Waymo self-driving challenge: YOLOR(Scaled-YOLOv4-based) is the best speed/accuracy, better than YOLOv5/Detr

[AlexeyAB]

Waymo autonomous driving challenge (Waymo Open Dataset): Real-time 2D Detection
YOLOR (Scaled-YOLOv4-based) is the best in terms of speed/accuracy, the fastest, and more accurate than YOLOv5 and Detr: https://waymo.com/open/challenges/2021/real-time-2d-prediction/

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• DIDI MapVision https://arxiv.org/abs/2106.08713
• YOLOR https://arxiv.org/abs/2105.04206
  • code (Pytorch): https://github.com/WongKinYiu/yolor
• Scaled-YOLOv4(CVPR21): https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Scaled-YOLOv4_Scaling_Cross_Stage_Partial_Network_CVPR_2021_paper.html
  • code (Pytorch): https://github.com/WongKinYiu/ScaledYOLOv4
• YOLOv4: https://arxiv.org/abs/2004.10934
  • code (Darknet, Pytorch, TensorFlow, TRT, OpenCV...): https://github.com/AlexeyAB/darknet#yolo-v4-in-other-frameworks

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Waymo Open Dataset: https://waymo.com/open/download/

The field of machine learning is changing rapidly. Waymo is in a unique position to contribute to the research community with some of the largest and most diverse autonomous driving datasets ever released.

• Waymo Open Dataset: https://www.youtube.com/watch?v=tuhnwkUeMNo&ab_channel=ComputerVisionFoundationVideosComputerVisionFoundationVideos
• [CVPR'21 WAD] Challenge - Waymo Open Dataset: https://www.youtube.com/watch?v=sjXI_FKYw4Y&ab_channel=WADatCVPR