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[Project Addition] : Sports Image Classification #633
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Thank you for creating this issue! We'll look into it as soon as possible. Your contributions are highly appreciated! 😊 |
Assigned @kyra-09 |
You can't work on two issues at a time. Please complete the assigned issue, then only you can work on this issue and get the assignment. |
Hey @abhisheks008, |
Full name : Mehak |
Full name : Arya Vishal |
@abhisheks008 if I implement atleast 5 different models can this issue be labled as Level 3? |
Yes |
Pardon me for asking another question...but do we get extra points for creating an issue as well? |
@kyra-09 can we both work on this problem?? |
Not sure. You can check with the core team for this. I am adding the labels properly, if they allot points for it, you'll automatically get it. @aryaVishal1706 sorry mate, one contributor can work on one issue at a time. |
Hello @kyra-09! Your issue #633 has been closed. Thank you for your contribution! |
Deep Learning Simplified Repository (Proposing new issue)
🔴 Project Title : Sports Image Classification
🔴 Aim : To detect 100 different sports using DL models.
🔴 Dataset : https://www.kaggle.com/datasets/gpiosenka/sports-classification
🔴 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.
📍 Follow the Guidelines to Contribute in the Project :
requirements.txt
- This file will contain the required packages/libraries to run the project in other machines.Model
folder, theREADME.md
file must be filled up properly, with proper visualizations and conclusions.🔴🟡 Points to Note :
✅ To be Mentioned while taking the issue :
2.) use data augmentation techniques to improve the accuarcy of models.
3.) Comparing performance and accuracy of models using accuracy score ,loss and accuracy graph , confusion matrix for better understanding.
4.) Perfroming EDA (data analysis) for dataset to understand the structure of data.
5.) Using README file for describing the work I've performed.
Happy Contributing 🚀
All the best. Enjoy your open source journey ahead. 😎
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