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YOLOv4 model zoo: https://github.com/AlexeyAB/darknet/wiki/YOLOv4-model-zoo
FAQ - frequently asked questions: FAQ - frequently asked questions
Google Colab - use free GPU in Cloud for easy step-by-step examples of Detection and Training:
- Compilation and Detection Compiling And Running Darknet on Kaggle
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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:
- Yolo detection: https://pjreddie.com/darknet/yolo/
- ImageNet classification: https://pjreddie.com/darknet/imagenet/
- Text Generation: https://pjreddie.com/darknet/rnns-in-darknet/
- GO playing: https://pjreddie.com/darknet/darkgo-go-in-darknet/
- GAN Nightmare: https://pjreddie.com/darknet/nightmare/
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Train Classifier on ImageNet (ILSVRC2012): https://github.com/AlexeyAB/darknet/wiki/Train-Classifier-on-ImageNet-(ILSVRC2012)
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Train YOLOv4 Detector on MS COCO (trainvalno5k 2014) dataset: https://github.com/AlexeyAB/darknet/wiki/Train-Detector-on-MS-COCO-(trainvalno5k-2014)-dataset
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How to evaluate accuracy and speed of YOLOv4 on MS COCO dataset: https://github.com/AlexeyAB/darknet/wiki/How-to-evaluate-accuracy-and-speed-of-YOLOv4
- Train and Evaluate Detector on Pascal VOC (VOCtrainval 2007 2012) dataset: https://github.com/AlexeyAB/darknet/wiki/Train-and-Evaluate-Detector-on-Pascal-VOC-(VOCtrainval-2007-2012)-dataset
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CFG-Parameters in the
[net]
section: https://github.com/AlexeyAB/darknet/wiki/CFG-Parameters-in-the-%5Bnet%5D-section -
CFG-Parameters in the different layers: https://github.com/AlexeyAB/darknet/wiki/CFG-Parameters-in-the-different-layers
CLICK ME - Use Yolo in other frameworks
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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
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TensorFlow: convert
yolov3.weights
/cfg
files toyolov3.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
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TensorFlow: convert
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PyTorch > ONNX > CoreML > iOS how to convert cfg/weights-files to pt-file: ultralytics/yolov3 and iOS App
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TensorRT for YOLOv3 (-70% faster inference): Yolo is natively supported in DeepStream 4.0 read PDF
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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
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OpenDataCam - It detects, tracks and counts moving objects by using Yolo: https://github.com/opendatacam/opendatacam#-hardware-pre-requisite
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Netron - Visualizer for neural networks: https://github.com/lutzroeder/netron