YOLO v3 introduction
I will make Yolo v3 easy and reusable without over-complicating things. With this tutorial, you will be able to simply implement object detection in TensorFlow 2
YOLO v3 mnist detect
In this tutorial, I’ll cover the Yolo v3 loss function and model training. We’ll train custom object detector on mnist dataset.
YOLO v3 custom train
In this step-by-step tutorial, I will show how to train a 7-class object detector (could use this method to get a dataset for every detector you may use).
YOLO v3 custom images
In this tutorial, I’m going to explain to you an easy way to train YOLO v3 on TensorFlow 2 to detect a custom object even if you’re a beginner or if you have no experience with coding.
YOLO v3 on Colab
In this tutorial, I will demonstrate how to use Google Colab (Google's free cloud service for AI developers) to train the Yolo v3 custom object detector with free GPU.
YOLO v3 Tiny
In this tutorial, you will learn how to utilize YOLOv3-Tiny the same as we did for YOLOv3 for near real-time object detection.
YOLO v3 Object tracking
In this tutorial, I will explain to you what is object tracking, where we use it, what are the differences between object detection and will give you my own working example code.
YOLO v3 mAP metric
In this tutorial, you will figure out how to use the mAP (mean Average Precision) metric to evaluate the performance of an object detection model.
YOLOv3 & Raspberry Pi
This tutorial will provide step-by-step instructions for how to set up TensorFlow 2.* and run YOLOv3 on the Raspberry Pi
In this article, we'll try to understand YOLOv4 theory and why the release of new object detection method spread through the internet in just a few days.
In this part, we will continue bag-of-specials (BoS) and we’ll cover bag-of-freebies (BoF). Will show you how to train custom YOLOv4 model.
In this part, I will show you how we can optimize our deep learning model and speed it up with TensorRT while runing it on NVIDIA GPUs
This tutorial is a brief introduction to multiprocessing in Python. At the end of this tutorial, I will show how I use it to make TensorFlow and YOLO object detection to work faster.
Create training data
In many cases when we want to train a neural network to detect our custom object it's hard to find that labeled data to download, so in this case, we must do this manually, in this tutorial I'll show how to speed up this process.