Principles & Practices of Computer Vision

  • ITAI05
  • Classroom
  • Intermediate
  • Thai | 0
AI & Data Technology
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A training workshop on the design and implementation of computer vision applications for object
detection and recognition, using modern deep learning tools and technologies

Course description

Time
Days :
4 Day(s)
Duration :
24 Hour(s)
Time :
09:00:00 - 16:00:00
Training Date :
12 July 2022 - 15 July 2022
Status :
Class end / Close
Instructor
Name :
avatar
Mr.Tapanan Yeophantong

Assistant Dean for External Affairs
Language :
Thai
Venue
Venue :
ห้องอบรม ชั้น 3 อาคารซอฟต์แวร์พาร์ค
Type :
Classroom
Principles & Practices of Computer Vision

A training workshop on the design and implementation of computer vision applications for object
detection and recognition, using modern deep learning tools and technologies. Topics include an
introduction to object detection and recognition, using R-CNN and the inception models, SSD architecture
and MobileNet, and approaches for training deep learning models for object classification.

Pre-requisite(s) : Computer Vision & Deep Learning (or equivalent)

Lesson Plan
Lesson 1 Introduction to Object Detection & Recognition (6 hours)
  1.1. Overview of object detection tasks & pipelines
  1.2. Object detection using YOLO
  1.3. Overview of object recognition
  1.4. Example: Face recognition using FaceNet
Lesson 2 R-CNN & the Inception Models (6 hours)
  2.1. Understanding R-CNN family of algorithms
  2.2. Object detection using R-CNN & pre-trained models
  2.3. Understanding Inception models
  2.4. Training an Inception model for object recognition
Lesson 3 SSD Architecture & MobileNet Models (6 hours)
  3.1. Overview of SSD architecture
  3.2. Object detection using SSD: OpenCV’s Caffe Model example
  3.3. Understanding MobileNet models
  3.4. Training MobileNet for object recognition
Lesson 4 Object Classification (6 hours)
  4.1. Overview of object classification problems
  4.2. Importance of feature extraction: feature embeddings
  4.3. Training a deep learning model for feature extraction
  4.4. Pipelining object detection & classification for real-world applications

Payment can be made by:

  1. Cash or Credit Card or Bank Cheque payable to
    สำนักงานพัฒนาวิทยาศาสตร์และเทคโนโลยีแห่งชาติ or National Science and Technology Development Agency
    (a post-dated cheque is not accepted) on the first day of the service or within the last day of the service.

  2. Account transfer and send the proof of the payment (the deposit slip) via email tes@swpark.or.th

    • ธนาคารกรุงเทพ สาขาอุทยานวิทยาศาสตร์
      Saving Account Number: 080-0-00001-0
      Account Name: สำนักงานพัฒนาวิทยาศาสตร์และเทคโนโลยีแห่งชาติ

    • ธนาคารกรุงไทย สาขาตลาดไท
      Saving Account Number: 152-1-32668-1
      Account Name: สำนักงานพัฒนาวิทยาศาสตร์และเทคโนโลยีแห่งชาติ

Notes:

  • Withholding tax (3%) is exempt.
  • Should you need to withdraw, you must send the notice of the withdrawal in writing no later than 7 working days before the commencement date. The cancellation less than 7 days will be subject to a fine of 40% of the fee.
  • Software Park Thailand reserves the rights to cancel courses due to unforeseen circumstances.

Contact Person

For more information, contact our course coordinator on:

Tel: +66-2583-9992 Ext. 81442-81443

Email: tes@swpark.or.th

You are encouraged to use the course schedule as a guide to plan your training. The schedule is accessible at www.swpark.or.th for more information. 

8,800 THB .

ราคา: 9,416 บาท (รวมภาษีมูลค่าเพิ่ม 7% แล้ว และได้รับการยกเว้นไม่ต้องหักภาษี ณ ที่จ่าย 3%)

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Course Detail :
Days :
4 Day(s)
Duration :
24 Hour(s)
Time :
09:00:00 - 16:00:00
Training Date :
12 July 2022 - 15 July 2022
Status :
Class end / Close
Instructor info
avatar
Mr.Tapanan Yeophantong

Assistant Dean for External Affairs