YOLOv4 was released at the end of April. It soon gained popularity among the machine learning community. You can check it out with following link.

• YOLO v4 essay：https://arxiv.org/abs/2004.10934
• YOLO v4 code：https://github.com/AlexeyAB/darknet

Compared with YOLOv3, YOLOv4’s AP has increased by 10%, while its FPS has increased by 12%. But can Jetson Nano handle YOLOv4? If you have tried YOLOv3 (darknet version) on Jetson Nano to perform real-time object detection, especially using the darknet version, you know what I’m saying. Usually, Jetson can only run the detection at around 1 FPS.

But with YOLOv4, Jetson Nano can run detection at more than 2 FPS.

Although YOLOv4 runs 167 layers of neural network, which is about 50% more than YOLOv3, 2 FPS is still too low. Now let’s try to accelerate it with PyTorch.

Run the following command

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git clone https://github.com/ultralytics/yolov3 && cd yolov3
#down load yolov4 pre-trained weights
wget https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v3_optimal/yolov4.weights
#convert darknet cfg/weights to pytorch model
python3 -c "from models import *; convert('cfg/yolov4.cfg', 'yolov4.weights')"

When success, you can run the YOLOv4 PyTorch model by using the following command

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python3 detect.py --cfg cfg/yolov4.cfg --weights weights/yolov4.pt --source 0

Now you can enjoy the 10 FPS full YOLO on Jetson Nano!

Source: /blog/2020/06/03/accelerate-yolov4-real-time-object-detection-on-jetson-nano/