Description
YOLOv3 – Robust Deep Learning Object Detection in 1 hour, the training course on object detection with deep learning and YOLOv3 has been published by Yudemy Academy. When we first started working in deep learning, especially in computer vision, we were really excited about the possibilities of this technology to help people. The only problem is that if you’re just starting to learn about object recognition, you might run into some of the following common obstacles along the way: labeling datasets is tedious and difficult, annotation formats between models Different detection of objects are completely different. Labels can be messed up with free annotation tools, unclear instructions on how to train models – wasting a lot of time during trial and error. Duplicate images are hard to manage.
This led us to look for a better way to manage the object detection workflow that would not only help us better manage the object detection process, but also improve our time to market. In this course I’ll show you how to use this workflow with your own custom Yolov3 tutorial, as well as how to deploy your own models using PyTorch. So basically, we’ve designed this tutorial to reduce debugging, speed up your time to market, and get your product to fruition sooner.
What you will learn
- Learn object recognition using Yolov3.
- Learn an object detection workflow that will save you time and money.
- The fastest way to collect images and annotate datasets.
- Important point regarding Data Augmentation to increase data
- How to use artificial intelligence to label your datasets
- See how you can train your custom Yolov3 from scratch.
- Step-by-step training on how to run, unlock, boot and run customized Yolov3 models.
Who is this course suitable for?
- This course is for students who have Python, Opencv or AI experience and want to learn how to detect objects with Yolov3.
- Those who already have a theoretical understanding of Object Detection, CNN and Yolo Architecture.
- Those looking for a practical way to detect objects with Yolov3.
YOLOv3 course specifications – Robust Deep Learning Object Detection in 1 hour
- Publisher: Udemy
- teacher : Augmented Startups
- English language
- Education level: Intermediate
- Number of courses: 20
- Training duration: 2 hours and 18 minutes
YOLOv3 course chapters – Robust Deep Learning Object Detection in 1 hour
Course prerequisites
- Have prior experience in Python using Anaconda.
- A PC/Laptop with CUDA-enabled NVIDIA graphics card for training – We use Ubuntu for training.
- Create a Free Account with Supervisely.
- Be Familiar with Computer Vision or AI.
Pictures
Sample video
Installation guide
After Extract, view with your favorite Player.
English subtitle
Quality: 720p
download link
File(s) password: www.downloadly.ir
Size
2.21 GB