Yolov5: Train image size and image prediction size must have same pixels ?

[blinkbink]

❔Question

1. in Yolov5, i trained 1000 data image with random image size, like 1024x768 or 640x480 etc, is it wrong ?
2. trained required same size ? Example 640x640 all for data trained ?
3. data image to predict must have same size with data trained, example trained data is 640x480 then test image have size 1024x768 ?

Thank you

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[NanoCode012]

You don’t need to resize them manually. It just works which is great.

I don’t know if there’s a “perfect” image size to use. It should be up to your experiment.

[luvwinnie]

i took a dataset with 4k and trained with 640, however, it seems like I need to use --img-size=3840 with detect.py to detect correctly, anyway to get the best performance for any size?

[glenn-jocher]

@luvwinnie train and detect at the same --img-size for best results.

[blinkbink]

@glenn-jocher how about the size on training data but with base width is 640, i mean the base width is 640 but have various height like 320x640, 480x640, 221x640, so just can resize by width, not both height and width, because if i resize with same size 640x640 got strecth image

[glenn-jocher]

@blinkbink train and detect at the same --img-size for best results, such as the default --img 640.

[blinkbink]

yes sorry, i don't understand, i mean --image-size 640 is for 640x640 right ? how about various like 480x640, 320x640, 211x640 ?

[glenn-jocher]

@blinkbink you only supply the longest dimension, --img 640. The rest is handled automatically.