Hi,

This week you'll learn about A Better, Faster, and Stronger Object Detector (YOLOv2).

Image

History has continually told us that even after groundbreaking revolutions, growth cannot, has not stopped. Microsoft didn't stop after the release of the Windows 1.01 operating system. They kept on improving their work, and today, along with macOS and Ubuntu, Windows 10 is the most widely used operating system.

A screenshot of Windows 1.01

The world moves at a very fast pace. If a technological revolution like the Windows operating system didn't evolve along with the growing world, it would be nothing but a one-hit-wonder obsolete piece in history. 

The true visionaries never rest, with the most common example in fiction being Iron Man, who continually developed battle suits to fight his ever-growing and ever-evolving rogue gallery.

Iron Man's suits

With each iteration, Iron Man's suits became faster, more efficient, and loaded with much more functionalities. Similarly, today we will learn about YOLOv1's successor, the faster and more efficient YOLOv2.

The big picture: YOLOv1 was already a groundbreaking step in object detection. However, there were several areas where it couldn't deliver ideal performance. YOLOv2 was created not only to address these issues but also to make detections much faster and more efficient. 

How it works: With the introduction of YOLOv2, we saw the inclusion of a higher resolution classifier, anchor boxes, and batch normalization, to name a few. It also used the Darknet-19 architecture, notably known for fast real-time detections. 

Our thoughts: The inclusion of the concept of anchor boxes is something that played a huge role even in the subsequent YOLO architectures. It is important to understand the role anchor boxes play in the whole architecture. 

Yes, but: As we have already mentioned before, the growth never stops. Just when everyone thought that we had reached the peak of object detection, YOLOv3 came out, smashing all records out of the park. 

Stay smart: Neglecting YOLOv2 can lead to lapses in understanding in the subsequent YOLO versions. Thoroughly try and grasp what we have presented here. Stay tuned for the upcoming tutorials in this series. 

Click here to read the full tutorial

Need Code Right Now?

You can instantly access all of the code for A Better, Faster, and Stronger Object Detector (YOLOv2) by joining PyImageSearch University. Get working code to

  1. Finish your project this weekend with our code
  2. Excel at work immediately by solving difficult problems with our code
  3. Publish groundbreaking research without multiple tries at coding the hard parts

I want the code


The PyImageSearch Team

Image


P.S. You're ready to study deep learning and computer vision...but you don't know where to start.

What you need is a book that:
  1. Starts with the fundamentals and then builds up to advanced techniques
  2. Helps you learn how to successfully apply CV/DL to your own projects and datasets
  3. Provides an actual real person to support you and help you as you work through it and have questions
The good news is that book does exist — it's called Deep Learning for Computer Vision with Python.

Rishiraj Acharya, teacher at Stanford University, had this to say about the book:

Image
While Sean Mackenzie, long-time PyImageSearch reader and customer said the following after I helped him through a tricky project:

"Thank you for the incredible customer service. It's amazing to know you care so much about providing great service rather than just putting out content and getting people to buy packages. It's obvious you care about the success of each one of your customers. I appreciate it."

I'd love to be able to help you on your CV/DL journey, just like I've helped Rishiraj, Sean, and tens of thousands of other PyImageSearch readers.

If you'd like to follow in their footsteps, I'd be happy to send you a PDF containing the Table of Contents and a few sample chapters to my deep learning book:

Click here to grab the PDF of sample chapters and Table of Contents

After clicking the above link, you'll receive a separate email with the PDF in a few short moments.