Computer Vision: YOLO Custom Object Detection with Colab GPU

In this module, we will introduce the course content and outline the key concepts you'll be learning. This section will provide an overview, helping you understand the course structure and what to expect as you progress.

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In this module, we will dive into the basics of YOLO, a state-of-the-art object detection algorithm. You'll learn about its scope, importance, and why it's widely used in various computer vision applications.

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In this module, we will guide you through installing and setting up Anaconda, a popular platform for managing Python environments. You'll learn how to prepare your system for running the course projects.

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In this module, we will cover fundamental Python programming concepts, including flow control, data structures, and functions. These basics are crucial for developing and understanding the custom YOLO model later in the course.

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In this module, we will walk you through the installation of the OpenCV library, a key tool for image processing and computer vision. You'll ensure your environment is ready for the practical tasks ahead.

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In this module, we will introduce Convolutional Neural Networks (CNNs), the backbone of many modern computer vision applications. You'll gain insights into how CNNs function and their relevance to YOLO.

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In this module, we will guide you through using a pre-trained YOLO model to detect objects in images. You'll learn how to perform this task step-by-step.

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In this module, we will explore Non-Maximum Suppression (NMS), a technique used to improve object detection accuracy in YOLO. You'll see how NMS helps eliminate redundant detections.

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In this module, we will demonstrate how to perform real-time object detection using a webcam and a pre-trained YOLO model. You'll learn to adapt YOLO for live video feeds.

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In this module, we will show you how to apply YOLO to detect objects in pre-saved video files. You'll explore the nuances of video-based detection.

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In this module, we will introduce you to the process of custom training a YOLO model. You'll learn about the advantages of customizing YOLO for specific tasks.

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In this module, we will focus on setting up the Darknet environment, a key step in custom training YOLOv4 models. You'll download weights and prepare your system.

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In this module, we will guide you through the data collection process for training a YOLOv4 model. You'll learn how to gather and organize data effectively.

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In this module, we will cover the image labeling process, a critical step in preparing your dataset for YOLOv4 training. You'll use labeling tools to create annotations.

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In this module, we will explain the concept of train-test splitting, essential for evaluating the performance of your YOLOv4 model. You'll learn data balancing techniques.

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In this module, we will focus on the final stages of preparing your dataset for YOLOv4 training. You'll apply preprocessing techniques to ensure data readiness.

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In this module, we will demonstrate how to sync your data with Google Drive and connect it to Colab. You'll learn efficient file management techniques.

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In this module, we will guide you through compiling and testing Darknet, the framework used for YOLOv4 training. You'll learn to resolve setup issues.

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In this module, we will explore how to monitor and analyze the training progress of your YOLOv4 model. You'll use charts and metrics for assessment.

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In this module, we will cover the final steps of YOLOv4 training, including downloading and saving the model weights. You'll complete the training process.

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In this module, we will discuss the GPU usage limits in Google Colab and how they may affect your YOLOv4 training. You'll learn resource management strategies.

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In this module, we will guide you through upgrading OpenCV to ensure compatibility with YOLOv4. You'll learn to perform the upgrade and resolve issues.

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In this module, we will demonstrate how to use a pre-trained YOLOv4 model to detect objects in both images and videos. You'll explore practical applications.

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In this module, we will show you how to train a YOLOv4 model to detect coronavirus in images. You'll learn specialized detection techniques.

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In this module, we will focus on applying a custom-trained YOLOv4 model to detect coronavirus in videos. You'll adapt models for video analysis.

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In this module, we will present additional real-world case studies demonstrating the application of YOLO in different industries. You'll explore practical implementations.

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