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Keyword: DETR ×Clear all

3 claims from 1 paper

1. Computer Science › Advanced Neural Network Applications

   You Only Look One-level Feature

   Chen, Wang, Yang, Zhang, Cheng and Sun · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · 2021

   The paper argues that feature pyramids help object detectors mainly by easing optimisation, and presents YOLOF, a one-level-feature detector that matches RetinaNet, and DETR, with less cost.

   Unchecked3 claims
   Show 3 claims

   1. UncheckedOn the COCO benchmark, YOLOF, which uses one feature level, reaches results comparable to RetinaNet's feature pyramid and runs 2.5 times faster.“Our YOLOF achieves comparable results with its feature pyramids counterpart RetinaNet while being $2.5\times$ faster.”
   2. UncheckedThe paper says YOLOF, with no transformer layers and one feature level, can match DETR's performance using seven times fewer training epochs.“Without transformer layers, YOLOF can match the performance of DETR in a single-level feature manner with $7\times$ less training epochs.”
   3. UncheckedAt 608×608 image size, YOLOF is reported to reach 44.3 mAP at 60 fps on a 2080Ti GPU, 13% faster than YOLOv4.“With an image size of $608\times608$, YOLOF achieves 44.3 mAP running at 60 fps on 2080Ti, which is $13\%$ faster than YOLOv4.”