For a 640*640 image, what is the smallest object that yolov5s can detect

[LiaoChengkk]

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Hello! I wonder for a 640*640 image, what is the smallest object that yolov5s can detect?

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[github-actions]

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Designed to be fast, accurate, and easy to use, YOLOv8 is an ideal choice for a wide range of object detection, image segmentation and image classification tasks. With YOLOv8, you'll be able to quickly and accurately detect objects in real-time, streamline your workflows, and achieve new levels of accuracy in your projects.

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[glenn-jocher]

@LiaoChengkk hello!

Thank you for your question and for checking the existing issues and discussions before posting. The smallest object that YOLOv5s can detect in a 640x640 image largely depends on several factors, including the object's size relative to the image, the quality of the training data, and the specific use case.

In general, YOLOv5s can detect objects that occupy at least a few pixels in each dimension. However, for practical purposes, objects that are smaller than 10x10 pixels might be challenging to detect reliably. The model's performance can be improved by ensuring that your training dataset includes a variety of object sizes and that the smaller objects are well-represented.

If you are working with particularly small objects, you might consider:

1. Increasing the image resolution: Training with higher resolution images can help the model detect smaller objects.
2. Using a larger model: Models like YOLOv5m, YOLOv5l, or YOLOv5x have more capacity and might perform better on smaller objects.
3. Data augmentation: Techniques such as mosaic augmentation can help the model learn to detect smaller objects more effectively.

Feel free to experiment with these suggestions and see what works best for your specific application. If you have any further questions or need more detailed assistance, please let us know!

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