Perform Real-Time Object Detection with YOLOv3

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在此指导项目中,您将:

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
Intermediate中级
Cloud无需下载
Video分屏视频
Comment Dots英语(English)
Laptop仅限桌面

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.

您要培养的技能

Deep LearningOpencvYOLOObject DetectionComputer Vision

分步进行学习

在与您的工作区一起在分屏中播放的视频中,您的授课教师将指导您完成每个步骤:

  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

指导项目工作原理

您的工作空间就是浏览器中的云桌面,无需下载

在分屏视频中,您的授课教师会为您提供分步指导

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