How does Yolov3 loss function change with resolution of the image?
In general input size changes the size of the final grid and so the number of predicted boxes. Depending on the implementation you use this can change the loss. If e.g. the loss values for one parameter are all summed up, more boxes would mean higher loss. If loss values are averaged over all boxes you can probably compare it.
You might wanna use some other form of evaluation besides just the loss. Depending on the task have a look at benchmarks and the metrics they are using. For object detection the IoU is often used to assign predictions to ground truth and calculate a precision out of it.
I'm training Yolov3 on my dataset. I'm training it on different resolutions of the image, trying to find the best speed/quality ratio. I started wondering if I can compare the loss function of Yolo between different resolutions. Anchor sizes are proportional to resolution. 
So does same loss on different resolutions mean I'd get approximately the same results on my test dataset?