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
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.
在與您的工作區一起在分屏中播放的視頻中,您的授課教師將指導您完成每個步驟:
Introduction and Overview
Explore the Dataset
Setup Training and Validation Data Generators
Create a Convolutional Neural Network (CNN) Model
Train and Evaluate Model
Save and Serialize Model as JSON String
Create a Flask App to Serve Predictions
Create a Model Class to Output Predictions
Design an HTML Template for the Flask App
Use Model to Recognize Facial Expressions in Videos
您的工作空間就是瀏覽器中的雲桌面,無需下載
在分屏視頻中,您的授課教師會為您提供分步指導
This Project course is really helpful for me. I was trying to implement yolo in my project and this course helped how to implement YOLO from scratch.
It is very much helpful for my new project call face recognition.if u are interested in doing computer vision types work this course may help you.
This course was awesome and clear my many doubts .In this course I learnt a lot about real time object detection and use of yolov3. Thank you
It will be great if setting up the development environment would be great. But the course is exactly what i was looking for
如果我購買指導項目,會得到什麼?
購買指導項目後,您將獲得完成指導項目所需的一切,包括通過Web 瀏覽器訪問云桌面工作空間,工作空間中包含您需要了解的文件和軟件,以及特定領域的專家提供的分步視頻說明。
指導項目可在台式設備和移動設備上學習嗎?
由於您的工作空間包含適合筆記本電腦或台式計算機使用的雲桌面,因此指導項目不在移動設備上提供。
指導項目的講師是誰?
指導項目講師是特定領域的專家,他們在項目的技能、工具或領域方面經驗豐富,並且熱衷於分享自己的知識以影響全球數百萬的學生。
我能在完成指導項目後從中下載作品嗎?
您可以從指導項目中下載並保留您創建的任何文件。為此,您可以在訪問云桌面時使用‘文件瀏覽器’功能。
我能夠退款嗎?退款政策是如何規定的?
指導項目不符合退款條件。 請查看我們完整的退款政策。
有助學金嗎?
指導項目不提供助學金。
我能旁聽指導項目並免費觀看視頻部分嗎?
指導項目不支持旁聽。
我需要具備多少經驗才能做這個指導項目?
您可在頁面頂部點按此指導項目的經驗級別,查看任何知識先決條件。對於指導項目的每個級別,您的講師會逐步為您提供指導。
我能直接通過 Web 瀏覽器來完成此指導項目,而不必安裝特殊軟件嗎?
是,您可以在瀏覽器的雲桌面中獲得完成指導項目所需的一切。
指導項目的學習體驗如何?
您可以直接在瀏覽器中於分屏環境下完成任務,以此從做中學。在屏幕的左側,您將在工作空間中完成任務。在屏幕的右側,您將看到有講師逐步指導您完成項目。
還有其他問題嗎?請訪問 學生幫助中心。