PyTorch Object Detection :: YOLOv5 TXT


YOLOv5 is Here

YOLOv5 was released by Glenn Jocher on June 9, 2020. It follows the recent releases of YOLOv4 (April 23, 2020) and EfficientDet (March 18, 2020).

YOLO Inference

YOLOv5 Performance

YOLOv5 is smaller and generally easier to use in production. Given it is natively implemented in PyTorch (rather than Darknet), modifying the architecture and exporting to many deploy environments is straightforward.

  • SIZE: YOLOv5s is about 88% smaller than big-YOLOv4 (27 MB vs 244 MB)

YOLOv5 Size

  • SPEED: YOLOv5 performs batch inference at about 140 FPS by default.

  • ACCURACY: YOLOv5 is roughly as accurate as YOLOv4 on small tasks (0.895 mAP vs 0.892 mAP on BCCD). On larger tasks like COCO, YOLOv4 is more performant.

Read more about YOLOv5 performance.

YOLOv5 Performance

Using YOLOv5

We've written both a YOLOv5 tutorial and YOLOv5 Colab notebook for training YOLOv5 on your own custom data.

2022 YOLOv5 releases Classification and Instance Segmentation

YOLOv5 launched supporting bounding boxes for object detection. Now you can use YOLOv5 for classification and instance segmentation as well.