Swin-Transformer-Object-Detection VS TACO

I'm currently trying to transfer learn a set of custom classes of fish, for instance segmentation. I have found the official implementation of Swin Transformer as a backbone for instance segmentation using MRCNN: https://github.com/SwinTransformer/Swin-Transformer-Object-Detection.

Hi, So my project is to do with object detection on trash in the wild on this fairly obscure dataset: http://tacodataset.org/ and I was thinking of applying vision transformers to it for feature extraction. I was thinking of taking the YOLOX implementation and swapping out the backbone with swin transformers and perform bunch of comparisons/experiments for the write up. Sort of like how they applied swin transformers to mask R-CNN here but I am struggling to understand where to begin.

Yes, have a look at this paper.

Object detection on COCO: https://github.com/SwinTransformer/Swin-Transformer-Object-Detection

http://tacodataset.org/ <- Open source dataset if you want to train a classifier, I like this one

Hi, So my project is to do with object detection on trash in the wild on this fairly obscure dataset: http://tacodataset.org/ and I was thinking of applying vision transformers to it for feature extraction. I was thinking of taking the YOLOX implementation and swapping out the backbone with swin transformers and perform bunch of comparisons/experiments for the write up. Sort of like how they applied swin transformers to mask R-CNN here but I am struggling to understand where to begin.

I was wondering if anyone had a script or could point me to one that would be able to convert polygons from image segmentation to bounding boxes for object detection. I am looking to create a trash detector to run on my trash picking up robot. I found the TACO dataset, but it uses segmentation and I just want to start with bounding boxes. Any help would be appreciated.