Perform Real-Time Object Detection with YOLOv3

3.7
stars
249 ratings
Offered By
Coursera Project Network
7,541 already enrolled
In this Guided Project, you will:

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
IntermediateIntermediate
CloudNo download needed
VideoSplit-screen video
Comment DotsEnglish
LaptopDesktop only

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.

Skills you will develop

Deep LearningOpencvYOLOObject DetectionComputer Vision

Learn step-by-step

In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:

  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

How Guided Projects work

Your workspace is a cloud desktop right in your browser, no download required

In a split-screen video, your instructor guides you step-by-step

Reviews

TOP REVIEWS FROM PERFORM REAL-TIME OBJECT DETECTION WITH YOLOV3

View all reviews

Frequently asked questions

Frequently Asked Questions

More questions? Visit the Learner Help Center.