Perform Real-Time Object Detection with YOLOv3

3.7
별점
238개의 평가
제공자:
Coursera Project Network
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학습자는 이 안내 프로젝트에서 다음을 수행하게 됩니다.

Perform real-time object detection with YOLOv3

Use pre-trained models to perform real-time and passive inference with a GPU

Use OpenCV to manipulate video data and develop a command line application with Python for inference

Clock1.5 hours
Intermediate중급
Cloud다운로드 필요 없음
Video분할 화면 동영상
Comment Dots영어
Laptop데스크톱 전용

In this 1-hour long project-based course, you will perform real-time object detection with YOLOv3: a state-of-the-art, real-time object detection system. Specifically, you will detect objects with the YOLO system using pre-trained models on a GPU-enabled workstation. To apply YOLO to videos and save the corresponding labelled videos, you will build a custom command-line application in Python that employs a pre-trained model to detect, localize, and classify objects. It will use OpenCV to read the video streams, draw bounding boxes around detected objects, label the objects along with confidence scores, and save the labelled videos to disk. This course runs on Coursera's hands-on project platform called Rhyme. On Rhyme, you do projects in a hands-on manner in your browser. You will get instant access to pre-configured cloud desktops containing all of the software and data you need for the project. Everything is already set up directly in your internet browser so you can just focus on learning. For this project, you’ll get instant access to a cloud desktop with Python, Jupyter, and Keras pre-installed. Notes: - You will be able to access the cloud desktop 5 times. However, you will be able to access instructions videos as many times as you want. - This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

개발할 기술

Deep LearningOpencvYOLOObject DetectionComputer Vision

단계별 학습

작업 영역이 있는 분할 화면으로 재생되는 동영상에서 강사는 다음을 단계별로 안내합니다.

  1. Introduction and Overview

  2. Explore the Dataset

  3. Setup Training and Validation Data Generators

  4. Create a Convolutional Neural Network (CNN) Model

  5. Train and Evaluate Model

  6. Save and Serialize Model as JSON String

  7. Create a Flask App to Serve Predictions

  8. Create a Model Class to Output Predictions

  9. Design an HTML Template for the Flask App

  10. Use Model to Recognize Facial Expressions in Videos

안내형 프로젝트 진행 방식

작업 영역은 브라우저에 바로 로드되는 클라우드 데스크톱으로, 다운로드할 필요가 없습니다.

분할 화면 동영상에서 강사가 프로젝트를 단계별로 안내해 줍니다.

검토

PERFORM REAL-TIME OBJECT DETECTION WITH YOLOV3의 최상위 리뷰

모든 리뷰 보기

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자주 묻는 질문

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