Localize to Classify and Classify to Localize: Mutual Guidance in Object Detection

Programming assignments and quizzes from all courses within the Machine Learning Engineering for Production (MLOps) specialization offered by deeplearning.ai

PyTorch image models, scripts, pretrained weights -- ResNet, ResNeXT, EfficientNet, EfficientNetV2, NFNet, Vision Transformer, MixNet, MobileNet-V3/V2, RegNet, DPN, CSPNet, and more

A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning

💯 Curated coding interview preparation materials for busy software engineers

Probabilistic Machine Learning: Advanced Topics

A PyTorch implementation of the Deep SVDD anomaly detection method

Code for "AN EFFECTIVE FOVEATED 360° IMAGE ASSESSMENT BASED ON GRAPH CONVOLUTION NETWORK" paper

Time-Series Work Summary in CS Top Conferences (NIPS, ICML, ICLR, KDD, AAAI, etc.)

This is the repository for https://quantumalgorithms.org

Home of Qiskit documentation translations

A university quantum algorithms/computation course supplement based on Qiskit

Code for the paper

A collection of Jupyter notebooks showing how to use the Qiskit SDK

The online book for the Introduction to Quantum Information Science course, by Artur Ekert and Tim Hosgood.

A quantum anomaly detection method based on the expected values of density matrices and density estimation. Honorable Mention (3-4 Place) in the Qiskit Hackaton Global 2021.

A curated list of awesome quantum computing learning and developing resources.

Anomaly detection on a quantum computer using Qiskit

Detectron2 is FAIR's next-generation platform for object detection, segmentation and other visual recognition tasks.

The OCR approach is rephrased as Segmentation Transformer: https://arxiv.org/abs/1909.11065. This is an official implementation of semantic segmentation for HRNet. https://arxiv.org/abs/1908.07919

Sandbox for training deep learning networks

Try out deep learning models online on Google Colab

The project is an official implementation of our CVPR2019 paper "Deep High-Resolution Representation Learning for Human Pose Estimation"

FIL_VAE_LSTM_Fed

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

Activity Recognition from 2D pose using an LSTM RNN

Geometric Deep Learning Extension Library for PyTorch