Build a Custom Object Detector in Live Video using TensorFlow
Intro - Custom Object Detection in Live Video
with nitinsethi|4 years experience
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Intro - Custom Object Detection in Live Video
Oct 10, 2018 | 1:46 PM1:30
Session 1.1 Downloading required dependencies
Oct 11, 2018 | 12:00 AM10:23
Session 1.2 Compiling
Oct 11, 2018 | 12:02 AM5:26
Session 1.3 Jupyter notebook
Oct 11, 2018 | 12:03 AM1:39
Session 1.4 Running our first object detection
Oct 11, 2018 | 12:04 AM5:42
Session 2 Detection in Live Video
Oct 11, 2018 | 1:29 AM19:47
Session 3.1 Introduction to Data Augmentation
Oct 11, 2018 | 2:01 AM24:34
Session 3.2 Data Augmentation
Oct 11, 2018 | 2:02 AM7:21
Session 3.3 Bounding boxes setup
Oct 11, 2018 | 2:03 AM7:55
Session 3.4 Bounding boxes
Oct 11, 2018 | 2:04 AM11:6
Session 4.1 Convert data into array
Oct 11, 2018 | 3:01 AM30:21
Session 4.2.1 Convert data into csv files
Oct 11, 2018 | 3:02 AM8:25
Session 4.2.2 Convert data into csv files
Oct 11, 2018 | 3:02 AM5:46
Session 4.3.1 Create tf records
Oct 11, 2018 | 3:03 AM17:35
Session 4.3.2 Create tf records
Oct 11, 2018 | 3:03 AM3:33
Session 5.1 Setting up config files
Oct 11, 2018 | 4:01 AM5:55
Session 5.2.1 Training
Oct 11, 2018 | 4:02 AM4:44
Session 5.2.2 Training
Oct 11, 2018 | 4:02 AM25
Session 5.3 How to view tensorboard
Oct 11, 2018 | 4:03 AM2:3
Session 5.4 Custom Object Detection in Live Video
Oct 11, 2018 | 4:04 AM18:5
Build a Custom Object Detector in Live Video using TensorFlow
- English
- Artificial Intelligence
Computer Vision- Project length: 3h 12m
Ever wanted to make something that could detect an object of your choice? And if I told you that it could detect that even in live videos. Let us bring that imagination to reality with this project.
Overview
Introduction
This tutorial will cover all the details (resources, tools, languages etc) that are necessary to build a complete and operational custom object detector for a live video* You will be guided through all the steps and concepts, starting from the basic ones like setting up the right tools and frameworks to the more advanced topics related to the development. And ultimately you will be able to detect your own objects without any difficulty.
What are the requirements?
- Python basics
- Basic neural network concepts
- And the most important is: a desire to learn
What is the target audience?
- Learners who want to enhance their knowledge in computer vision.
- Entrepreneurs who want to employ object detection in their tasks.
- This course will help the students who are doing their final projects
Project Outline
Session 1: Installation
- Session 1.1 Downloading required dependencies
- Session 1.2 Compiling
- Session 1.3 Jupyter notebook
- Session 1.4 Running our first object detection
Session 2: Detection in live Video
- Detection in live video
Session 3: Data Preparation
- Session 3.1 Data Augmentation Introduction
- Session 3.2 Data Augmentation
- Session 3.4 Bounding boxes setup
- Session 3.5 Bounding boxes
Session 4: Create tf Records
- Session 4.1: Convert data into array
- Session 4.2.1: Convert data into csv files
- Session 4.2.2: Convert data into csv files 2
- Session 4.3.1: Create tf records 1
- Session 4.3.2: Create tf records 2
Session 5: Custom Object Detection in Live Video
- Session 5.1: Setting up config files
- Session 5.2.1: Training
- Session 5.2.2: Training 2
- Session 5.3: How to view tensorboard
- Session 5.4: Custom Object Detection in Live Video