YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors

Code

• github.com/wongkinyiu/yolov7
  OfficialIn paperpytorch★ 14,114
• github.com/pjreddie/darknet
  pytorch★ 26,445
• github.com/AlexeyAB/darknet
  tf★ 22,190
• github.com/PaddlePaddle/PaddleDetection
  paddle★ 14,132
• github.com/open-mmlab/mmyolo
  pytorch★ 3,429
• github.com/WongKinYiu/YOLO
  pytorch★ 1,633
• github.com/PaddlePaddle/PaddleYOLO
  paddle★ 661
• github.com/securade/hub
  pytorch★ 256
• github.com/ibaiGorordo/ONNX-YOLOv7-Object-Detection
  none★ 207
• github.com/mkang315/rcs-yolo
  pytorch★ 97

Abstract

YOLOv7 surpasses all known object detectors in both speed and accuracy in the range from 5 FPS to 160 FPS and has the highest accuracy 56.8% AP among all known real-time object detectors with 30 FPS or higher on GPU V100. YOLOv7-E6 object detector (56 FPS V100, 55.9% AP) outperforms both transformer-based detector SWIN-L Cascade-Mask R-CNN (9.2 FPS A100, 53.9% AP) by 509% in speed and 2% in accuracy, and convolutional-based detector ConvNeXt-XL Cascade-Mask R-CNN (8.6 FPS A100, 55.2% AP) by 551% in speed and 0.7% AP in accuracy, as well as YOLOv7 outperforms: YOLOR, YOLOX, Scaled-YOLOv4, YOLOv5, DETR, Deformable DETR, DINO-5scale-R50, ViT-Adapter-B and many other object detectors in speed and accuracy. Moreover, we train YOLOv7 only on MS COCO dataset from scratch without using any other datasets or pre-trained weights. Source code is released in https://github.com/WongKinYiu/yolov7.

Tasks

Benchmark Results

Reproductions