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tiny-dnn documentations

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tiny-dnn documentations ########################

tiny-dnn <https://github.com/tiny-dnn/tiny-dnn/>_ is a header only, dependency free deep learning library written in C++. It is designed to be used in the real applications, including IoT devices and embedded systems.

Table of Contents

Getting Started:

.. toctree:: :maxdepth: 1

getting_started/index

How-Tos:

.. toctree:: :maxdepth: 1

how_tos/index

Layers:

.. toctree:: :maxdepth: 1

layers/index

Examples(Link to github):

  • MNIST image classification <https://github.com/tiny-dnn/tiny-dnn/tree/master/examples/mnist>_
  • Cifar-10 image classification <https://github.com/tiny-dnn/tiny-dnn/tree/master/examples/cifar10>_
  • Deconvolutional Auto-encoder <https://github.com/tiny-dnn/tiny-dnn/tree/master/examples/deconv>_
  • Importing Caffe's model into tiny-dnn <https://github.com/tiny-dnn/tiny-dnn/tree/master/examples/caffe_converter>_

Update Logs:

.. toctree:: :maxdepth: 1

update_log/index

Developer Guides:

.. toctree:: :maxdepth: 1

developer_guides/index

External Links

Here is the list of apps/papers using tiny-dnn. I'm willing to update this list if your software use tiny-dnn. Please contact me at nyanpn (at) gmail (dot) com.

Applications

  • zhangqianhui/CnnForAndroid <https://github.com/zhangqianhui/CnnForAndroid>_ - A Vehicle Recognition Project using Convolutional Neural Network(CNN) in Android platform
  • edgarriba/opencv_contrib <https://github.com/edgarriba/opencv_contrib>_ (in progress) - A new opencv's dnn module which use tiny-dnn as its backend

Papers

  • S.S.Sarwar, S.Venkataramani, A.Raghunathan, and K.Roy, Multiplier-less Artificial Neurons Exploiting Error Resiliency for Energy-Efficient Neural Computing <http://arxiv.org/abs/1602.08557>_
  • A.Viebke, Accelerated Deep Learning using Intel Xeon Phi <http://homepage.lnu.se/staff/saplaa/papers/Andre_Viebke_thesis_final.pdf>_
  • Y.Xu, M.Berger, Q.Qian and E.O.Tuguldur, DAInamite - Team Description 2016 <http://www.robocup2016.org/media/symposium/Team-Description-Papers/StandardPlatform/RoboCup_2016_SPL_TDP_DAInamite.pdf>_
  • K.Kim, H.Woo, Y.Han, K.Cho, H.Moon, D.Han, Diagnosis-Prescription System for Plant Disease using Mobile Device <http://www.eiric.or.kr/community/post2.php?m=view&gubun=201607&num=5090&pg=33&seGubun=&seGubun1=&SnxGubun=%C6%F7%BD%BA%C5%CD&searchBy=&searchWord=>_ (Written in Korean)