{"id":150,"date":"2021-12-12T10:56:19","date_gmt":"2021-12-12T07:26:19","guid":{"rendered":"https:\/\/haghiri75.com\/en\/?p=150"},"modified":"2021-12-12T10:56:19","modified_gmt":"2021-12-12T07:26:19","slug":"why-i-love-yolov5","status":"publish","type":"post","link":"https:\/\/haghiri75.com\/en\/why-i-love-yolov5\/","title":{"rendered":"Why I love YOLOv5?"},"content":{"rendered":"

I am a big fan of Nicholas Renotte’s channel on YouTube. I also love computer vision and its combination with deep learning. A few months ago, Nicholas posted this video<\/a>, which is about YOLOv5. I usually am too lazy to watch videos which are longer than 15 minutes and I watch them in a few episodes. But this video made me sit behind the laptop screen for over an hour and I’m sure I won’t regret it.<\/p>\n

So let’s start the article and see where this story begins. As I mentioned earlier, I love computer vision specially when it’s combined with deep learning. I believe it can help us solve very complex problems of our projects with ease. My journey in world of these YOLO<\/em> models have started almost a year ago, when I wanted to develop a simple object detection for detecting street signs.<\/p>\n

Firstly, I found a lot of tutorials on darknet<\/a> based training but l did not manage to get it to the work, specially since I have a mac, it could be a very realistic nightmare. So I guess YOLOv5 was a miracle. In this article, I am going to explain why I love YOLOv5 and why I prefer it to other YOLO versions.<\/p>\n

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What is YOLOv5?<\/h2>\n

According to their github repository<\/a>, YOLOv5 is a family of deep learning models which is essentially trained on Microsoft’s COCO dataset<\/a>. This makes it a very very general-purpose object detection tool which is fine for basic research and fun projects.<\/p>\n

But I also needed to have my own models because I wanted to develop some domain-specific object detection software. So I realized they also provide a python script which helps you fine-tune and train your own version of YOLOv5.<\/p>\n

So I basically fell in love with this new thing I have discovered. In the next sections, I will explain why I love YOLOv5!<\/p>\n

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Why I love YOLOv5?<\/h2>\n

Firstly, I invite you to see this chart, which shows the comparison of YOLOv5 with other commonly used object detection models:<\/p>\n

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And since there’s been a controversy about YOLOv5 claims about training time, inference time, model storage size, etc. I highly recommend you read this article<\/a> on Roboflow’s blog.<\/p>\n

So we can conclude the very first thing which made me happy is the speed and that’s right. The second thing by the way is the fact I am lazy. Yes, I am lazy and I know it.<\/p>\n

I always tried to compile darknet and use it for having a YOLOv4 model and make my projects on top of YOLOv4 but when I saw how hard it can get and since I have a mac and I didn’t really want to fire-up an old computer for these projects, I was looking for something which does everything with a bunch of python scripts.<\/p>\n

Since I discovered the YOLOv5, I started working with it and the very first project I have done was this pedestrian detection<\/em> for a self-driving car<\/em>.<\/p>\n