# mxnet.js

**Repository Path**: mirrors_dmlc/mxnet.js

## Basic Information

- **Project Name**: mxnet.js
- **Description**: MXNetJS: Javascript Package for Deep Learning in Browser (without server)
- **Primary Language**: Unknown
- **License**: Apache-2.0
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-08-08
- **Last Updated**: 2026-02-21

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

MXNetJS Deep Learning in Browser
================================
MXNetJS is the [Apache MXNet](https://github.com/apache/incubator-mxnet) Javascript package. MXNetJS brings state of art deep learning inference API to the browser.
It is generated with [Emscripten](https://github.com/kripken/emscripten) and [MXNet Amalgamation](https://github.com/apache/incubator-mxnet/tree/master/amalgamation).
MXNetJS allows you to run prediction of state-of-art deep learning models in any computational graph, and brings the fun of deep learning to the client side.

Try it in your browser
-----------------

This requires Python 2:
```
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](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](tools/model2json.py) to convert the model into json format and you are ready to go (note that only Python 2 is supported currently)


Library Code
------------
- [mxnet_predict.js](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](test_on_node.js) shows an example.
- [libmxnet_predict.js](libmxnet_predict.js) is automatically generated by running ```./build.sh``` and should not be modified by hand.


Unit Tests
----------
[test_on_node.js](test_on_node.js) will exercise the forward pass inference for a few models available at the [MXNet Model gallery](https://github.com/dmlc/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


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!