Crack Detection using DL

[achrekarom12]

Deep Learning Simplified Repository (Proposing new issue)

:red_circle: Project Title : Crack Detection using DL :red_circle: Aim : To detect the cracks in the image using Deep learning. :red_circle: Dataset : https://www.kaggle.com/competitions/crack-identification-ce784a-2020-iitk/data :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. 😎

[achrekarom12]

Full name : Om Achrekar GitHub Profile Link : https://github.com/achrekarom12 Email ID : achrekarom@gmail.com Participant ID (if applicable): Approach for this Project : I will various image detection models such as VGG16, ResNet50 to detect the cracks in the image. I will fine tune them if necessary. What is your participant role? SSoC '23 Participant

[abhisheks008]

Issue assigned to you @achrekarom12