GSoc/2023/StatusReports/QuocHungTran

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

gsoc23-autotags-assignment

Contacts

Email: [email protected]

Github: quochungtran

Invent KDE: quochungtran

LinkedIn: https://www.linkedin.com/in/tran-quoc-hung-6362821b3/

Links to Blogs and other writing

My blog for GSoC

My entire blog :

• https://quochungtran.github.io/

(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