MXNetJS: Javascript Package for Deep Learning in Browser (without server)
JavaScript HTML Makefile
Latest commit 5df2303 Feb 22, 2016 @tqchen tqchen Merge pull request #7 from rupeshs/master
Updated readme

README.md

MXNetJS Deep Learning in Browser

MXNetJS is the dmlc/mxnet Javascript package. MXNetJS brings state of art deep learning prediction API to the browser. It is generated with Emscripten and Amalgamation. MXNetJS allows you to run prediction of state-of-art deep learning models in any computational graph, and brings fun of deep learning to client side.

Try it on Browser

Online: http://webdocs.cs.ualberta.ca/~bx3/mxnet/classify.html

Local: Python User:

python -m SimpleHTTPServer

Then open browser http://localhost:8000/classify.html

NodeJS User:

npm install http-server -g
http-server

Then open browser http://127.0.0.1:8080/classify.html

See classify_image.js for how it works.

Speed

On Microsoft Edge and Firefox, performance is at least 8 times better than Google Chrome. We assume it is optimization difference on ASM.js.

Use Your Own Model

MXNetJS can take any model trained with mxnet, use tools/model2json.py to convert the model into json format and you are ready to go.

Library Code

  • mxnet_predict.js contains documented library code.
    • This is the code developer can call from
  • libmxnet_predict.js is automatically generated by make rebuild and should not be modified by hand.

Resources

Machine Eye -http://rupeshs.github.io/machineye/ Web service for local image file/image URL classification without uploading.

Contrbute to MXNetJS

Contribution is more than welcomed!