hitersyw
PHD candidate from harbin institute of technology. My supervisor is Prof Yili Fu.
Harbin institute of technologyHarbin China
Pinned Repositories
ActionRecognition
Explore Action Recognition
baxter_moveit_experiments
Set of environments to test various MoveIt! motion planning algorithms on the Baxter robot
baxter_mpnet_ompl_docker
BDLFusion
Codes about Bi-level Dynamic Learning for Jointly Multi-modality Image Fusion and Beyond
CFTT
Correlation Filters Tissue Tracking with Application to Robotic Minimally Invasive Surgery
control-toolbox
The Control Toolbox - An Open-Source C++ Library for Robotics, Optimal and Model Predictive Control
Coursera-ML-AndrewNg-Notes
吴恩达老师的机器学习课程个人笔记
daVinci-TemporalSegmentation
Temporal Convolutional Network (TCN) provides pre-processed kinematics and video data for a myriad of machine learning techniques, optimized for real surgical data.
fastron
A C++ implementation of Fastron based on the paper "Learning-Based Proxy Collision Detection for Robot Motion Planning Applications".
kuka_arm
A gazebo simulation with kuka_kr6r900sixx robotic arm of motion planning functions
hitersyw's Repositories
hitersyw/baxter_mpnet_ompl_docker
hitersyw/baxter_moveit_experiments
Set of environments to test various MoveIt! motion planning algorithms on the Baxter robot
hitersyw/Lightweight-Segmentation
Lightweight models for real-time semantic segmentation(include mobilenetv1-v3, shufflenetv1-v2, igcv3, efficientnet).
hitersyw/Robotic-Arm
Kinematics, Dynamics, Trajectory planning and Control of a 4 degrees of freedom robotic arm with matlab robotic toolbox
hitersyw/cvpr_with_code
Rank the papers accepted to CVPR 2019 by the number of stars in their github repository
hitersyw/deep-learning-drizzle
Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learning from these exciting lectures!!
hitersyw/mobile-deeplab-v3-plus
Deeplab-V3+ model with MobilenetV2/MobilenetV3 on TensorFlow for mobile deployment.
hitersyw/ModernRoboticsCpp
Modern Robotics: Mechanics, Planning, and Control C++ Library --- The primary purpose of the provided software is to be easy to read and educational, reinforcing the concepts in the book. The code is optimized neither for efficiency nor robustness. http://modernrobotics.org/
hitersyw/mobilenetv3-segmentation
MobileNetV3 for Semantic Segmentation.
hitersyw/Image-Based-Tracking
Image-guided tracking algorithm for surgical scenes
hitersyw/drinking_assistant
Robotic drinking assistant using Kinova JACO
hitersyw/LDESCpp
This is a C++ implementation of AAAI2019 paper LDES tracker
hitersyw/MobilenetV3-Tensorflow-1
the multi-GPUs implementation of mobilenet v3 in tensorflow with tf.layers
hitersyw/kuka_control
hitersyw/LRCN-for-Activity-Recognition
hitersyw/MobileNetV3-for-Segmentation
Re-implementing MobileNetV3 for semantic segmentation on cityscapes with pytorch
hitersyw/Fast-SCNN-pytorch
A PyTorch Implementation of Fast-SCNN: Fast Semantic Segmentation Network
hitersyw/robot-surgery-segmentation
Wining solution and its improvement for MICCAI 2017 Robotic Instrument Segmentation Sub-Challenge
hitersyw/CS205A-Mathematical-Methods-for-Robotics--Vision--and-Graphics
斯坦福数值分析公开课的学习资料
hitersyw/robotics_analysis
robotics codes in python (and matlab)
hitersyw/jaco-arm-pkgs
A model of the Kinova Jaco arm including some tools, e.g. plugins for Gazebo
hitersyw/lihang_book_algorithm
致力于将李航博士《统计学习方法》一书中所有算法实现一遍
hitersyw/pytorch-segmentation-detection
Image Segmentation and Object Detection in Pytorch
hitersyw/Image_Segmentation
pytorch Implementation of U-Net, R2U-Net, Attention U-Net, Attention R2U-Net.
hitersyw/PyAdvancedControl
Python codes for advanced control
hitersyw/pumpkin-book
《机器学习》(西瓜书)公式推导解析,在线阅读地址:https://datawhalechina.github.io/pumpkin-book
hitersyw/STAPLE
C++ implementation of staple algorithm for object tracking.
hitersyw/awesome-deep-learning
A curated list of awesome Deep Learning tutorials, projects and communities.
hitersyw/Lihang
Statistical learning methods, 统计学习方法 [李航] 值得反复读. [笔记, 代码, notebook, 参考文献, Errata]
hitersyw/clamp
Combined Learning from Demonstration and Motion Planning