Pytorch-UNet VS efficientdet-pytorch

Probably mean something like unet: https://github.com/milesial/Pytorch-UNet

Hi! I will give you some resources that might help you understand(I didnt implement a network but I can answer more questions about how you can train it). 1 This link gives you a broad explanation about UNet. 2 This is a link to a UNet used for binary segmentation. 3 This is a step by step guide. The UNet++ that I posted is good for multiclass segmentation. If you need more advice feel free to reply. Good luck!

However, there are papers on oriented object detectors (see https://arxiv.org/pdf/1911.07732.pdf) for example. In that paper, they do achieve better results using oriented bounding boxes. If you want to go down that route, I would suggest using the EfficientDet model, because the PyTorch code that you'll find for it is quite easy to understand and modify. For example, I've taken https://github.com/zylo117/Yet-Another-EfficientDet-Pytorch, and modified it to include a "thing-ness" logit, and this was pretty easy to do. Classic EfficientDet models only include logits (aka output neurons that get softmax-ed) for each class, and if any one of these class neurons is greater than 0.5, then it is considered "a thing". Anyway - that's digression, but my point is that I've thought about adding oriented box support to an EfficientDet model, and it didn't seem to be too hard, although I haven't actually done it. If I was to start now, I would probably go with https://github.com/rwightman/efficientdet-pytorch, since Ross Wightman's models are becoming a de-facto standard in the PyTorch world for all things image-related.