Udemy
    •  
    •  
    •  
    •  
    •  
    •  
    •  
    •  
Turn what you know into an opportunity and reach millions around the world.
Learn More
Your cart is empty.
Keep shopping
Complete Machine Learning Project Using YOLOv9 From Scratch
Rating: 3.9 out of 5(82 ratings)
8,010 students

Complete Machine Learning Project Using YOLOv9 From Scratch

Learn Complete Machine Learning Project Using YOLOv9 Model , YOLOv9 Dataset , YOLOv9 Annotation
Created byARUNNACHALAM SHANMUGARAAJAN
Last updated 9/2024
English
English [Auto],

What you'll learn

  • Dive into the process of collecting and preparing a dataset for object detection.
  • Understand the process of training the model on your annotated dataset.
  • Learn how to evaluate the performance of your trained model using metrics like mAP (mean Average Precision).
  • Learn how to set up a Python environment with necessary libraries for machine learning.

Course content

1 section • 8 lectures • 30m total length

  • Introduction To Complete Machine Learning Project Using YOLOv91:13

    Explore how to set up a Roboflow project workspace, create and annotate a dataset, train and validate with YOLOv9, and run the project in PyCharm.

  • CREATE PROJECT WORKSPACE IN ROBOFLOW WEBSITE2:41

    Create a Roboflow project workspace for a fire detection project using yolo v9, choose the free plan, and set up instance segmentation with the annotation class fire.

  • CREATE DATASET FOR YOLOV9 PROJECT5:08

    Create a fire-detection dataset for a YOLOv9 project using Roboflow and Kaggle. Learn downloading, formatting as YOLO, and uploading for annotation readiness.

  • ANNOTATE DATASET FOR YOLOV9 PROJECT7:02

    Learn how to annotate a dataset for a YOLOv9 project using Roboflow, including bounding boxes, polygons, smart polygon, label assist, and dataset split for training and validation.

  • TRAIN DATASET WITH YOLOV9 MODEL2:53

    Train your dataset with the YOLOv9 model on Roboflow, export and download the data, and start training with three credits to monitor mean average precision across epochs.

  • VALIDATE DATASET USING TRAINED YOLOV9 MODEL2:31

    Validate your dataset using a trained YOLOv9 model on Roboflow, verify a mean average precision of 99.5%, and test fire images by dragging and dropping samples with adjustable thresholds.

  • DEPLOYEMENT OPTIONS IN ROBOFLOW WEBSITE3:05

    Explore roboflow deployment options: run in PyCharm via image and code for multiple languages, use video method, download packages, and deploy on cloud docker or native python and edge devices.

  • EXECUTE PROJECT IN PYCHARM IDE AND CONCLUSION6:25

    Execute the project in PyCharm by creating folder, Python file, and installing Roboflow and inference, then run YOLOv9 to predict a JPG with a confidence score and save output.

  • ROBOFLOW WEBSITE MCQ
  • YOLO MODEL MCQ

Requirements

  • Basic knowledge of Python programming.
  • Familiarity with machine learning concepts

Description

Course Title: Complete Machine Learning Project Using YOLOv9 and Roboflow

Course Description:

Welcome to the "Complete Machine Learning Project Using YOLOv9 and Roboflow" course! In this hands-on and practical course, you will dive into the world of machine learning and object detection using the powerful YOLOv9 algorithm, along with the efficient data management platform, Roboflow. Whether you're a beginner in machine learning or an experienced practitioner, this course will guide you through the process of building a robust object detection model from scratch.

What You Will Learn:

  1. Introduction to Object Detection:

    • Understand the fundamentals of object detection in machine learning.

    • Explore the significance of YOLOv9 as a state-of-the-art object detection algorithm.

  2. Setting Up Your Machine Learning Environment:

    • Learn how to set up a Python environment with necessary libraries for machine learning.

    • Install and configure the required tools for using YOLOv9 and Roboflow.

  3. Data Collection and Annotation:

    • Dive into the process of collecting and preparing a dataset for object detection.

    • Understand the importance of accurate annotation using tools like Roboflow.

  4. Introduction to YOLOv9:

    • Learn about the architecture and principles behind the YOLOv9 algorithm.

    • Explore the advantages of YOLOv9 for real-time object detection tasks.

  5. Training Your Object Detection Model:

    • Implement training scripts and configurations for YOLOv9 using PyTorch.

    • Understand the process of training the model on your annotated dataset.

  6. Fine-Tuning and Model Optimization:

    • Explore techniques for fine-tuning the YOLOv9 model for improved accuracy.

    • Optimize model hyperparameters and training strategies for efficient convergence.

  7. Evaluation and Model Testing:

    • Learn how to evaluate the performance of your trained model using metrics like mAP (mean Average Precision).

    • Test the model on unseen data to assess its generalization capabilities.


Why Enroll:

  • Hands-On Learning Experience: Engage in a complete machine learning project, from data collection to model deployment.

  • Practical Skills Development: Apply YOLOv9 algorithms to solve real-world object detection challenges.

  • Career Advancement: Gain valuable experience in machine learning and computer vision with a project-based approach.

Embark on this exciting journey into the world of machine learning and object detection with YOLOv9 and Roboflow. By the end of this course, you'll have the skills and confidence to build and deploy your own object detection models for a variety of applications. Enroll now and take your machine learning projects to the next level!

Who this course is for:

  • Machine learning enthusiasts eager to learn about object detection and YOLOv9.
  • Data scientists and developers interested in implementing real-time object detection models.

Report abuse