- efficientdet-pytorch VS darknet
- efficientdet-pytorch VS segmentation_models.pytorch
- efficientdet-pytorch VS Yet-Another-EfficientDet-Pytorch
- efficientdet-pytorch VS Pytorch-UNet
- efficientdet-pytorch VS mmsegmentation
- efficientdet-pytorch VS involution
- efficientdet-pytorch VS ros-semantic-segmentation-pytorch
- efficientdet-pytorch VS InternImage
- efficientdet-pytorch VS automl
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efficientdet-pytorch reviews and mentions
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Bounding box annotations and object orientation
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.
Stats
rwightman/efficientdet-pytorch is an open source project licensed under Apache License 2.0 which is an OSI approved license.
The primary programming language of efficientdet-pytorch is Python.
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