Training YOLOv3 CNN's Using darknet: Part 1 of 14: Purpose of This Series

preview_player
Показать описание
Hi. My name is Tom Lever. I've recorded this video to offer you a guide to getting started using Google Colaboratory to train a YOLOv3 convolutional neural network on the 2014 COCO dataset, the Pascal VOC 2007 and 2012 datasets, or a custom dataset so as to be able to detect objects in images, videos, and camera streams. More technically, I'm offering you a guide to getting started using a Python3 kernel on a Linux virtual machine hosted by a Google server to compile AlexeyAB's deep-learning framework, called darknet, and using the kernel to run a compiled executable file, also called darknet. In running darknet the kernel will forward-propagate a matrix representing training images through a YOLOv3 convolutional neural network, will compare a manipulated matrix to a matrix that encodes whether objects are present in different regions in each image, where they are, and of what classes they are, and will update a weights file based on the above comparison that encodes the values of each cell in each filter in the network. The key take-away from training a YOLOv3 convolutional neural network is a weights file, which I can use to detect objects in images, video frames, or camera-stream frames by forward-propagating those images through the trained network.
Рекомендации по теме
Комментарии
Автор

What if i want to train yolov3 with existing weight file. So i want result like 80 classes and 2 new so total my model is able to detect 82 objects.

gaurangpatel