the image  unlike other algorithms that require multiple passes. This makes it extremely efficient and fast  which is the reason behind its name.   YOLO divides the image into a grid of cells  and for each cell  it predicts the bounding boxes of the objects present  along with the probability of belonging to a class. This process is carried out simultaneously for all the cells in the image.   Lets see how we can use YOLO for Face Detection in Python.   Face Detection in Python using YOLOFirst  we need to install YOLO. We can use the Ultralytics package  which provides a very convenient Python interface to the model. You can find all the information about this package here.   So first  let's install the package:   pip install ultralyticsNext  you need to download the model file available at this link. The model is called yolov8m-face.pt and is approximately 49 megabytes in size.   Lets take a moment to talk about the models name:   V8: This is the 8th version of the algorithm. If you want more information about the versions  you can find them here.m: The m stands for medium. With YOLO  you typically have 5 model sizes: nano (n)  small (s)  medium (m)  large (l)  and extra (x).face: The algorithm is a Face Detection model  so face stands for the object the algorithm identifies.We can use a single line or two lines of code for Face Detection in Python using YOLO.   The image we will use to test the algorithm is as follows:   Before we begin  make sure you have downloaded the model and placed it in your working directory.   Face Detection in Python using YOLO: 1 LineIn Google Colab or any Notebook (or in a Terminal)  simply run this code in a cell:   !yolo task=detect mode=predict model=yolov8m-face.pt conf=0.25 source='https://ultralytics.com/images/zidane.jpg'Remember to download the model and place it in your working directory.   Face Detection in Python using YOLO: 2 LinesRunning it from the command line  as in the previous case  is convenient  but it is less manageable in a Python program if we then want to use the model in some way.   The two-line version  instead  involves loading the model and then running the model on the image:   from ultralytics import YOLO model = YOLO('yolov8m-face.pt') results = model('https://ultralytics.com/images/zidane.jpg')If we dont consider the import  its just two lines of code!   YOLO OutputYOLO constructs a very specific path for saving the output. You will find this folder structure:   runs -> detect -> predict -> zidane.jpgThis will be the YOLO output:   ConclusionsIn this tutorial  I have shown you how you can apply a Face Detection algorithm in Python using YOLO  the most important Computer Vision model in existence.   You can find more similar content on my social channels  especially LinkedIn and Medium.   If you enjoyed this article  please help spread the word about the blog! I would be very grateful U+1F642