• Network Type: Detectnet_v2
• TLT Version: 4.0.1 (docker image 4.0.1-tf1.15.5)
We are training a DetectNet_v2 on our own dataset. Our dataset has a known bias towards a specific object size (~100px w/h). In the training spec, we set the zoom augmentation to prevent the network from biasing towards a target size:
zoom_min: 0.5zoom_max: 5.0
However, after continuing to have issues detecting larger objects, we visualized some of the images/results with tensorboard since we do not perform data augmentation offline. All training sessions show images zoomed way out (up to 5x). Per Experiment Spec File: meaning of zoom_min and zoom_max, zoom_max is supposed to be zooming in (i.e. image gets larger), however all of our images in the tensorboard are shown to be zoomed out instead (zoom_min).
Below, is one of the tensorboard images that shows the training data being zoomed out by a factor of approximately 4 despite the zoom specification limiting the zoom out to 0.5 (there is translation as well but this is expected). Is the decription in the link above incorrect or is there a bug flipping the zoom_min and zoom_max augmentation properties?
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