In this project I have collected photos of my all time favourite players and tried to do EDA. After performing EDA I trained multiple models and took the best one. Then I build a UI for user to drag and drop his/her own player image and the Flask server will come up with the prediction.
I took the dataset from here and also by manual downloading using the Fatkun batch image downloader.
- Python, HTML, CSS, JavaScript, jQuery
- Flask
git clone
https://github.com/Micky373/end_to_end_football_players_image_classification.git
cd end_to_end_football_players_image_classification
pip install -r requirements.txt
cd server
python sever.py
After the flask server is succesfully loaded go to the client directory and open the html
cd ../client
Then the UI will open
Here drag and drop a clear picture of one of the 5 players and click classify
In the test_images section I have provided 5 sample images you can check the classification using those too
All the EDA, model training and predicting is clearly shown is found in the
notebooks
folder.
Currently I have worked on simpler less number of images, my future plan is to come up with a robust model trained with vast data.
Contributions, issues, and feature requests are welcome!
Feel free to check the issues page.
Give a ⭐️ if you like this project!
- Special thanks to Dhaval Patel
- The UI implementation and code flow done with the help of this play list