YOLO11: Custom Object Detection & Web Apps in Python 2024

Description:

YOLO11 is the latest state-of-the-art object detection model from Ultralytics, surpassing previous versions in both speed and accuracy. Built upon the advancements of earlier YOLO models, YOLO11 introduces significant improvements in architecture and training, making it a versatile tool for various computer vision tasks.

YOLO11 models support a wide range of tasks, 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.