Course Information
Registration period
Year-round Recruitment
Course Level
Study Mode
Duration
9 Hour(s) 25 Minute(s)
Language
English
Taught by
Muhammad Moin
Rating
2 views
Course Overview
Learn Custom Object Detection, Tracking, and Pose Estimation with YOLO11 & YOLOv12, and Build Web Apps
YOLO11 and YOLOv12 are the latest state-of-the-art computer vision model architectures, surpassing previous versions in both speed and accuracy. Building on the advancements of earlier YOLO models, YOLO11 and YOLOv12 introduce significant architectural and training enhancements, making them versatile tools for a variety of computer vision tasks..
These models support a wide range of applications, including object detection, instance segmentation, image classification, pose estimation, and oriented object detection (OBB).
In this course, you will learn:
What's New in Ultralytics YOLO11.
How to use Ultralytics YOLO11 for Object Detection, Instance Segmentation, Pose Estimation, and Image Classification.
Running Object Detection, Instance Segmentation Pose Estimation and Image Classification with YOLO11 on Windows/Linux.
Evaluating YOLO11 Model Performance: Testing and Analysis
Training a YOLO11 Object Detection Model on a Custom Dataset in Google Colab for Personal Protective Equipment (PPE) Detection.
Step-by-Step Guide: YOLO11 Object Detection on Custom Datasets on Windows/Linux.
Training YOLO11 Instance Segmentation on Custom Datasets for Pothole Detection.
Fine-Tuning YOLO11 Pose Estimation for Human Activity Recognition.
Fine-Tuning YOLO11 Image Classification for Plant Classification.
Multi-Object Tracking with Bot-SORT and ByteTrack Algorithms.
License Plate Detection & Recognition using YOLO11 and EasyOCR.
Integrating YOLO11 with Flask to Build a Web App.
Creating a Streamlit Web App for Object Detection with YOLO11.
Car and License Plate Detection & Recognition with YOLO11 and PaddleOCR
Introduction to YOLOv12.
How to use YOLOv12 for Object Detection.
Fine-Tune YOLOv12 Object Detection Model on Custom Dataset for PPE Detection.
Course Content
- 10 section(s)
- 18 lecture(s)
- Section 1 YOLO11: New Object Detection Model
- Section 2 Non Maximum Suppression & Mean Average Precision
- Section 3 YOLO11 Implementation | Google Colab
- Section 4 YOLO11 Implementation | Windows & Linux
- Section 5 Evaluating YOLO11 Model Performance: Testing and Analysis
- Section 6 Training Custom YOLO11
- Section 7 Multi-Object Tracking with Ultralytics YOLO11
- Section 8 Train YOLO11 Instance Segmentation Model on a Custom Dataset
- Section 9 Image Classification with YOLO11 on a Custom Dataset
- Section 10 Human Activity Recognition with YOLO11: Fine-Tune YOLO11 Pose Estimation Model
What You’ll Learn
- Object Detection, Instance Segmentation, Pose Estimation, and Image Classification with YOLO11 and YOLOv12
- Training and Fine-Tuning YOLO11 and YOLOv12 Models on Custom Datasets
- Multi-Object Tracking with Ultralytics YOLO11 and YOLOv12
- Develop a Streamlit Application for Object Detection with YOLO11 and YOLOv12
- Object Detection in the Browser using YOLO11/YOLOv12 and Flask
Skills covered in this course
Reviews
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PPrasad tushar kalekar
well explained, with nice and accurate speed, got so much to learn.
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EEmiliano Roberti
Very useful and enjoyed doing the training
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TTom Christian
Few of the commands are explained, so it would be impossible for me to duplicate the instructors results or to develop my own code for doing what I want to do. No real instruction so far. Maybe there's more instruction later.
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AAnna
The course provides a good understanding of YOLO11, along with some exciting projects are also created as well
