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pytorch-doc-zh

Pytorch 中文文档

qrcode

Experimental Python QR Code Generator

retinanet-examples

Fast and accurate object detection with end-to-end GPU optimization

rolo

ROLO is short for Recurrent YOLO, aimed at simultaneous object detection and tracking

ros_exploring

《ROS机器人开发实践》源码

sdnn_python

self-driving-car

sfmlearner

An unsupervised learning framework for depth and ego-motion estimation from monocular videos

siamfc-tf

State-of-the-art object tracking at high framerates, in TensorFlow.

simpledet_and_mmdet_notes

simpledet和mmdetection源码阅读笔记

slambook

snn

spatialspikingneuralnetworks

C and Matlab code for simulatung large, spatially extended spiking neuronal networks

spencer_people_tracking

Multi-modal ROS-based people detection and tracking framework for mobile robots developed within the context of the EU FP7 project SPENCER.

spiking-neural-network---theano-framework

Spiking neural networks are biologically plausible CNNs which learn through a temporally dependent learning method known as Spike Time Dependant Plasticity (STDP)- an alternate to gradient descent. This repository contains layers built on top of Lasagne layers for spiking neural networks. This is the first implementation of spiking neural networks in any tensor based framework to the best of my knowledge. The various layers can be found in snn.py for dense layer and snn_conv.py for other layers. These layers are to be processed for each time step which is done using the Theano scan as a quick hack - in the snn class. The results can be found the ppt. Further details on how to use the code will be put up after later.

spiking-neural-network-simulations

A series of simulations organized as homework assignments that were part of the course on Neuromorphic Engineering at IIT Bombay (course code: EE 746)

spikingneuralnetworks

Toy code written for learning neural networks and processing event-based data as part of a winter research project at UQ

ssd_tensorflow_traffic_sign_detection

Implementation of Single Shot MultiBox Detector in TensorFlow, to detect and classify traffic signs

stdp-mnist

tbcf

Tracking Benchmark for Correlation Filters

tensorflow

Computation using data flow graphs for scalable machine learning

tensorflow-and-deeplearning-tutorial

TensorFlow & Deep Learning Tutorial

tensorflow-posenet

Implementation of Posenet in TensorFlow

tensorflow-yolo

tensorflow implementation of 'YOLO : Real-Time Object Detection'(train and test)

tensorrt-yolov4

tensorrt5, yolov4, yolov3,yolov3-tniy,yolov3-tniy-prn

tf-model-service

Flask microservice of TensorFlow models

tffrcnn

FastER RCNN built on tensorflow

theano

Theano is a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. It can use GPUs and perform efficient symbolic differentiation.

tracking-with-darkflow

Real-time people Multitracker using YOLO v2 and deep_sort with tensorflow

traffic-sign-classification

This is the second project that is submitted towards completing Self Driving Car Nanodegree from Udacity. In this project, we develop a LeNet based neural network that detects the traffic signs commonly observed by a car on roads. A German dataset of traffic sign images that is considered as benchmark is used for training and validating the network. The database contains more than 30000 images of various traffic signals. The images were captured in different situations. The architecture of the network is inspired from one of the most celebrated researcher Yann LeCunn who pioneed the LeNet that was actually made for handwritting detection. The accuracy of the network is around 96%.