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.

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.

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

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.

• mxnet_predict.js contains documented library code and provides convenient APIs to use in your JS application.
  • This is the API code your application should use. test_on_node.js shows an example.
• libmxnet_predict.js is automatically generated by running ./build.sh and should not be modified by hand.

test_on_node.js will exercise the forward pass inference for a few models available at the MXNet Model gallery. The model JSON files are prepared by running the script ./prepare_models.sh -all from the ./model folder. Currently the test exercises the following models

• InceptionBN
• SqueezeNET
• ResNET18
• NiN

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

Contribution is more than welcomed!