Weather Image Recognition

[aditya0929]

Pull Request for DL-Simplified 💡

Issue Title : Weather Image Recognition #338

• Info about the related issue (Aim of the project) : to predict correct weather labels corresponding to their corresponding images from the dataset containing 6800+ images in 11 classes .
• Name: Aditya Narayan Jha
• GitHub ID: https://github.com/aditya0929
• Email ID: [email protected]
• Idenitfy yourself: (Mention in which program you are contributing in. Eg. For a JWOC 2022 participant it's, JWOC Participant) ssoc-2023

Closes: #338

Describe the add-ons or changes you've made 📃

i started developing a CNN model from scratch but it only gave me an accuracy of about 60% so further ahead i went with a different approach of dealing with the dataset using RESNET models , out of all the models i had tried RESNET152V2 gave the best accuracy in the first 10 iterations itself so that was the one i considered .

out of vgg16 and vgg19 i had chosen vgg19 only for the fact that in the last two pull requests that i had made i had been using VGG16 and vgg19 had only been used once by me .

InceptonV3 has generally proved good on larger dataset giving a constant decent accuracy and that why i chose inception rather than other models .

Type of change ☑️

What sort of change have you made:

• Bug fix (non-breaking change which fixes an issue)
• 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? ⚙️

i had predicted 5 image labels for each of the models and all of them came to be the exactly true and in accordance to their respective accuracy score .
i had taken the classification score and f1 score into consideration where it depicted individual accuracy of each classes of all three models .
here are the predicted labels

Checklist: ☑️

• My code follows the guidelines of this project.
• I have performed a self-review of my own code.
• I have commented my code, particularly wherever it was hard to understand.
• I have made corresponding changes to the documentation.
• My changes generate no new warnings.
• I have added things that prove my fix is effective or that my feature works.
• Any dependent changes have been merged and published in downstream modules.

[github-actions]

Our team will soon review your PR. Thanks @aditya0929 :)

[abhisheks008]

Your PR is approved and ready to be merged.
@aditya0929