Pinned Repositories
12_Python_Seaborn_Module
Seaborn is one of the go-to tools for statistical data visualization in python. It has been actively developed since 2012 and in July 2018, the author released version 0.9. This version of Seaborn has several new plotting features, API changes and documentation updates which combine to enhance an already great library. This article will walk through a few of the highlights and show how to use the new scatter and line plot functions for quickly creating very useful visualizations of data.
6DRepNet
Official Pytorch implementation of 6DRepNet: 6D Rotation representation for unconstrained head pose estimation.
acad-homepage.github.io
AcadHomepage: A Modern and Responsive Academic Personal Homepage
AgentFormer
[ICCV 2021] Official PyTorch Implementation of "AgentFormer: Agent-Aware Transformers for Socio-Temporal Multi-Agent Forecasting".
agg_net
AI_course
ailia-models
The collection of pre-trained, state-of-the-art AI models for ailia SDK
gluon-cv
Gluon CV Toolkit
HowToCook
程序员在家做饭方法指南。Programmer's guide about how to cook at home (Chinese only).
Thirteentj.github.io
Thirteentj's Repositories
Thirteentj/automated-driving-control
Thirteentj/awesome-deep-learning-papers
The most cited deep learning papers
Thirteentj/berkeley-stat-157
Homepage for STAT 157 at UC Berkeley
Thirteentj/cityscapesScripts
README and scripts for the Cityscapes Dataset
Thirteentj/CMU-DATF
Thirteentj/ComplexUrbanScenarios
Thirteentj/Data-Competition-TopSolution
Data competition Top Solution 数据竞赛top解决方案开源整理
Thirteentj/DeepInf
DeepInf: Social Influence Prediction with Deep Learning
Thirteentj/DRLPytorch
Pytorch for Deep Reinforcement Learning
Thirteentj/Facial-Expression-Recognition.Pytorch
A CNN based pytorch implementation on facial expression recognition (FER2013 and CK+), achieving 73.112% (state-of-the-art) in FER2013 and 94.64% in CK+ dataset
Thirteentj/gluoncv-torch
PyTorch API for GluonCV Models
Thirteentj/HeadPoseEstimation-WHENet
Thirteentj/LaneGCN
[ECCV2020 Oral] Learning Lane Graph Representations for Motion Forecasting
Thirteentj/PathPlanning
Common used path planning algorithms with animations.
Thirteentj/PathPredictNusc
Thirteentj/PDPbox
python partial dependence plot toolbox
Thirteentj/pytorch-book
PyTorch tutorials and fun projects including neural talk, neural style, poem writing, anime generation (《深度学习框架PyTorch:入门与实战》)
Thirteentj/pytorch-openpose
pytorch implementation of openpose including Hand and Body Pose Estimation.
Thirteentj/PyTrafficSim
PyTrafficSim is a light traffic simulator for research related purposes. PTS is the most easy way to test your self-driving algorithm within a complicated intersection scenario.
Thirteentj/Real-time-Vehicle-and-Pedestrian-Counting-CenterNet
实时车辆行人交通流计数Real-time Vehicle and Pedestrian Counting (CenterNet)
Thirteentj/safe-BC
Thirteentj/Self-driving-car-Nanodegree
Learning to build the future, today! Self-driving cars represent one of the most significant advances in modern history. Their impact will go beyond technology, beyond transportation, beyond urban planning to change our daily lives in ways we have yet to imagine. Here are some considerations: self-driving vehicles will save a lot of lives they will make our lives also more comfortable (e.g. mobility for seniors) transport will be delivered as a service from companies who own fleets of self-driving vehicles transportation will become more tightly integrated and packaged into many services premium vehicle services will be available being able to avoid crashes will change the vehicle body construction radically interior equipment will focus even more on comfort emotion (max. speed, acceleration, handling, exterior design ..) might almost entirely leave transportation are parking lots or parking spaces in town centers necessary anymore? traffic flow will be better regulated infrastructure utilization will be optimized a hugh amount of data will be collected and used hacking of vehicles will be a serious issue ... In this program you could learn the skills and techniques used by self-driving car teams at the most innovative companies in the world like NVIDIA, Mercedes-Benz, Uber ATG, Elektrobit. This amazing technology is practiced through interactive projects in computer vision, robotic controls, localization, path planning, machine learning and more.
Thirteentj/Self_Driving_Car_specialization
Assignments and notes for the Self Driving Cars course offered by University of Toronto on Coursera
Thirteentj/Social-Intuition
Currently under research development
Thirteentj/STGAT
STGAT: Modeling Spatial-Temporal Interactions for Human Trajectory Prediction
Thirteentj/SVGNet
Thirteentj/Trajectory-Transformer
Code for "Transformer Networks for Trajectory Forecasting"
Thirteentj/Trajectron
Code accompanying "The Trajectron: Probabilistic Multi-Agent Trajectory Modeling with Dynamic Spatiotemporal Graphs" by Boris Ivanovic and Marco Pavone.
Thirteentj/unbox_yolov5_deepsort_counting
yolov5 deepsort 行人 车辆 跟踪 检测 计数
Thirteentj/Yolov5-deepsort-driverDistracted-driving-behavior-detection
基于深度学习的驾驶员分心驾驶行为(疲劳+危险行为)预警系统使用YOLOv5+Deepsort实现驾驶员的危险驾驶行为的预警监测