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Temario del curso

Introduction

Overview of YOLO Pre-trained Models Features and Architecture

  • The YOLO Algorithm
  • Regression-based Algorithms for Object Detection
  • How is YOLO Different from RCNN?

Utilizing the Appropriate YOLO Variant

  • Features and Architecture of YOLOv1-v2
  • Features and Architecture of YOLOv3-v4

Installing and Configuring the IDE for YOLO Implementations

  • The Darknet Implementation
  • The PyTorch and Keras Implementations
  • Executing the OpenCV and NumPy

Overview of Object Detection Using YOLO Pre-trained Models

Building and Customizing Python Command-Line Applications

  • Labeling Images Using the YOLO Framework
  • Image Classification Based on a Dataset

Detecting Objects in Images with YOLO Implementations

  • How do Bounding Boxes Work?
  • How Accurate is YOLO for Instance Segmentation?
  • Parsing the Command-line Arguments

Extracting the YOLO Class Labels, Coordinates, and Dimensions

Displaying the Resulting Images

Detecting Objects in Video Streams with YOLO Implementations

  • How is it Different from Basic Image Processing?

Training and Testing the YOLO Implementations on a Framework

Troubleshooting and Debugging

Summary and Conclusion

Requerimientos

  • Experiencia en programación con Python 3.x
  • Conocimientos básicos de cualquier IDE de Python
  • Experiencia con argparse de Python y argumentos de línea de comandos
  • Comprensión de bibliotecas de visión por computadora y aprendizaje automático
  • Conocimiento de algoritmos fundamentales de detección de objetos

Público objetivo

  • Desarrolladores backend
  • Científicos de datos
 7 Horas

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