如何从训练和测试图像创建 Yolo model？

Question

I have a dataset of images that have two folders: test and training. I need to do object detection using OpenCV and Yolo.

Thus, I need to create my own Yolo model for the street objects.

For the training folder: training

Example training image: training image

For the test folder: test

I have the classes txt file which includes id, name and classification (warning, indication and mandatory). Example:

0 = animal crossing (warning)
1 = soft verges (warning)
2 = road narrows (warning)

Here, the numbers are the numbers (or ids) in the training folder, names, and classification.

My purpose is to create a Yolo model from these training images. I have checked some papers and articles, but in their case, they label the full image using labelimg, but in my case training images are so small and they don't need any labeling.

Thus, I'm confused about how to do this. Could you please give me some ideas?

Answer 1

I would refer to this image that describes the different types of computer vision tasks.

I think what you want to do is a Classification tasks. Yolo is for Object Detection tasks, where you usually want to detect more than one object per image.

For classification tasks, it can be easier because you don't need to make separate label files. The names of the folders are the labels. Here is an example of a classification model that you can use https://pytorch.org/tutorials/beginner/transfer_learning_tutorial.html

If you really want to use Yolo you will need to make label files. If you are going to do Classification of the whole image then the format of the annotation will be easy. It would be something like this.

`0 0.5 0.5 1 1' The first column is the class number: 0,1,2,3 etc. You will need to make one file for each image with the name.txt.

Does this help you?

Answer 2

Labeling images is a must in YOLO's that's how they deal with their loss functions. To detect objects something called (intersection over union ) More easy way to label images is by using (roboflow site ).