Deep Learning in a Single File for Smart Devices(dmlc.ml)
dmlc.ml
Deep Learning in a Single File for Smart Devices
http://dmlc.ml/mxnet/2015/11/10/deep-learning-in-a-single-file-for-smart-device.html
4 comments
The author is here :) Thanks for your suggestion. The detail of network modification will be released in my thesis, there is a "fast good net" but currently I don't have time to make it faster, so I simply use fast poor net.
I saw a demo of a deep convnet (I assume) running on a tablet at a conference recently. While it's limited to the vocabulary that the net is trained on, it's seriously impressive seeing this stuff work in realtime.
One of the other speakers was giving a tech demo using his webcam. He was looking around for a mug to demonstrate that the classification was good and could work quickly. In the meantime the camera was looking behind him on stage and correctly classified the image as "theatre curtains". It was particularly cool because image processing results are often cherrypicked to show optimal performance and you learn to be skeptical.
One of the other speakers was giving a tech demo using his webcam. He was looking around for a mug to demonstrate that the classification was good and could work quickly. In the meantime the camera was looking behind him on stage and correctly classified the image as "theatre curtains". It was particularly cool because image processing results are often cherrypicked to show optimal performance and you learn to be skeptical.
Thumb up for Qualcomm demo and Andrea Vedaldi presentation of MatConvNet presented at ICVSS 2015 summer school (not a conference). Both pieces made similar impression on me.
Link to the actual 30k line C++ file:
https://raw.githubusercontent.com/jdeng/gomxnet/master/mxnet...
Compiles with "clang++ -lblas mxnet.cc -std=c++11", so I'm not disappointed :)
Compiles with "clang++ -lblas mxnet.cc -std=c++11", so I'm not disappointed :)
[deleted]
I think there's some content still to be added here:
> Then we remove some layers. then further remove somethings, but now the results are too pool. more explains, and the results table.
> After compiled library for smart platform, the last thing is call C-API in the target language (Jave/Swift).
"After the library is compiled for the smart platform, the last thing to do is call the C API from the target language (Java/Swift)".
On the actual topic, I really like the ability now to take powerful recognition frameworks and put them onto "small" devices. Great work.