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  • šŸ‘‹ Hi, Iā€™m @Tenfleques [Chikake Tendai Mapungwana]
  • šŸ‘€ Iā€™m interested in research on combinatorial approaches to intepretable AI
  • šŸŒ± Iā€™m currently learning my PhD at Moscow Institute of Physics Technology
  • šŸ’žļø Iā€™m looking to collaborate on combinatorics torwards explainable AI and expert systems
  • šŸ“« How to reach me: [email protected]

Tendai's Projects

3dmppe_posenet_release icon 3dmppe_posenet_release

Official PyTorch implementation of "Camera Distance-aware Top-down Approach for 3D Multi-person Pose Estimation from a Single RGB Image", ICCV 2019

alphapose icon alphapose

Real-Time and Accurate Full-Body Multi-Person Pose Estimation&Tracking System

awesome-ai-ml-dl icon awesome-ai-ml-dl

Awesome Artificial Intelligence, Machine Learning and Deep Learning as we learn it. Study notes and a curated list of awesome resources of such topics.

bebeca icon bebeca

A benchmark for evaluating approximate betweenness centrality algorithms.

blazepose-tensorflow icon blazepose-tensorflow

A third-party Tensorflow Implementation for paper "BlazePose: On-device Real-time Body Pose tracking".

bluepy icon bluepy

Python interface to Bluetooth LE on Linux

car icon car

Content adaptive resampler for image downscaling

centernet icon centernet

Codes for our paper "CenterNet: Keypoint Triplets for Object Detection" .

codegeex icon codegeex

CodeGeeX: An Open Multilingual Code Generative Model

codetiming icon codetiming

A flexible, customizable timer for your Python code

crm icon crm

c# windows forms. SQL server. CRM.

cvr_course icon cvr_course

Repository for Computer Vision in Robotics course. https://www.starkitrobots.com/cvr

deepface icon deepface

Keras implementation of the renowned publication "DeepFace: Closing the Gap to Human-Level Performance in Face Verification" by Taigman et al. Pre-trained weights on VGGFace2 dataset.

dlib icon dlib

A toolkit for making real world machine learning and data analysis applications in C++

drbc icon drbc

DrBC: A novel graph neural network approach to identify high Betweenness Centraliy (BC) nodes ( CIKM'19 )

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