Please check this comment for the detail information: # Testing #batch=1 #subdivisions=1 # Training batch=64 subdivisions=16 width=608 height=608 channels=3 momentum=0.949 decay=0.0005 angle=0 saturation = 1.5. Darknet: Open Source Neural Networks in C. Darknet is an open source neural network framework written in C and CUDA. It is fast, easy to install, and supports CPU and GPU computation. You can find the source on GitHub or you can read more about what Darknet can do right here:darknet. src/parser.c:280: parse_region: Assertion `l.outputs = params.inputs' failed. I am unfortunately very inexperienced with C++ and have only used darknet in Python so far. For the work I have to set up a C++ project with Visual Studio, in which Yolov3 recognizes objects in the stream of the webcam.
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I use Windows and for testing purposes the whole thing should run on the CPU.Search: Darknet Yolov4. See Darknet: Open Source Neural Networks in C for more information. The source is published on GitHub - pjreddie/darknet: Convolutional Neural Networks. This time we used the repository: GitHub - AlexeyAB/darknet: Windows and Linux version of Darknet Yolo v3 & v2 Neural Networks for object detection (Tensor Cores are used).To the best of our knowledge, this is currently the highest accuracy on the COCO dataset among any published work.
The YOLOv4-tiny model achieves 22.0% AP (42.0% AP50) at a speed of 443 FPS on RTX 2080Ti, while by using TensorRT, batch size = 4 and FP16-precision the YOLOv4-tiny achieves 1774 FPS.
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