End-to-End Object Detection with Transformers
Authors present a new method that views object detection as a direct set prediction problem.
This approach simplifies the detection pipeline, effectively removing the need for many hand-designed components like a non-maximum suppression procedure or anchor generation that explicitly encode our prior knowledge about the task.
The main ingredients of the new framework, called DEtection TRansformer or DETR, are a set-based global loss that forces unique predictions via bipartite matching, and a transformer encoder-decoder architecture
DETR demonstrates accuracy and run-time performance on par with the well-established and highly-optimized Faster RCNN baseline on the challenging COCO object detection dataset. Moreover, DETR can be easily generalized to produce panoptic segmentation in a unified manner
Paper: https://arxiv.org/abs/2005.12872
Code: https://github.com/facebookresearch/detr
#deeplearning #objectdetection #transformer #coco
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Team
@OpenArchiveBooks
@data_entusiasts
Authors present a new method that views object detection as a direct set prediction problem.
This approach simplifies the detection pipeline, effectively removing the need for many hand-designed components like a non-maximum suppression procedure or anchor generation that explicitly encode our prior knowledge about the task.
The main ingredients of the new framework, called DEtection TRansformer or DETR, are a set-based global loss that forces unique predictions via bipartite matching, and a transformer encoder-decoder architecture
DETR demonstrates accuracy and run-time performance on par with the well-established and highly-optimized Faster RCNN baseline on the challenging COCO object detection dataset. Moreover, DETR can be easily generalized to produce panoptic segmentation in a unified manner
Paper: https://arxiv.org/abs/2005.12872
Code: https://github.com/facebookresearch/detr
#deeplearning #objectdetection #transformer #coco
ᅠᅠ
Team
@OpenArchiveBooks
@data_entusiasts