Deep Learning Simplified is an Open-source repository, containing beginner to advance level deep learning projects for the contributors, who are willing to start their journey in Deep Learning. Devfolio URL, https://devfolio.co/projects/deep-learning-simplified-f013
Info about the related issue (Aim of the project) :
Name: Hitesh
GitHub ID: hiteshhhh007
Email ID: hiteshkrishna43@gmail.com
Idenitfy yourself:GSSoC'24 Participant
Closes: #518 will be closed through this PR
Describe the add-ons or changes you've made 📃
I have added 3 different approaches and all the 3 approaches have been tested out and then was decided to be implemented for this project. The 3 approaches are:
OpenCV-Technique
Usage of U-Net DL Architecture
Pre-Trained Architecture DeepFillv2 using Gated NN's.
All the approaches are implemented in the .ipynb with comments and details.
Type of change ☑️
What sort of change have you made:
[x] Bug fix (non-breaking change which fixes an issue)
[x] New feature (non-breaking change which adds functionality)
[x] Code style update (formatting, local variables)
[ ] Breaking change (fix or feature that would cause existing functionality to not work as expected)
[ ] This change requires a documentation update
How Has This Been Tested? ⚙️
All the approaches have been tested out on multiple images on metrics like loss and accuracy. The loss metric has been more focused here because of the project nature.
Models have been verified by their prediction on the val-set and then on test-set, both of which was taken from the training-set
Checklist: ☑️
[x] My code follows the guidelines of this project.
[x] I have performed a self-review of my own code.
[x] I have commented my code, particularly wherever it was hard to understand.
[x] I have made corresponding changes to the documentation.
[x] My changes generate no new warnings.
[x] I have added things that prove my fix is effective or that my feature works.
[x] Any dependent changes have been merged and published in downstream modules.
Pull Request for DL-Simplified 💡
Issue Title : Image De-Photobombing
GSSoC'24 Participant
Closes: #518 will be closed through this PR
Describe the add-ons or changes you've made 📃
I have added 3 different approaches and all the 3 approaches have been tested out and then was decided to be implemented for this project. The 3 approaches are:
Type of change ☑️
What sort of change have you made:
How Has This Been Tested? ⚙️
All the approaches have been tested out on multiple images on metrics like loss and accuracy. The loss metric has been more focused here because of the project nature.
Models have been verified by their prediction on the val-set and then on test-set, both of which was taken from the training-set
Checklist: ☑️