Scaled YOLOv4

Chien-Yao Wang, Alexey Bochkovskiy, and Hong-Yuan Mark Liao (more commonly known by their GitHub monikers, WongKinYiu and AlexyAB) have propelled the YOLOv4 model forward by efficiently scaling the network’s design and scale, surpassing the previous state-of-the-art EfficientDet published earlier this year by the Google Research/Brain team.

Train scaled YOLOv4 (PyTorch)

The Scaled-YOLOv4 implementation is written in the YOLOv5 PyTorch framework. Training scaled YOLOv4 is similar to training YOLOv5.

Here is the Scaled-YOLOv4 repo, though you will notice that WongKinYiu has provided it there predominantly for research replication purposes and there are not many instructions for training on your own dataset. To train on your own data, our guide on training YOLOv5 in PyTorch on custom data will be useful, as it is a very similar training procedure.

Tutorials from Roboflow:

• Video tutorial:

• Blog post

• Google Colab Notebook

My Colab Notebook: yolov4_scaled.ipynb

Train scaled YOLOv4 (Darknet)

YOLOv4-csp training is also supported by Darknet. Training yolov4-csp is similar to training yolov4 and yolov4-tiny. Slight difference:

• For config file, use yolov4-csp.cfg
• For pretrained weights, use yolov4-csp.weights
• For pretrained convolutional layer weights, use yolov4-csp.conv.142

Reference

• Scaled YOLOv4 paper

• Github repo: WongKinYiu/ScaledYOLOv4 (Different size of model in different branch)

• Blog post from AlexAB: Scaled YOLO v4 is the best neural network for object detection on MS COCO dataset

• Tutorials blog posts:

  • robolfow: Scaled-YOLOv4 is Now the Best Model for Object Detection
  • YOLOv4 团队最新力作！1774fps、COCO 最佳精度，分别适合高低端 GPU 的 YOLO
  • 上达最高精度，下到最快速度，Scaled-YOLOv4：模型缩放显神威