Feature Request : Segmentation model

The paper lists an easy way to use the model for segmentation. I really hope there is enough flexibility in your code to allow for that alteration.

Following [16], we modify our EfficientDet model to keep feature level {P2,P3,...,P7} in BiFPN, but only use P2 for the fi- nal per-pixel classification. For simplicity, here we only evaluate a EfficientDet-D4 based model, which uses a Ima- geNet pretrained EfficientNet-B4 backbone (similar size to ResNet-50). We set the channel size to 128 for BiFPN and 256 for classification head. Both BiFPN and classification head are repeated by 3 times.

[faheemullah]

@rwightman, @bluesky314, How will be its diagram if we add segmentation also to EfficientDet in order to get results like Mask R-CNN? The paper has explained briefly but I couldn’t get that.

[jinfagang]

@rwightman Do u have plan to switch Yolact backbone to efficientnet to do instance segmentation? It’s also most same in terms of objecet detection part (only difference is backbone and FPN) both retina architecture and design. it would be very awsome if instance segmentation implemented upon efficientdet. (I believe Yolact is at the bottle neck of it’s detector, but it’s simplicity and speed quite amazing)