Machine Learning as a Service for HEP
-
Updated
May 10, 2022 - Python
Machine Learning as a Service for HEP
Seatbelt detection using YOLOv5 ML model
A Machine Learning model created using prebuild model. We need to feed the images to the model and it will predict if the same person is there else it will mark as unknown.
Animal Classification: A CNN-based image recognition model.
SugarSense : The Diabetes Prediction Application
The Smart Crop Disease Detection System is a Django web app that uses machine learning to identify crop diseases from leaf images. It helps farmers detect diseases quickly and take action to protect their crops. The system features AWS S3 image storage, TensorFlow Lite integration, and a responsive front-end for easy use.
Video-based surgical skill assessment using 3D convolutional neural networks
This project provides a robust pipeline for detecting deepfake content in images, videos, and audio files. By utilizing multiple machine learning models and advanced feature extraction techniques, the system can identify tampered media with high accuracy.
This is a vegetable sales prediction ML Model. This system is part of Online Crop Management And Forecasting System for Farmers and Agro Business Industry.
Classification and regression models for predicting the level of risk associated with extending credit to a borrower and the basic EPS amount respectively.
Developed an ML model for an e-commerce website to recommend products.
summer-search is a Python package that provides a simple interface for searching the web, extracting relevant content, and generating a summary based on the extracted information
Heart attack risk prediction using machine learning (Random Forest Model)
Car make and model classification with YOLOv3 object detector:- Python
Building a simple, not-so cool ML Model - Polynomial Fitting
A Software for Cabs which comprises most innovative ideas to provide a best-personalized user experience.
In this project I have created an end to end diabetes prediction application using streamlit.
Add a description, image, and links to the ml-model topic page so that developers can more easily learn about it.
To associate your repository with the ml-model topic, visit your repo's landing page and select "manage topics."