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awesome-semantic-segmentation-pytorch

Semantic Segmentation on PyTorch (include FCN, PSPNet, Deeplabv3, Deeplabv3+, DANet, DenseASPP, BiSeNet, EncNet, DUNet, ICNet, ENet, OCNet, CCNet, PSANet, CGNet, ESPNet, LEDNet, DFANet)

book-code

《深度学习之PyTorch实战计算机视觉》全书代码

camvid

CamVid original data set, and the generated 11 category labels and training grayscale images.

chaotic-transformer-tutorials

clash_for_windows_pkg

A Windows GUI based on Clash

clips

A Tool for Building Expert Systems

cnn-svo

Real-time motion from structure

deep-learning-based-state-estimation

Incorporating Transformer and LSTM to Kalman Filter with EM algorithm

deep-speed-constrained-ins

Codes for Deep Learning Based Speed Estimation for Constraining Strapdown Inertial Navigation on Smartphones

deep_ekf_vio

deephmm

A PyTorch implementation of a Deep Hidden Markov Model [Structured Inference Networks for Nonlinear State Space Models]

depth

dso

Direct Sparse Odometry

ebunet

source code of our ERUNet

electron-ssr-backup

electron-ssr原作者删除了这个伟大的项目，故备份了下来，不继续开发,且用且珍惜

end-to-end-learning-framework-for-imu-based-6-dof-odometry

presents an end-to-end learning framework for performing 6-DOF odometry by using only inertial data obtained from a low-cost IMU.

equipment

equipmenttester

esekf_imu

A python implemented error-state extended Kalman Filter. Suit for learning EKF and IMU integration.

fbsnet

This repository is an official PyTorch implementation of our paper"FBSNet: A Fast Bilateral Symmetrical Network for Real-Time Semantic Segmentation". (TMM2022)

ggcnn

Generative Grasping CNN from "Closing the Loop for Robotic Grasping: A Real-time, Generative Grasp Synthesis Approach" (RSS 2018)

gslam

A General Simultaneous Localization and Mapping Framework which supports feature based or direct method and different sensors including monocular camera, RGB-D sensors or any other input types can be handled.

hyperpose

HyperPose

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.

learning-opencv-3_examples

learningnote

C++和Linux学习笔记

learnopencv

Learn OpenCV : C++ and Python Examples

lstmkf_iccv2017

Long Short-Term Memory Kalman Filters: Recurrent Neural Estimators for Pose Regularization

lwoi

Python code of the paper "Learning Wheel Odometry and IMU Errors for Localization"

magnetic-flux-anomaly-detection

Machine learning applied to magnetic flux datasets (.tdms files) to detect anomalies within the data.