qihao._.huang (qihao-huang)

qihao-huang

Geek Repo

Company:Hong Kong

Location:Somewhere, Mars

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

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qihao._.huang's repositories

machine-configuration

configuration for a new ubuntu/mac machine

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cpp-python-socket

Simple socket communication between C++ and Python

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real-time-face-sentimental-app

Real-time sentimental analysis on Android mobile platform

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50projects50days

50+ mini web projects using HTML, CSS & JS

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AdelaiDepth

This repo contains the projects: 'Virtual Normal', 'DiverseDepth', and '3D Scene Shape'. They aim to solve the monocular depth estimation, 3D scene reconstruction from single image problems.

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awesome-multiple-object-tracking

Paper list for multiple object tracking

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Awesome-Optical-Flow

This is a list of awesome paper about optical flow and related work.

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awesome-radar-perception

A curated list of radar datasets, detection, tracking and fusion

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awesome-state-of-depth-completion

Current state of supervised and unsupervised depth completion methods

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byteps

A high performance and generic framework for distributed DNN training

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cocoapi

COCO API Customized for YouTubeVIS evaluation

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GeoNet

Code for GeoNet: Unsupervised Learning of Dense Depth, Optical Flow and Camera Pose (CVPR 2018)

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LeetCode

solver of LeetCode

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minitorch

The full minitorch student suite.

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mmdetection

OpenMMLab Detection Toolbox and Benchmark

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MMP_Track1_ICCV21

Tracking code for the winner of track 1 in the MMP-Tracking Challenge at ICCV 2021 Workshop.

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mobile-vision

Mobile vision models and code

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monocular-depth-prediction

monocular depth for the project of Computational Photography

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pytorch-distributed

A quickstart and benchmark for pytorch distributed training.

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pytorch-mgpu-cifar10

testing multi gpu for pytorch

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PyTorch-Universal-Docker-Template

Template repository to build PyTorch projects from source on any version of PyTorch/CUDA/cuDNN.

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TrackEval

HOTA (and other) evaluation metrics for Multi-Object Tracking (MOT).

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UniTrack

Unified tracking framework with a single appearance model. It supports Single Object Tracking (SOT), Video Object Segmentation (VOS), Multi-Object Tracking (MOT), Multi-Object Tracking and Segmentation (MOTS), Pose Tracking, Video Instance Segmentation (VIS), and class-agnostic MOT (e.g. TAO dataset).

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