YOLOv7 Object Detection Course
- Development
- Dec 31, 2024

YOLOv7 Object Detection Course, available at $54.99, has an average rating of 4.05, with 22 lectures, based on 17 reviews, and has 132 subscribers.
You will learn about Fundamentals of YOLOv7 Training YOLOv7 in Custom Dataset Deploying&Testing the Model in YOLOv7 Data Processing, Segmentation, Data Collection, and Essential Prerequisites in YOLOv7 This course is ideal for individuals who are Everyone who is interested in Computer Vision or Machine Learning. or Want to learn the latest YOLO version or Build Deep learning App with Computer Vision It is particularly useful for Everyone who is interested in Computer Vision or Machine Learning. or Want to learn the latest YOLO version or Build Deep learning App with Computer Vision.
Enroll now: YOLOv7 Object Detection Course
Summary
Title: YOLOv7 Object Detection Course
Price: $54.99
Average Rating: 4.05
Number of Lectures: 22
Number of Published Lectures: 22
Number of Curriculum Items: 22
Number of Published Curriculum Objects: 22
Original Price: $19.99
Quality Status: approved
Status: Live
What You Will Learn
Who Should Attend
Target Audiences
==Price going up to $29 on 5th October 2022 due to new content==
Object-detection technology is widely used as the backend of many applications in the industry, including desktop and web applications. Also, it’s a backbone for many computer vision tasks, which include object segmentation, object tracking, object classification, object counting, etc. The YOLO (You Only Look Once) model is a single-stage object detector. Image frames are featured through a backbone, features are combined and mixed in the neck, and then they are passed along to the head of the network where YOLO predicts the bounding box locations, the classes of the bounding boxes, and the objectness of the bounding boxes. With its amazing characteristics, YOLOv7 is a real-time object detector that is now transforming computer vision.
In comparison to its earlier iterations, the official YOLOv7 offers incredible speed and precision. YOLOv7 weights are trained without pre-trained weights using the COCO dataset from Microsoft. With this full-stack object detection course, you will learn to code YOLOv7 from the basics, training your custom YOLOv7, multi-object tracking system, Flask integrations, various segmentation, and object detection feature to its most practical applications. You will also have access to the most delicate details of this state-of-the-art system.
In this course, we will share the first two modules of our full-stack course.
Module 1 – Getting Started with YOLOv7
Module 2 – Training YOLOv7 on Custom Dataset
This course is highly recommended to anyone interested in computer vision!
Course Curriculum
Chapter 1: Module 1 – Introduction to YOLOv7
Lecture 1: Lecture 1.1 Introduction to YOLOv7
Lecture 2: Lecture 1.2 YOLOv7 Research Paper
Lecture 3: Lecture 1.3 Run YOLOv7 on Ubuntu
Lecture 4: Lecture 1.4 Run YOLOv7 on Windows 10/11
Lecture 5: Lecture 1.5.1 YOLOv7 Object Detection on Images, Video & WebCam in Google Colab
Lecture 6: Lecture 1.5.2 Training YOLOv7 in Colab
Chapter 2: Module 2 – Training Custom YOLOv7
Lecture 1: Lecture 2.1 Training Process of YOLOv7
Lecture 2: Lecture 2.2.1 Custom Dataset PPE Detection part 1
Lecture 3: Lecture 2.2.2 Custom Dataset PPE Detection part 2
Lecture 4: Lecture 2.3.1 Data Collection Part 1
Lecture 5: Lecture 2.3.2 Data Collection Part 2
Lecture 6: Lecture 2.3.3 Data Collection Part 3
Lecture 7: Lecture 2.4 Data Annotation
Lecture 8: Lecture 2.5 Dataset Bias & Health
Lecture 9: Lecture 2.6 Data Pre-processing
Lecture 10: Lecture 2.7 Data Augmentation
Lecture 11: Lecture 2.8.1 Roboflow Train Part 1
Lecture 12: Lecture 2.8.2 Roboflow Train Part 2
Lecture 13: Lecture 2.9 Tuning Training Parameters
Lecture 14: Lecture 2.10 Deploy the Model
Lecture 15: Lecture 2.11 Testing the Model
Lecture 16: Bonus Lecture – Continue Learning YOLOv7
Instructors

Augmented Startups
M(Eng) AI Instructor 100k+ Subs on YouTube & 60k+ students
Rating Distribution
Frequently Asked Questions
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