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knightofdawn's Projects

motor-imagery-eeg-data icon motor-imagery-eeg-data

It includes Motor Imagery EEG data for 15 subjects collected at BCI-HCI Labaratory at IIT Kharagpur.

motorimageryml icon motorimageryml

A comparison of different machine learning algorithms for binary classification of EEG signals elicited from motor imagery

mtcnn-caffe icon mtcnn-caffe

Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Neural Networks

multi-contact-grasping icon multi-contact-grasping

This project implements a simulated grasp-and-lift process in V-REP using the Barrett Hand, with an interface through a python remote API.

mx-rcnn icon mx-rcnn

Faster R-CNN, an MXNet implementation with distributed implementation and data parallelization.

mxnet icon mxnet

Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more

mxnet-ssd icon mxnet-ssd

MXNet port of SSD: Single Shot MultiBox Object Detector. Reimplementation of https://github.com/weiliu89/caffe/tree/ssd

my-blog icon my-blog

:palm_tree::octocat:A simple & beautiful blogging system implemented with spring-boot & thymeleaf & mybatis My Blog 是由 SpringBoot + Mybatis + Thymeleaf 等技术实现的 Java 博客系统,页面美观、功能齐全、部署简单及完善的代码,一定会给使用者无与伦比的体验

mytech icon mytech

Java的基础总结和学习笔记,包括Java核心技术点和常见知识点。同时提供了Java基础原理的代码实现,供大家实践时参考。已补充JVM和JUC的相关内容,欢迎交流。

nauta icon nauta

A multi-user, distributed computing environment for running DL model training experiments on Intel® Xeon® Scalable processor-based systems

nlp-datasets icon nlp-datasets

A list of datasets/corpora for NLP tasks, in reverse chronological order.

nnie icon nnie

重构海思sample中的NNIE模块

nniefacelib icon nniefacelib

nniefacelib是一个在海思35xx系列芯片上运行的人脸算法库

nupic.kaggle-eeg icon nupic.kaggle-eeg

Using NuPIC on EEG data to identify seizures - team entry for the Kaggle competition.

object-detection icon object-detection

Object detection project for real-time (webcam) and offline (video processing) application.

object-detection-with-deep-learning-and-sliding-window icon object-detection-with-deep-learning-and-sliding-window

Introduces an approach for object detection in an image with sliding window. The repository contains three files, make_data.py reads the image in gray scale and converts the image into a numpy array. The labels are also appended based on the file name. In this case, if the file name starts with "trn", then 1 is appended else 0. Finally, all the images and labels are saved into .npy file. The test-model-1.py file loads the images and converts the labels into two categories as we are doing binary classification of images. The model is built using keras with theano as backend. In this case, the best training accuracy was 80% since the data was just 500 images and the testing accuracy was 67%

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