Yet-Another-EfficientDet-Pytorch VS SynthDet

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.

Didn't try but Unity did a object recognition project with store products: https://github.com/Unity-Technologies/SynthDet. You can find the models and texture in the "Assets/Foreground Objects" folder. And you may need to check the license depending on your usage.

This is probably what you're looking for . https://github.com/Unity-Technologies/SynthDet

So I'm working on Unity with this Project (SynthDet) https://github.com/Unity-Technologies/SynthDet and I'm working on imported objects (obj. ), trying to edit materials of those objects, but I can't. The materials are gredy out. Don't know what to do. Do you guys have any ideas what to do, to allow to edit imported materials?