<p>It is not possible. In the past people trained several networks for different scales but the current state-of-the-art approach is feature pyramids.</p>
<p><a href="https://arxiv.org/pdf/1612.03144.pdf" rel="nofollow noreferrer">https://arxiv.org/pdf/1612.03144.pdf</a></p>
<p>Another great candidate is to use dilated convolution which can learn long distance dependencies among pixels with varying distance. You can concatenate the outputs of them and the model will then learn which distance is important for which case</p>
<p><a href="https://towardsdatascience.com/review-dilated-convolution-semantic-segmentation-9d5a5bd768f5" rel="nofollow noreferrer">https://towardsdatascience.com/review-dilated-convolution-semantic-segmentation-9d5a5bd768f5</a></p>
Change the spatial input dimension during training
<p>I am training a yolov4 (fully convolutional) in tensorflow 2.3.0.</p>
<p>I would like to change the spatial input shape of the network during training, to further adjust the weights to different scales.</p>
<p>Is this possible?</p>
<p>EDIT:
I know of the existence of darknet, but it suffers from some very specific augmentations I use and have implemented in my repo, that is why I ask explicitly for tensorflow.</p>
<p>To be more precisely about what I want to do.</p>
<p>I want to train for several batches at <code>Y1xX1xC</code> then change the input size to <code>Y2xX2xC</code> and train again for several batches and so on.</p>