Vision transformer for object detection beats state-of-art ResNet-50

Vision transformer for object detection beats state-of-art ResNet-50

Will Transformers Replace CNNs in Computer Vision?

In less than 5 minutes, you will know how the transformer architecture can be applied to computer vision with a new paper called the Swin Transformer

Towards AI https://pub.towardsai.net/will-transformers-replace-cnns-in-computer-vision-55657a196833

YouTube 9 min video https://www.youtube.com/watch?v=QcCJJOLCeJQ

Comparison of SWIN with other object detection algorithms

Object Detection on COCO test-dev

Papers With Code https://paperswithcode.com/sota/object-detection-on-coco

Swin Transformer: Hierarchical Vision Transformer using Shifted Windows (Microsoft)

arXiv paper abstract https://arxiv.org/abs/2103.14030v1

arXiv PDF paper https://arxiv.org/pdf/2103.14030v1.pdf

GitHub https://github.com/microsoft/Swin-Transformer

This paper presents a new vision Transformer, called Swin Transformer, that capably serves as a general-purpose backbone for computer vision.

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This hierarchical architecture has the flexibility to model at various scales and has linear computational complexity with respect to image size. These qualities of Swin Transformer make it compatible with a broad range of vision tasks, including image classification ... and dense prediction tasks such as object detection ... and semantic segmentation

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Its performance surpasses the previous state-of-the-art by a large margin of +2.7 box AP and +2.6 mask AP on COCO, and +3.2 mIoU on ADE20K, demonstrating the potential of Transformer-based models as vision backbones.

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