ciky奇's repositories
tf-cpn
a tensorflow implementation of CPN (Cascaded Pyramid Network for Multi-Person Pose Estimation)
cv-tricks.com
Repository for all the tutorials and codes shared at cv-tricks.com
Vehicle-Detection
Compare FasterRCNN,Yolo,SSD model with the same dataset
LRP
Localization Recall Precision Performance Metric toolkit for PASCAL-VOC, COCO datasets with Python and MATLAB implementations.
mace
MACE is a deep learning inference framework optimized for mobile heterogeneous computing platforms.
mace-models
Mobile AI Compute Engine Model Zoo
AutoStarter
This library helps bring up the autostart permission manager of a phone to the user so they can add an app to autostart.
keras
Deep Learning for humans
yolt
You Only Look Twice: Rapid Multi-Scale Object Detection In Satellite Imagery
python-machine-learning-book
The "Python Machine Learning (1st edition)" book code repository and info resource
Detectron
FAIR's research platform for object detection research, implementing popular algorithms like Mask R-CNN and RetinaNet.
Skill-Tree
🐼 准备秋招，欢迎来树上取果实
incubator-mxnet
Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more
CapsGAN
Unsupervised representation learning with CapsNet based Generative Adversarial Networks
pygta5
Explorations of Using Python to play Grand Theft Auto 5.
VS-ReID
Video Object Segmentation with Re-identification
master_thesis_code
Code for my master thesis: Vehicle Detection and Pose Estimation for Autonomous Driving
Deformable-ConvNets
Deformable Convolutional Networks
self-driving-car-1
Udacity Self-Driving Car Engineer Nanodegree projects.
TFlite_android_test
Tensorflow-lite移动端测试自己的模型
XX-Net
a web proxy tool
lanenet-lane-detection
Implemention of lanenet model for real time lane detection using deep neural network model
domain-transfer-network
TensorFlow Implementation of Unsupervised Cross-Domain Image Generation
LiteFlowNet
LiteFlowNet: A Lightweight Convolutional Neural Network for Optical Flow Estimation, CVPR18 (Spotlight)
tensorflow-yolo-v3
Implementation of YOLO v3 object detector in Tensorflow (TF-Slim)
Traffic-Condition-Recognition-Using-The-K-Means-Clustering-Method
Prediction of travel time has major concern in the research domain of Intel- ligent Transportation Systems (ITS). Clustering strategy can be used as a powerful tool of discovering hidden knowledge that can easily be applied on historical traffic data to predict accurate travel time. In our Modified K-means Clustering (MKC) approach, a set of historical data is portioned into a group of meaningful sub- classes (also known as clusters) based on travel time, frequency of travel time and velocity for a specific road segment and time group. The information from these are processed and provided back to the travellers in real time. Traffic flow modelling and driving condition analysis have many applications to various areas, such as Intelligent Trans- portation Systems (ITS), adaptive cruise control, pollutant emissions dispersion and safety.