MXNetJS Deep Learning in Browser
Try it in your browser
This requires Python 2:
python -m SimpleHTTPServer
Then open browser http://localhost:8000/classify.html
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.
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 (note that only Python 2 is supported currently)
- 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.shand 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
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!