Cropping training images along with their respective annotations

[andualemw1]

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Question

As we know, YOLO supports both square and rectangular images. However, for speed and dataset size considerations, I want to crop an image from 1280x1280 to 640x640. YOLO annotations/labels are originally created based on the image’s width and height. How can I bridge the gap in training the dataset before and after cropping the image while keeping the annotations unchanged?

thanks in advance!

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[UltralyticsAssistant]

👋 Hello @andualemw1, thank you for your interest in YOLOv5 🚀! An Ultralytics engineer will assist you soon.

To get started with cropping images while retaining annotations, you might find our ⭐️ Tutorials helpful. You can explore guides for tasks such as Custom Data Training where managing image sizes and annotations is discussed.

If this is a 🐛 Bug Report, please provide a minimum reproducible example to help us debug it.

For custom training questions, provide as much information as possible, including dataset image examples and training logs. Verify you are following our Tips for Best Training Results.

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cd yolov5
pip install -r requirements.txt  # install

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