Ebubekir Dogan (ebubekirdgn)

ebubekirdgn

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Location:İstanbul

Home Page:https://www.linkedin.com/in/ebubekirdgn/

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Ebubekir Dogan 's starred repositories

openpose

OpenPose: Real-time multi-person keypoint detection library for body, face, hands, and foot estimation

Language:C++License:NOASSERTIONStargazers:30955Issues:922Issues:1970

learnopencv

Learn OpenCV : C++ and Python Examples

Language:Jupyter NotebookStargazers:21107Issues:883Issues:319

yolov10

YOLOv10: Real-Time End-to-End Object Detection [NeurIPS 2024]

Language:PythonLicense:AGPL-3.0Stargazers:9602Issues:50Issues:403

yolov9

Implementation of paper - YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information

Language:PythonLicense:GPL-3.0Stargazers:8892Issues:55Issues:525

YOLO-World

[CVPR 2024] Real-Time Open-Vocabulary Object Detection

Language:PythonLicense:GPL-3.0Stargazers:4453Issues:40Issues:435

sahi

Framework agnostic sliced/tiled inference + interactive ui + error analysis plots

Language:PythonLicense:MITStargazers:3998Issues:43Issues:0

LSTM-Human-Activity-Recognition

Human Activity Recognition example using TensorFlow on smartphone sensors dataset and an LSTM RNN. Classifying the type of movement amongst six activity categories - Guillaume Chevalier

Language:Jupyter NotebookLicense:MITStargazers:3337Issues:160Issues:41

NTURGB-D

Info and sample codes for "NTU RGB+D Action Recognition Dataset"

PythonWindows

Unofficial Python installers for Windows

Human-Activity-Recognition-using-CNN

Convolutional Neural Network for Human Activity Recognition in Tensorflow

Language:Jupyter NotebookLicense:Apache-2.0Stargazers:468Issues:13Issues:21

HAR-stacked-residual-bidir-LSTMs

Using deep stacked residual bidirectional LSTM cells (RNN) with TensorFlow, we do Human Activity Recognition (HAR). Classifying the type of movement amongst 6 categories or 18 categories on 2 different datasets.

Language:PythonLicense:Apache-2.0Stargazers:318Issues:19Issues:7

DeepConvLSTM

Deep learning framework for wearable activity recognition based on convolutional and LSTM recurretn layers

Language:Jupyter NotebookStargazers:269Issues:19Issues:7

Awesome-Human-Activity-Recognition

An up-to-date & curated list of Awesome IMU-based Human Activity Recognition(Ubiquitous Computing) papers, methods & resources. Please note that most of the collections of researches are mainly based on IMU data.

License:MITStargazers:244Issues:16Issues:0

stove

Stove: The easiest way of writing e2e/component tests for your JVM back-end API with Kotlin

Language:KotlinLicense:Apache-2.0Stargazers:161Issues:14Issues:44

Exercise-Correction

Make use of the power of Mediapipe’s pose detection, this project is built in order to analyze, detect and classifying the forms of fitness exercises.

Language:Jupyter NotebookLicense:MITStargazers:56Issues:2Issues:0

A-Deep-Learning-Framework-for-Assessing-Physical-Rehabilitation-Exercises

A framework for quality assessment of exercises in physical rehabilitation based on skeletal joint displacements collected with a motion capture system.

Language:Jupyter NotebookStargazers:50Issues:4Issues:3

Deep-Learning-Fitness-Exercise-Correction-Keras

Machine Learning Course at University of Zürich

Language:Jupyter NotebookStargazers:30Issues:4Issues:1

labelImg

🖍️ LabelImg is a graphical image annotation tool and label object bounding boxes in images

Language:PythonLicense:MITStargazers:26Issues:0Issues:0

Object-Detection-Web-Application-with-Flask-and-YOLOv9

Object Detection Web Application with Flask and YOLOv9

fitness-activity-classification-with-lstms

Web Application for Human Activity (Fitness) Recognition using LSTMs

Language:C++Stargazers:12Issues:2Issues:0

YOLOv8-Pose-Classification

YOLOv8 Pose classification and repetition counting with the k-NN algorithm

Language:Jupyter NotebookStargazers:9Issues:2Issues:0

AI-Trainer

This repository presents a set of tools to help you improve your weightlifting form. It does so by analyzing a video of your workout, estimating your pose with a AI model (mediapipe-blazepose) and giving you feedback on your form.

Language:PythonLicense:GPL-3.0Stargazers:3Issues:1Issues:2

biceps-curl-counter

Biceps Curl Counter python app based on Mediapipe's Human Pose Estimation architecture.

Language:Jupyter NotebookLicense:MITStargazers:3Issues:1Issues:0
Language:PythonLicense:MITStargazers:2Issues:0Issues:0

Real-time-Bicep-Curls-Analysis-and-Counting-using-OpenCV-and-MediaPipe-

This real-time biceps curls analysis and counting system demonstrates the practical application of computer vision in fitness, offering users an interactive and informative tool for enhancing their biceps curl exercises and overall workout routines.

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