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
Idenitfy yourself: (Mention in which program you are contributing in. Eg. For a JWOC 2022 participant it's, JWOC Participant) : GSSOC-2024 Contributor
Closes: #597
Describe the add-ons or changes you've made 📃
1.) Used Transfer learning and trained and analysed models such as VGG16 , ResNet50 etc. for the specified dataset.
2.) used data_augmentation techniques to increase accuracy and the performance of models.
3.)Perfromed EDA to understand dataset.
4.) compared accuracy scores of models using accuarcy score and graph visualization.
Give a clear description of what have you added or modifications made
Type of change ☑️
What sort of change have you made:
[ ] Bug fix (non-breaking change which fixes an issue)
[x] New feature (non-breaking change which adds functionality)
[ ] 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? ⚙️
Describe how it has been tested
Describe how have you verified the changes made
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.
[ ] 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 : Traffic Sign Recognition
JWOC Participant
) : GSSOC-2024 ContributorCloses: #597
Describe the add-ons or changes you've made 📃
1.) Used Transfer learning and trained and analysed models such as VGG16 , ResNet50 etc. for the specified dataset. 2.) used data_augmentation techniques to increase accuracy and the performance of models. 3.)Perfromed EDA to understand dataset. 4.) compared accuracy scores of models using accuarcy score and graph visualization.
Give a clear description of what have you added or modifications made
Type of change ☑️
What sort of change have you made:
How Has This Been Tested? ⚙️
Describe how it has been tested Describe how have you verified the changes made
Checklist: ☑️