Development and construction of butterfly target detection and recognition system based on YOLOv8

In a previous blog post, I have described in detail how to develop and build my own personalized target detection model based on YOLOv8. If you are interested, you can see:

"Ultra-detailed tutorial on developing and building a target detection model based on YOLOv8 [taking the weld quality detection data scene as an example]"

The main purpose of this article is to develop and build a fine-grained butterfly target detection and analysis system based on YOLOv8. First, look at the renderings:

 The data set is as follows:

 You can replace it with your own data set according to your needs.

The data sets I use here are all collected from the crawler network, and then manually labeled.

The overall use of YOLOv8 should be the most direct and simple form. If you have any questions here, you can refer to my previous article. I won’t expand here. Just look at the example reasoning effect:

 Here I store the result locally after parsing and processing, as shown below:

{"Byasa_alcinous": [[0.8852506875991821, [91, 125, 287, 386]]]}

The interface example is as follows: