Hrugved Kolhe's Projects
AICTE Task management Application
This is an industry-based project where we have to Predict careers based on the user profile.
Food Crop disease detection model using InceptionV3 architecture
Food Crop disease detection model using VGG16 architecture
Discord Bot based on your favourite character
One Stop Destination to get acquainted with scripts in Data Science. Show your support by β¨ this repository.π
In this repository I have explained the model for face blurring and deblurring.
Demonstration of different algorithms and operations on faces. Join the Discord channel for discussion.
Cheatsheet material related to GIT and GitHub
Config files for my GitHub profile.
All the content and the dependencies required for the portfolio is stored here
This model detects and tracks the pose of the human through Image as well as Video using Computer Vision.
Data Science and Machine Learning tasks are given by The Intern Academy are performed in this repository.
Notebooks used and created during the 30days challenge are uploaded here
Learn OpenCV : C++ and Python Examples
Showcase website for M4I model
Delivery of Medical equipment with the help of drones in Hilly terrain using Unsupervised Machine Learning and Google map APIs
Machine Learning Algorithms
ML/DL Projects will be included in this repository.
Machine Learning models
The goal of this project is to make a prediction model which will predict whether an athlete will win a medal or not.
An online Doctor Appointment system DATABASE is created using PostgreSQL
Plant Disease Classification server for m4i application
Real-time QRcode/Barcode scanning Model
This is a RealTime-MultiplevDigitRecognition application that can predict output corresponding to handwritten images. Here, I used LinearSVC(support vector classifier) and the sequential model of Keras for creating this predictive model. I trained SVC and reshaped it to the 28X28 MNIST dataset, the accuracy of this model turned out to be very good when I run this model.
This project classifies the emotions like happy, neutral, angry, disgusted, fearful, sad and surprised in real-time using face detection.
Emotion (Happy, Neutral, Anger, Disgust, Fear, Sad) detection is performed in this repository. I am using the popular dataset Crema from Speech Emotion Recognition (en) which contains 7,442 original clips from 91 actors - 48 male and 43 female of a wide range of ages, races and ethnicities.