Udemy – YOLOv8 & YOLO11: Custom Object Detection & Web Apps 2025 2024-11

Udemy – YOLOv8 & YOLO11: Custom Object Detection & Web Apps 2025 2024-11 Downloadly IRSpace

Aug 17, 2025 - 02:36 Updated: Aug 19, 2025 - 14:32

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Udemy – YOLOv8 & YOLO11: Custom Object Detection & Web Apps 2025 2024-11

YOLOv8 & YOLO11: Custom Object Detection & Web Apps 2025. This course focuses on training advanced YOLOv8 and YOLO11 models for object detection, pattern segmentation, and building web applications. With architectural and training improvements, these models offer high-performance capabilities in computer vision tasks such as object detection, segmentation, state estimation, and license plate recognition. The first part covers the basics of convolutional neural networks and RCNN models up to YOLOv8, then covers running the model on Windows and Google Colab, preparing custom datasets such as pothole and personal protective equipment (PPE) detection, and projects such as multi-object tracking with DeepSORT, traffic analysis, and pothole segmentation. It also covers integrating YOLOv8 with Flask to build web applications. The second part is dedicated to YOLO11, which includes new features, model evaluation, training on custom datasets, and projects such as advanced interception with Bot-SORT and ByteTrack. Finally, integrating YOLO11 with Flask and Streamlit for web application development is covered. This course is designed for those interested in computer vision and intelligent application development.

What you will learn

An introduction to YOLO and the basics of YOLOv8.
How to run YOLOv8 programs for object detection, segmentation, and classification.
Finding suitable datasets and annotating/labeling the data.
Training and fine-tuning the YOLOv8 model for custom projects.
Visualizing the performance of multi-object training and tracking with YOLOv8.
Carrying out numerous and advanced projects such as pothole detection, personal protective equipment, traffic analysis, and license plate recognition.
Integrating YOLOv8 with Flask for building web applications.
Familiarity with YOLO11 features and its applications in object detection, segmentation, state estimation, and image classification.
Training and fine-tuning YOLO11 models on custom datasets.
Developing Streamlit applications for object recognition with YOLO11.

This course is suitable for people who:

Are interested in computer vision.
They study computer vision and want to know how to use YOLO for object detection, sample segmentation, state estimation, and image classification.
They plan to build deep learning applications using computer vision.