Full YOLOv4 Pro Course Bundle

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Ritesh Kanjee

4:42:00

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01.01-introduction.mp4

03:51

01.02-how to excel in this course.mp4

02:56

01.03-yolov4 theory.mp4

11:48

01.04-installation of yolov4 dependencies such as cuda python opencv.mp4

13:23

02.01-yolov4 object detection on image and video.mp4

10:03

02.02-yolov4 darknet explanation with code and webcam implementation.mp4

05:54

02.03-social distancing monitoring app.mp4

11:21

02.04-social distancing monitoring coaching session.mp4

19:47

02.05-count parked cars.mp4

07:08

02.06-deepsort intuition-how deepsort object tracking works.mp4

15:56

02.07-robust tracking with yolov4 and deepsort.mp4

08:17

03.01-evolution of yolov1 to yolov3.mp4

12:25

03.02-yolov5 chess piece detection.mp4

19:51

03.03-bernie sanders detector.mp4

25:00

04.01-introduction to data annotation.mp4

01:34

04.02-yolov4 format for image labelling.mp4

01:37

04.03-yolov4 labelling tools.mp4

03:15

04.04-web-scaping data.mp4

02:55

04.05-annotating images with labelimg.mp4

02:44

04.06-labelling on video using labelimg.mp4

03:12

04.07-labelling on video using darklabel.mp4

04:03

04.08-label objects on this video.mp4

00:57

04.09-annotation summary.mp4

01:34

04.10-data annotation key takeaway.mp4

01:32

05.01-introduction how to create custom dataset.mp4

00:59

05.02-toolkit for downloading image datasets.mp4

03:12

05.03-downloading images from specific classes.mp4

05:19

05.04-converting downloaded files to yolov4 format.mp4

19:30

05.05-data augmentation using rotational transform.mp4

05:26

05.06-summary-key takeaways for custom datasets.mp4

00:53

06.01-introduction to training yolov4 with darknet framework.mp4

01:10

06.02-step 1-configuring the files for training.mp4

03:34

06.03-step 2-creating the obj.names file.mp4

00:46

06.04-step 3-dataset placement for training.mp4

00:50

06.05-step 4-train test metafiles.mp4

01:43

06.06-step 5-training yolov4.mp4

04:25

06.07-trained yolov4 execution on image and video for mask detection.mp4

02:24

06.08-activity train on your own dataset.mp4

00:46

06.09-when to stop training.mp4

03:52

06.10-summary-key takeaways.mp4

00:37

07.01-introduction to object detection with pyqt.mp4

01:06

07.02-installing pyqt.mp4

01:37

07.03-gui layout using pyqt designer.mp4

05:45

07.04-integrating pyqt with yolov4.mp4

03:11

07.05-code explanation.mp4

04:52

07.06-adding gui widgets-counting objects.mp4

03:58

07.07-adding widgets-slider threshold.mp4

03:43

07.08-adding widgets-class filter using checkbox widget.mp4

04:05

07.09-adding widgets-real-time live plot graph widget.mp4

04:05

07.10-social distancing in pyqt activity.mp4

02:28

07.11-conclusion.mp4

00:41

9781803236780 Code.zip

Description

This course is a perfect fit if you want to natively train your own YOLOv4 neural network. You’ll start off with a gentle introduction to the world of computer vision with YOLOv4, install darknet, and build libraries for YOLOv4 to implement YOLOv4 on images and videos in real-time.

You’ll even solve current and relevant real-world problems by building your own social distancing monitoring app and implementing vehicle tracking using the robust DeepSORT algorithm.

After that, you’ll learn more techniques and best practices/rules of how to take your Python implementations and develop GUIs for your YOLOv4 apps using PyQT.

Then, you’ll be labeling your own dataset from scratch, converting standard datasets into YOLOv4 format, amplifying your dataset 10x, and employing data augmentation to significantly increase the diversity of available data for training models, without collecting new data.

Finally, you’ll develop your own Mask Detection app to detect whether a person is wearing their mask and to flag an alert.

By the end of this course, you’d be able to implement and train your own custom CNNs with YOLOv4. It will help you in solving real-world problems, freelancing AI projects, getting that opportunity in AI, and tackling your research work by saving time and money. The world is your oyster; just start exploring the world once you have skills in AI.

All the resource files are uploaded on the GitHub repository at https://github.com/PacktPublishing/Full-YOLOv4-Pro-Course-Bundle

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Computer Vision

Ritesh Kanjee

Instructor's Courses

Augmented Startups have over 8 years experience in Printed Circuit Board (PCB) design as well in image processing and embedded control. Author Ritesh Kanjee has completed his Masters Degree in Electronic engineering and published two papers on the IEEE Database with one called "Vision-based adaptive Cruise Control using Pattern Matching" and the other called "A Three-Step Vehicle Detection Framework for Range Estimation Using a Single Camera" (on Google Scholar). His work was implemented in LabVIEW. He works as an embedded electronic engineer in defence research and has experience in FPGA design with programming in both VHDL and Verilog. He also has expertise in augmented reality and machine learning in which he shall be introducing new technologies through the medium of video

PacktPub

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Packt is a publishing company founded in 2003 headquartered in Birmingham, UK, with offices in Mumbai, India. Packt primarily publishes print and electronic books and videos relating to information technology, including programming, web design, data analysis and hardware.