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Ernest A.'s Projects

imgregverifier icon imgregverifier

An image registry verifier base on deep learning to ensure a clients image is not registered already in a data base

jupyter icon jupyter

Jupyter metapackage for installation, docs and chat

kalman-and-bayesian-filters-in-python icon kalman-and-bayesian-filters-in-python

Kalman Filter book using Jupyter Notebook. Focuses on building intuition and experience, not formal proofs. Includes Kalman filters,extended Kalman filters, unscented Kalman filters, particle filters, and more. All exercises include solutions.

kaolin icon kaolin

A PyTorch Library for Accelerating 3D Deep Learning Research

labelimgplus icon labelimgplus

:metal: LabelImgPlus is a graphical image annotation tool which supports CLS,DET and SEG

machine_learning_lectures icon machine_learning_lectures

Collection of lectures and lab lectures on machine learning and deep learning. Lab practices in Python and TensorFlow.

markdown-here icon markdown-here

Google Chrome, Firefox, and Thunderbird extension that lets you write email in Markdown and render it before sending.

melgan-neurips icon melgan-neurips

GAN-based Mel-Spectrogram Inversion Network for Text-to-Speech Synthesis

mnn-lanenet icon mnn-lanenet

Lane detection model for mobile device via MNN project

mocapnet icon mocapnet

We present MocapNET, a real-time method that estimates the 3D human pose directly in the popular Bio Vision Hierarchy (BVH) format, given estimations of the 2D body joints originating from monocular color images. Our contributions include: (a) A novel and compact 2D pose NSRM representation. (b) A human body orientation classifier and an ensemble of orientation-tuned neural networks that regress the 3D human pose by also allowing for the decomposition of the body to an upper and lower kinematic hierarchy. This permits the recovery of the human pose even in the case of significant occlusions. (c) An efficient Inverse Kinematics solver that refines the neural-network-based solution providing 3D human pose estimations that are consistent with the limb sizes of a target person (if known). All the above yield a 33% accuracy improvement on the Human 3.6 Million (H3.6M) dataset compared to the baseline method (MocapNET) while maintaining real-time performance

networkextensions icon networkextensions

This is a reboot of the successful mod. From here, more of the content you love will continue to flow.

nlp-tutorial icon nlp-tutorial

Natural Language Processing Tutorial for Deep Learning Researchers

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