Muffin vs Chihuahua Detection

[abhisheks008]

Deep Learning Simplified Repository (Proposing new issue)

:red_circle: Project Title : Muffin vs Chihuahua Detection :red_circle: Aim : The aim of this project is to detect the Muffin and Chihuahua based on the given dataset. :red_circle: Dataset : https://www.kaggle.com/datasets/samuelcortinhas/muffin-vs-chihuahua-image-classification :red_circle: Approach : Try to use 3-4 algorithms to implement the models and compare all the algorithms to find out the best fitted algorithm for the model by checking the accuracy scores. Also do not forget to do a exploratory data analysis before creating any model.

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📍 Follow the Guidelines to Contribute in the Project :

• You need to create a separate folder named as the Project Title.
• Inside that folder, there will be four main components.
  • Images - To store the required images.
  • Dataset - To store the dataset or, information/source about the dataset.
  • Model - To store the machine learning model you've created using the dataset.
  • requirements.txt - This file will contain the required packages/libraries to run the project in other machines.
• Inside the Model folder, the README.md file must be filled up properly, with proper visualizations and conclusions.

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:red_circle::yellow_circle: Points to Note :

• The issues will be assigned on a first come first serve basis, 1 Issue == 1 PR.
• "Issue Title" and "PR Title should be the same. Include issue number along with it.
• Follow Contributing Guidelines & Code of Conduct before start Contributing.

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:white_check_mark: To be Mentioned while taking the issue :

• Full name :
• GitHub Profile Link :
• Email ID :
• Participant ID (if applicable):
• Approach for this Project :
• What is your participant role? (Mention the Open Source program)

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Happy Contributing 🚀

All the best. Enjoy your open source journey ahead. 😎

[Jeba-Rachel-Nesica]

Hi @abhisheks008 , I'd like to work on this issue.

Full name : JEBA RACHEL NESICA GitHub Profile Link : https://github.com/Jeba-Rachel-Nesica Email ID : [email protected] Participant ID (if applicable): Approach for this Project : SVM, Random forest, CNN (using pre-trained models like VGG) are to be used to find the best fitting one. What is your participant role? (Mention the Open Source program): GSSoC'24 Contributor

[MrPotato-00]

Hello is this problem statement still open, please assign this to me. I am eager to contribute to this.

Full name : Dipankar Dutta
GitHub Profile Link : https://github.com/MrPotato-00
Email ID : [email protected]
Participant ID (if applicable):
Approach for this Project : CNN ( transfer learning model like VGG16 etc can be used) 
What is your participant role? Contributor

[abhisheks008]

Hi @MrPotato-00 need more clarity in the approach.

[MrPotato-00]

First I will apply image augmentations to the image dataset. Then I will build CNN model, a VGG16 and RESNET and then tune the hyperparameters to increase the accuracy and get the best mode out of them.

[abhisheks008]

Good to go. Issue assigned to you @MrPotato-00

[MrPotato-00]

Thanks a lot