how to write data cfg file?

[hitbuyi]

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Question

mydata
├── dataSet
├── image
├── label
└── label_xml
where dataSet includes trainval.txt,val.txt, image is png image, label includes txt lablefiles,
how to write data cfg ymal files?

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[pderrenger]

To create a YAML configuration file for your dataset structure, you'll need to specify the dataset path and split information. Here's an example based on your folder structure:

# mydata.yaml
path: /path/to/mydata  # dataset root dir
train: dataSet/trainval.txt  # path to train images list (relative to 'path')
val: dataSet/val.txt  # path to val images list (relative to 'path')

# Classes
nc: 80  # number of classes
names: ['person', 'bicycle', 'car', ...]  # class names

Make sure your trainval.txt and val.txt files contain relative paths to your images in the image folder, and that your label files in the label folder follow the YOLO format with corresponding filenames to your images.

[hitbuyi]

To create a YAML configuration file for your dataset structure, you'll need to specify the dataset path and split information. Here's an example based on your folder structure:

mydata.yaml

path: /path/to/mydata # dataset root dir
train: dataSet/trainval.txt # path to train images list (relative to 'path')
val: dataSet/val.txt # path to val images list (relative to 'path')

Classes

nc: 80 # number of classes
names: ['person', 'bicycle', 'car', ...] # class names
Make sure your trainval.txt and val.txt files contain relative paths to your images in the image folder, and that your label files in the label folder follow the YOLO format with corresponding filenames to your images.

I think it should be /images/ and /labels/, image files should be placed inimagesfolder, and labels should be placed inlabelsfolder

[pderrenger]

You're absolutely right! For YOLOv5 to work properly, your dataset structure should follow the standard convention with images and labels folders. You'll need to rename your image folder to images and your label folder to labels, then update your YAML accordingly:

# mydata.yaml
path: /path/to/mydata  # dataset root dir
train: images/train  # or dataSet/trainval.txt if using txt files
val: images/val      # or dataSet/val.txt if using txt files

# Classes
nc: 80  # number of classes
names: ['person', 'bicycle', 'car', ...]  # class names

This ensures YOLOv5 can automatically find the corresponding label files in the labels folder for each image in the images folder.