This project is an image classifier that predicts the identity of a sports celebrity using machine learning. The classifier has been trained to distinguish between five well-known athletes: Cristiano Ronaldo, Lionel Messi, Neymar Jr, Novak Djokovic, and Stephen Curry.
- Machine Learning Models: Tested various models including Logistic Regression, SVM, and Random Forest. The Support Vector Machine (SVM) was found to be the best performer.
- Data Pipeline: Includes data collection, data cleaning, and feature engineering to ensure high-quality input for the model.
- Model Deployment: The trained model is served via a Flask server.
- Frontend Interface: Simple and intuitive HTML interface to upload images and display classification results.
The dataset was curated through careful data collection and cleaning to ensure diversity and representativeness of each athlete.
After testing multiple models, the SVM model was selected based on its superior accuracy and performance.
- Clone the repository:
git clone https://github.com/RiyanBhargava/ML_Project_3_Sports_Celebrity_Image_Classifier.git
- Navigate to the project directory:
cd ML_Project_3_Sports_Celebrity_Image_Classifier - Install the required dependencies:
pip install -r requirements.txt
- Run the Flask server:
python server.py
- Access the application:
Run
index.html
- Upload an image of a sports celebrity.
- The model will predict the celebrity's identity and display the result along with a probability score.
- Extend the classifier to include more sports celebrities.
- Improve the frontend with additional features and styling.
- Optimize the model further using advanced techniques like deep learning.
Feel free to fork this repository, submit issues, or contribute with pull requests.