Packt YOLO v3 Robust Deep Learning Object Detection

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Packt YOLO v3 Robust Deep Learning Object Detection

English | Size: 210.73 MB

Category: Tutorial

When we first got started in Deep Learning particularly in Computer Vision, we were really excited at the possibilities of this technology to help people. The only problem is that if you are just getting started learning about AI Object Detection, you may encounter some of the following common obstacles along the way:

Labeling dataset is quite tedious and cumbersome,

Annotation formats between various object detection models are quite different.

Labels may get corrupt with free annotation tools,

Unclear instructions on how to train models - causes a lot of wasted time during trial and error.

Duplicate images are a headache to manage.

This got us searching for a better way to manage the object detection workflow, that will not only help us better manage the object detection process but will also improve our time to market.

Amongst the possible solutions we arrived at using Supervisely which is free Object Detection Workflow Tool, that can help you:

 

Use AI to annotate your dataset,

Annotation for one dataset can be used for other models (No need for any conversion) - Yolo, SSD, FR-CNN, Inception etc,

Robust and Fast Annotation and Data Augmentation,

Supervisely handles duplicate images.

You can Train your AI Models Online (for free) from anywhere in the world, once you've set up your Deep Learning Cluster.

So as you can see, that the features mentioned above can save you a tremendous amount of time.

 

Another exquisit release

 

Packt.YOLO.v3.Robust.Deep.Learning.Object.Detection.in.1.Hour-XQZT

 

Title: YOLO v3 - Robust Deep Learning Object Detection in 1 Hour

Publisher: Packt

Size: 211M (220967712 B)

Files: 5F

Date: 12/17/2018

Course #: 9781838558789

Type: N/A

Published: Packt Subscription

Modified: N/A

URL: www.packtpub.com/application-development/yolo-v3-robust-deep-learning-object-detection-1-hour-video

Author: Ritesh Kanjee

Duration:

Skill: N/A

Exer/Code:

 

Installation:

Unpack that shit, run that shit

 

Notes:

Labeling dataset is quite tedious and cumbersome

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