making yolo v8 train on data that have it directory listed on .csv

[mohaMKRaod]

i want to know how can i give yolo v8 a .csv file that include the paths for the training and validation image for an object detection task instead of the root folder directory.

[glenn-jocher]

@mohaMKRaod hello! To train YOLOv8 with a .csv file containing the paths to your training and validation images, you'll need to convert your .csv data into the format that YOLOv8 expects for training. YOLOv8 uses a specific directory structure and annotation format, typically with a .yaml file that specifies the paths to your dataset directories and classes.

Here's a brief overview of the steps you would follow:

1. Convert CSV to YOLO Format: You'll need to convert your .csv file into two separate text files for training and validation, where each line contains the path to an image followed by the bounding box annotations in YOLO format.

2. Create Dataset Directory: Organize your images and annotation text files into the appropriate directory structure. For example, you might have a dataset folder with subfolders images and labels for both training and validation.

3. Prepare YAML File: Create a .yaml file that lists the paths to your training and validation directories, as well as the class names.

4. Train: Use the YOLOv8 CLI or Python environment to start training with the .yaml file.

For detailed instructions on the expected dataset format and the training process, please refer to the documentation at https://docs.ultralytics.com. The documentation provides comprehensive guidance on preparing your data and configuring your training environment. Happy training! 🚀