Pinned Repositories
ActionRecognition
Explore Action Recognition
auditory_hallucinations_CNN-LSTM
Using CNN-LSTM networks to learn multi-modal representations of the marimba
awesome-data-labeling
A curated list of awesome data labeling tools
Awesome-Edge-Detection-Papers
:books: A collection of edge/contour/boundary detection papers and toolbox.
BCDU-Net
BCDU-Net : Medical Image Segmentation
ccnn
Code repository of the paper "Towards a General Purpose CNN for Long Range Dependencies in N-D" https://arxiv.org/abs/2206.03398.
ImageBasedModalAnalysisTutorial
High speed camera (or image) based experimental modal analysis
MAE-pytorch
Unofficial PyTorch implementation of Masked Autoencoders Are Scalable Vision Learners
precipitation-prediction-convLSTM-keras
CIKM contest entry 'Convolutional LSTM neural network to extrapolate radar images, and predict rainfall'
QtEVM
C++ implementation of EVM(Eulerian Video Magnification), based on OpenCV and Qt.
danny0559's Repositories
danny0559/ImageBasedModalAnalysisTutorial
High speed camera (or image) based experimental modal analysis
danny0559/MAE-pytorch
Unofficial PyTorch implementation of Masked Autoencoders Are Scalable Vision Learners
danny0559/Awesome-Edge-Detection-Papers
:books: A collection of edge/contour/boundary detection papers and toolbox.
danny0559/cnn-explainer
Learning Convolutional Neural Networks with Interactive Visualization.
danny0559/CNN-RIS
Making accurate object detection at the edge: review and new approach
danny0559/crack_segmentation
This repository contains code and dataset for the task crack segmentation using two architectures UNet_VGG16, UNet_Resnet and DenseNet-Tiramusu
danny0559/Data-Generators-DL-Video-Architecture
This repo presents the data pre-processing and accessing system used for machine learning applications in Python.
danny0559/Deep-Hough-Transform-Line-Priors
Official implementation for Deep-Hough-Transform-Line-Priors (ECCV 2020)
danny0559/deeplearning_ai_books
deeplearning.ai（吴恩达老师的深度学习课程笔记及资源）
danny0559/EVA
Fast Edge Video Analytics
danny0559/Eyes
danny0559/flownet2-pytorch
Pytorch implementation of FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks
danny0559/flownet3d
FlowNet3D: Learning Scene Flow in 3D Point Clouds (CVPR 2019)
danny0559/forza-painter
danny0559/lcnn
LCNN: End-to-End Wireframe Parsing
danny0559/motion-detection-using-optical-flow-estimation
The purpose of this project was to capture moving objects in a video sequence using sparse and dense optical flow methods. The final build can compute motion patterns of corners, edges (namely features), and also global pixels in consecutive image frames of different visual scenes and then draw them in corresponding bounding boxes.
danny0559/nodes2021_kg_workshop
danny0559/opencv_tutorials
Opencv4.0 with python (English&中文)， and will keep the update ! 👊
danny0559/Optical-Flow-Motion
Here we try to track the motion of the vehicles on a highway using the concept of Optical Motion Flow.
danny0559/optical_flow
Optical Flow for Cell Motion independent of Cell Tracking
danny0559/PaddleDetection
Object detection and instance segmentation toolkit based on PaddlePaddle.
danny0559/pyidi
Python Image Displacement Identification
danny0559/SegFormer
Official PyTorch implementation of SegFormer
danny0559/SlowFast
PySlowFast: video understanding codebase from FAIR for reproducing state-of-the-art video models.
danny0559/subpixel-edges
A pure Python implementation of the subpixel edge location algorithm
danny0559/tensorflow-convlstm-cell
A ConvLSTM cell with layer normalization and peepholes for TensorFlow's RNN API.
danny0559/tf-slim
danny0559/Time-series-classification-using-1-D-CNNs
This project is on how to Develop 1D Convolutional Neural Network Models for Human Activity Recognition Below is an example video of a subject performing the activities while their movement data is being recorded. The six activities performed were as follows: Walking Walking Upstairs Walking Downstairs Sitting Standing Laying The movement data recorded was the x, y, and z accelerometer data (linear acceleration) and gyroscopic data (angular velocity) from the smart phone, specifically a Samsung Galaxy S II. Observations were recorded at 50 Hz (i.e. 50 data points per second). Each subject performed the sequence of activities twice, once with the device on their left-hand-side and once with the device on their right-hand side. Pre-processing accelerometer and gyroscope using noise filters. Splitting data into fixed windows of 2.56 seconds (128 data points) with 50% overlap. Splitting of accelerometer data into gravitational (total) and body motion components.
danny0559/TrafficMonitor
这是一个用于显示当前网速、CPU及内存利用率的桌面悬浮窗软件，并支持任务栏显示，支持更换皮肤。
danny0559/YOLOV5-DeepSORT-Vehicle-Tracking-Master
In this project, urban traffic videos are collected from the middle section of Xi 'an South Second Ring Road with a large traffic flow, and interval frames are extracted from the videos to produce data sets for training and verification of YOLO V5 neural network. Combined with the detection results, the open-source vehicle depth model data set is u