Yolo11: Custom Object Detection & Web Apps In Python 2024

Learn Custom Object Detection, Tracking and Pose Estimation with YOLO11, and Build Web Apps with Flask and Streamlit

What you'll learn

Object Detection, Instance Segmentation, Pose Estimation and Image Classification using YOLO11

Training / Fine-Tuning YOLO11 Models on Custom Dataset

Multi-Object Tracking with Ultralytics YOLO

Develop Streamlit Application for Object Detection with YOLO11.

Object Detection in the Browser using YOLO11 and Flask

Requirements

Mac / Windows / Linux - all operating systems work with this course!

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 AnalysisTraining 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.

Overview

Section 1: YOLO11 Implementation | Google Colab

Lecture 1 Object Detection, Instance Segmentation, Pose Estimation & Image Classification

Section 2: YOLO11 Implementation | Windows & Linux

Lecture 2 Object Detection, Instance Segmentation, Pose Estimation & Image Classification

Section 3: Evaluating YOLO11 Model Performance: Testing and Analysis

Lecture 3 Testing and Analyzing YOLO11 Model Performance

Section 4: Training Custom YOLO11

Lecture 4 YOLO11 Object Detection on Custom Dataset for PPE Detection.

Section 5: Multi-Object Tracking with Ultralytics YOLO11

Lecture 5 Multi-Object and Multithreaded Tracking Using Ultralytics YOLO11

Section 6: Train YOLO11 Instance Segmentation Model on a Custom Dataset

Lecture 6 Instance Segmentation using YOLO11 on a Custom Dataset

Section 7: Image Classification with YOLO11 on a Custom Dataset

Lecture 7 YOLO11 Image Classification on Custom Dataset

Section 8: Human Activity Recognition with YOLO11: Fine-Tune YOLO11 Pose Estimation Model

Lecture 8 Train YOLO11 Pose Estimation Model on a Custom Dataset

Section 9: License Plate Detection and Recognition using YOLO11 and EasyOCR

Lecture 9 Automatic Number Plate Recognition (ANPR) with Yolo11 and EasyOCR

Section 10: YOLO11 Streamlit Application

Lecture 10 Create a User-Friendly Interactive Interface for YOLO11 using Streamlit

Anyone who is interested in Computer Vision,Anyone who study Computer Vision and want to know how to use YOLO11 for Object Detection, Instance Segmentation, Pose Estimation and Image Classification,Anyone who aims to build Deep learning Apps with Computer Vision