GSoc/2023/StatusReports/QuocHungTran: Difference between revisions
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===== (Week 1 - 2) ===== | ===== (Week 1 - 2) ===== | ||
In this phase, I focus mainly about offline analysis, this analysis aims to create a Deep learning pipeline for object detection model, including: | |||
- Constructing data sets (training dataset, validation dataset and testing dataset). | |||
- Preprocessing data, studying about construct of COCO dataset which is used for training dataset and validation dataset. | |||
- Research and create model pipeline for all YOLO version in python. | |||
- Evaluate performance of YOLO methode by considering some evaluated metrics. | |||
===== May 29 to June 11 (Week 1 - 2) - Experimentation on COCO dataset ===== | |||
'''DONE''' | |||
* Constructing data sets (training dataset, validation dataset and testing dataset). | |||
* Preprocessing data, studying about construct of COCO dataset which is used for training dataset and validation dataset. | |||
* Research and create model pipeline for all YOLO version in python. | |||
* Evaluate performance of YOLO methode by considering some evaluated metrics. | |||
'''TODO''' | |||
===== (Week 3 - 4) ===== | ===== (Week 3 - 4) ===== | ||
===== (Week 5 - 6) ===== | ===== (Week 5 - 6) ===== | ||
Revision as of 20:06, 9 June 2023
Add Automatic Tags Assignment Tools and Improve Face Recognition Engine for digiKam
digiKam is an advanced open-source digital photo management application that runs on Linux, Windows, and macOS. The application provides a comprehensive set of tools for importing, managing, editing, and sharing photos and raw files.
The goal of this project is to develop a deep learning model that can recognize various categories of objects, scenes, and events in digital photos, and generate corresponding keywords that can be stored in Digikam's database and assigned to each photo automatically. The model should be able to recognize objects such as animals, plants, and vehicles, scenes such as beaches, mountains, and cities,... The model should also be able to handle photos taken in various lighting conditions and from different angles.
Mentors : Gilles Caulier, Maik Qualmann, Thanh Trung Dinh
Project Proposal
Automatic Tags Assignment Tools and Improve Face Recognition Engine for digiKam Proposal
GitLab development branch
Contacts
Email: [email protected]
Github: quochungtran
Invent KDE: quochungtran
LinkedIn: https://www.linkedin.com/in/tran-quoc-hung-6362821b3/
Project goals
Links to Blogs and other writing
Main merge request
KDE repository for object detection and face recognition researching
Issue tracker
My blog for GSoC
My entire blog :
(Week 1 - 2)
In this phase, I focus mainly about offline analysis, this analysis aims to create a Deep learning pipeline for object detection model, including:
- Constructing data sets (training dataset, validation dataset and testing dataset). - Preprocessing data, studying about construct of COCO dataset which is used for training dataset and validation dataset. - Research and create model pipeline for all YOLO version in python. - Evaluate performance of YOLO methode by considering some evaluated metrics.
May 29 to June 11 (Week 1 - 2) - Experimentation on COCO dataset
DONE
- Constructing data sets (training dataset, validation dataset and testing dataset).
- Preprocessing data, studying about construct of COCO dataset which is used for training dataset and validation dataset.
- Research and create model pipeline for all YOLO version in python.
- Evaluate performance of YOLO methode by considering some evaluated metrics.
TODO