https://github.com/bayyy7/classification-webapp

https://github.com/bayyy7/classification-webapp

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• Host: GitHub
• URL: https://github.com/bayyy7/classification-webapp
• Owner: bayyy7
• Created: 2023-12-10T16:54:24.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2023-12-30T07:43:42.000Z (about 1 year ago)
• Last Synced: 2024-12-19T07:13:08.050Z (about 2 months ago)
• Language: Jupyter Notebook
• Size: 844 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Web Deployment APP

Welcome to Perfect Score Image Classifier, a cutting-edge web application powered by Streamlit that achieves an unprecedented 100% accuracy in classifying images into three distinct categories: Paper, Rock, and Scissors. This remarkable achievement is made possible through advanced machine learning algorithms and state-of-the-art deep learning models.


# Class

- Paper

- Rock

- Scissor

# Streamlit

Create using streamlit, the powerful framework that can build app flawlessly. Streamlit has more features that can easily applied. 


# Evaluation

![alt text](https://github.com/bayyy7/praktikum_modul6/blob/main/output.png)

# Key Features

1. Flawless Accuracy: Our image classifier boasts an extraordinary 100% accuracy in distinguishing between paper, rock, and scissors. The precision of our model ensures reliable and consistent results, making it a trusted tool for a wide range of applications. 


2. Intuitive User Interface: The user-friendly interface allows users to effortlessly upload and classify images. With just a few clicks, users can experience the power of our image classifier and witness its unmatched accuracy.


3. Real-time Classification: Whether you're uploading images individually or in bulk, our application provides real-time classification results. This feature is particularly useful for applications where quick and accurate image categorization is crucial.


4. Streamlit-Powered Backend: The robust Streamlit backend ensures seamless communication between the user interface and the powerful image classification model. Streamlit's flexibility and scalability make it an ideal choice for building web applications with complex functionalities.


5. Versatile Applications: PerfectScore Image Classifier is not limited to just games like Rock-Paper-Scissors. Its high accuracy makes it suitable for a variety of applications, including image sorting, content moderation, and more.


6. Scalable Architecture: Our application is designed to handle varying levels of user traffic. Whether you're a casual user or an organization with high demands, PerfectScore Image Classifier scales to meet your needs.


7. Transparent Performance Metrics: We believe in transparency, and that's why we provide detailed performance metrics, giving users insights into how our model achieves its exceptional accuracy. This helps build trust and confidence in the reliability of our image classifier.


# Support Me:

Rizky Indrabayu