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Darknet (neural network framework)

FAQ - frequently asked questions: FAQ - frequently asked questions

cfg-files - are structures of neural networks: https://github.com/AlexeyAB/darknet/tree/master/cfg
weights-files - are weights for correspond cfg-file, can be downloaded from: https://pjreddie.com/darknet/

There are several tasks that are implemented in the Darknet framework:

CLICK ME - Use Yolo in other frameworks

OpenCV-dnn the fastest implementation for CPU (x86/ARM-Android), OpenCV can be compiled with OpenVINO-backend for running on (Myriad X / USB Neural Compute Stick / Arria FPGA), use yolov3.weights/cfg with: C++ example or Python example
Converting Yolo v3 models to TensorFlow and OpenVINO(IR) models: https://github.com/AlexeyAB/darknet/wiki/Converting-Yolo-v3-models-to-TensorFlow-and-OpenVINO(IR)-models
- TensorFlow: convert yolov3.weights/cfg files to yolov3.ckpt/pb/meta: by using mystic123 or jinyu121 projects, and TensorFlow-lite
- Intel OpenVINO 2019 R1: (Myriad X / USB Neural Compute Stick / Arria FPGA): read this manual
PyTorch > ONNX > CoreML > iOS how to convert cfg/weights-files to pt-file: ultralytics/yolov3 and iOS App
TensorRT for YOLOv3 (-70% faster inference): Yolo is natively supported in DeepStream 4.0 read PDF
TVM - compilation of deep learning models (Keras, MXNet, PyTorch, Tensorflow, CoreML, DarkNet) into minimum deployable modules on diverse hardware backends (CPUs, GPUs, FPGA, and specialized accelerators): https://tvm.ai/about
OpenDataCam - It detects, tracks and counts moving objects by using Yolo: https://github.com/opendatacam/opendatacam#-hardware-pre-requisite
Netron - Visualizer for neural networks: https://github.com/lutzroeder/netron