Do "empty" background images help during model training?

[haimat]

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Question

I have an image training data set showing the objects I need to detect, however, without much "background" around them - it's basically just a collection of the objects I need to detect. Therefore I have created "random" backgrounds and placed the objects I need to detect onto those generated background images.

However, what I do also have is a set of original background images, but without any objects to detect. Would it make sense to add these "empty" background images, i.e. without any labels, to the training data set, so that during training the model would at least see such backgrounds, even without any labels/objects? Or would the YOLOv8 framework ignore images with no labels at all?

Additional

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[glenn-jocher]

Including "empty" background images in your training dataset can indeed be beneficial. It helps the model learn what not to detect, which can improve its ability to distinguish between relevant objects and irrelevant background noise. This approach can reduce false positives.

YOLOv8 will not ignore images with no labels. These images are used to teach the model the concept of "negative" examples, where no objects of interest are present. Just ensure that these background images are representative of the environments where the model will be deployed.

Here's a simple example of how you might include such images in your dataset configuration:

# Example dataset configuration snippet
train: ./data/train/images/
val: ./data/val/images/

# Paths can include both object images and empty background images

Make sure your training and validation sets both include a mix of images with and without objects. This setup should help enhance the robustness of your model.

[CedarYang]

"YOlO expects at least some annotationseven if it's just an empty file to signify no objects in certain images."
do i need to set some txt files as label for the no label image?

[glenn-jocher]

Yes, for images without any detectable objects, you should still provide an empty .txt file for each image. This file should be named identically to the image file but with a .txt extension. This way, YOLO understands that the image has been considered during training and intentionally contains no annotations. This helps in effectively training the model to recognize scenes without target objects.