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Quick answer

Object Detection using OAK-D with Custom Dataset is an open source machine vision project from Hackster.io, indexed by ZouIC. This project is to showcase object detection using OAK-D, which is trained in SSD-MobileNet-V2 model with custom dataset.

Source
Hackster.io
Focus
machine vision
Author
JuanYi
Published
2021-12-12

Common questions

What does Object Detection using OAK-D with Custom Dataset do?

This project is to showcase object detection using OAK-D, which is trained in SSD-MobileNet-V2 model with custom dataset.

Where does Object Detection using OAK-D with Custom Dataset come from?

ZouIC indexes it from Hackster.io, original page at https://www.hackster.io/JuanYi/object-detection-using-oak-d-with-custom-dataset-ecc7f0.

What stack does Object Detection using OAK-D with Custom Dataset use?

tags Intel, showcase, intermediate.

When was Object Detection using OAK-D with Custom Dataset published?

The source page lists 2021-12-12.

Object Detection using OAK-D with Custom Dataset
Hackster.io All from this source License per the original page vision

Indexed by ZouIC from the source platform public page, for developer reference only. Copyright and licensing stay with the author; get code and files from the original platform.

Object Detection using OAK-D with Custom Dataset

Published 2021-12-12

This project is to showcase object detection using OAK-D, which is trained in SSD-MobileNet-V2 model with custom dataset.

Technical Specifications

Source platform
Hackster.io
Listing note
Metadata fetched from the public platform page; copyright belongs to the original author.
Difficulty
intermediate
content type
showcase

Related resources and purchase links

Statistics

respects
4
views
5064

Project Info

Source PlatformHackster.io
Categoryvision
LicenseLicense per the original page
Views5064
Likes4
Discussions0

Author Info

Citation Note

This content is sourced from Hackster.io Platform,All rights reserved by the original author. This platform only provides indexing and summaries. For detailed content, please visit the original link.

License per the original page
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