Sagar Shukla 's Projects
Tools/Methods:- OpenCV(Video inferencing),, YOLOv8(for Car , and license plate detection) , SORT(tracking), easyOCR(reading the license plate)
The official implementation of "Divergence of Features and Mean: A BatchNorm-based Abnormality Criterion for Weakly Supervised Video Anomaly Detection"
Best Algorithm :- ExtraTreesClassifier Accuracy:- 95.32% Developed a machine learning model to classify Breast cancer as 'benign' or 'malignant' with an accuracy of 95.32% using ExtraTree Classifier. Built a predictive system using Django backend and HTML/CSS frontend with Tailwind CSS framework to take user input and predict the cancer type.
test
CSI FCRIT OFFICIAL WEBSITE
Simple Online Realtime Tracking with a Deep Association Metric
A Lightweight Face Recognition and Facial Attribute Analysis (Age, Gender, Emotion and Race) Library for Python
Best ALGORITHM :- GradientBoostingClassifier , Accuracy :- 98.03% . This is a ML project which makes classification , whether a patient is diabetic or not , based on the inputs given .
Project tracking of the "Mobile ML Working Group", for the End-to-End TensorFlow Lite tutorials.
Demonstration of different algorithms and operations on faces. Star the repo⭐
Real-time face detection and emotion/gender classification using fer2013/imdb datasets with a keras CNN model and openCV.
My project is a Face Mask Detection system utilizing Deep Learning techniques. Using a live video feed and camera, the system accurately identifies whether a person is wearing a mask or not, providing real-time feedback. By leveraging advanced algorithms, it enhances safety measures by automatically detecting compliance with mask-wearing protocols.
A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.
Heart Disease Prediction System is a machine learning-based application deployed using Django framework. It utilizes Random Forest Classifier algorithm with an impressive accuracy of 99.02%. This system assists healthcare professionals in timely diagnosis and treatment decisions, improving patient outcomes.
This repository contains an implementation of object detection using YOLOv8 specifically designed for detecting weapons in images and videos. The repository includes pre-trained models and sample data for testing.
Object Detection System
This is Ml project with Django-Tailwind css to find whether the patient have parkinson or not
Real-time Pose estimation using Mediapipe and basic Pose classification(T pose, Tree pose , WarriorII pose) using Angle Heuristics.
Rajasthan Police Hackathon
Config files for my GitHub profile.
Language used :- Python , Tools :- Scikit-learn, OpenCV , mediapipe.
Simple, online, and realtime tracking of multiple objects in a video sequence.
Easily train or fine-tune SOTA computer vision models with one open source training library. The home of Yolo-NAS.
The Titanic Survival Prediction project is a web application built with Django as the backend framework and utilizes Machine Learning to predict whether a person aboard the Titanic would have survived or not, based on historical data from the famous Titanic dataset.
💎1MB lightweight face detection model (1MB轻量级人脸检测模型)
Implementation of paper - YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information