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YOLO Object Detection Using OpenCV And Python || Python Project
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YOLO Object Detection Using OpenCV And Python Complete Project || YOLO Object Detection from Scratch || Computer Vision Projects.
🔥 For Project resources, Click The Link below :
In this lesson we will learn YOLO Object Detection Using OpenCV And Python Complete Project.
Real - Time Object Detection
In this project we are building real time object detection where we will detect the object in real time. In this course course we trained 20 different object those are 'person', 'car', 'chair', 'bottle', 'pottedplant', 'bird', 'dog', 'sofa', 'bicycle', 'horse', 'boat', 'motorbike', 'cat', 'tvmonitor', 'cow', 'sheep', 'aeroplane', 'train', 'diningtable' and 'bus'.
In order to achieve this we will follow the Data Science SDLC (Software Development Life Cycle). First we will start by gathering the data and requirements. After that based on the requirements we will create the pipeline to prepare the data.
Once our data is ready then we will train our YOLOv5 model in Google Collab GPU environment. Finally we will extract the detection from the model.
Welcome to this comprehensive tutorial on YOLO (You Only Look Once) Object Detection using OpenCV and Python. In this video, you will learn how to implement the state-of-the-art deep learning model YOLO to detect objects in images and videos.
Here's what you will learn in this tutorial:
Introduction to YOLO object detection
Installing OpenCV and other required libraries
Preparing the dataset and annotations for training
Training the YOLO model using Darknet
Fine-tuning the pre-trained YOLO model for better accuracy
Testing the trained model on images and videos
Visualizing the results and tuning the parameters for better performance
Whether you're a beginner or an advanced user, this tutorial will guide you through every step of the process, from setting up the environment to training the model and testing it on real-world data.
At the end of this video, you will have a deep understanding of how YOLO works and how you can apply it to solve real-world problems. So, if you're ready to take your computer vision skills to the next level, hit the play button and let's get started!
Don't forget to like, share, and subscribe to our channel for more tutorials on computer vision, machine learning, and deep learning.
Thank you for watching!
🔥 For Project resources, Click The Link below :
In this lesson we will learn YOLO Object Detection Using OpenCV And Python Complete Project.
Real - Time Object Detection
In this project we are building real time object detection where we will detect the object in real time. In this course course we trained 20 different object those are 'person', 'car', 'chair', 'bottle', 'pottedplant', 'bird', 'dog', 'sofa', 'bicycle', 'horse', 'boat', 'motorbike', 'cat', 'tvmonitor', 'cow', 'sheep', 'aeroplane', 'train', 'diningtable' and 'bus'.
In order to achieve this we will follow the Data Science SDLC (Software Development Life Cycle). First we will start by gathering the data and requirements. After that based on the requirements we will create the pipeline to prepare the data.
Once our data is ready then we will train our YOLOv5 model in Google Collab GPU environment. Finally we will extract the detection from the model.
Welcome to this comprehensive tutorial on YOLO (You Only Look Once) Object Detection using OpenCV and Python. In this video, you will learn how to implement the state-of-the-art deep learning model YOLO to detect objects in images and videos.
Here's what you will learn in this tutorial:
Introduction to YOLO object detection
Installing OpenCV and other required libraries
Preparing the dataset and annotations for training
Training the YOLO model using Darknet
Fine-tuning the pre-trained YOLO model for better accuracy
Testing the trained model on images and videos
Visualizing the results and tuning the parameters for better performance
Whether you're a beginner or an advanced user, this tutorial will guide you through every step of the process, from setting up the environment to training the model and testing it on real-world data.
At the end of this video, you will have a deep understanding of how YOLO works and how you can apply it to solve real-world problems. So, if you're ready to take your computer vision skills to the next level, hit the play button and let's get started!
Don't forget to like, share, and subscribe to our channel for more tutorials on computer vision, machine learning, and deep learning.
Thank you for watching!
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