DJL - Apache MXNet model zoo


The model zoo contains symbolic models from Apache MXNet (incubating) that can be used for inference and training. All the models in this model zoo contain pre-trained parameters for their specific datasets.


The latest javadocs can be found on here.

You can also build the latest javadocs locally using the following command:

# for Linux/macOS:
./gradlew javadoc

# for Windows:
..\..\gradlew javadoc

The javadocs output is built in the build/doc/javadoc folder.


You can pull the MXNet engine from the central Maven repository by including the following dependency in you pom.xml file:


Pre-trained models

The MXNet model zoo contains two major categories: Computer Vision (CV) and Natural Language Processing (NLP). All the models are grouped by task under these two categories as follows:

How to find a pre-trained model in model zoo

In a model zoo repository, there can be many pre-trained models that belong to the same model family. You can use the ModelZoo class to search for the model that you need. First, decide which model family you want to use. Then, define your key/values search criteria to narrow down the model you want. If there are multiple models that match your search criteria, the first model found is returned. ModelNotFoundException will be thrown if no matching model is found.

The following is an example of the criteria to find a Resnet50-v1 model that has been trained on the imagenet dataset:

Criteria<Image, Classifications> criteria = Criteria.builder()
        .setTypes(Image.class, Classifications.class)
        .optFilter("layers", "50")
        .optFilter("flavor", "v1")
        .optFilter("dataset", "imagenet")

ZooModel<Image, Classifications> model = criteria.loadModel();

List of search criteria for each model

The following table illustrates the possible search criteria for all models in the model zoo:

Category Application Model Family Criteria Possible values
CV Action Recognition ActionRecognition backbone vgg16, inceptionv3
      dataset ucf101
  Image Classification MLP dataset mnist
    Resnet layers 18, 34, 50, 101, 152
      flavor v1, v2, v1d
      dataset imagenet, cifar10
    Resnext layers 101, 150
      flavor 32x4d, 64x4d
      dataset imagenet
    Senet layers 154
      dataset imagenet
    SeResnext layers 101, 150
      flavor 32x4d, 64x4d
      dataset imagenet
  Instance Segmentation mask_rcnn backbone resnet18, resnet50, resnet101
      flavor v1b, v1d
      dataset coco
  Object Detection SSD size 300, 512
      backbone vgg16, mobilenet, resnet18, resnet50, resnet101, resnet152
      flavor atrous, 1.0, v1, v2
      dataset coco, voc
  Pose Estimation SimplePose backbone resnet18, resnet50, resnet101, resnet152
      flavor v1b, v1d
      dataset imagenet
NLP Question and Answer BertQA backbone bert
      dataset book_corpus_wiki_en_uncased

Note: Not all combinations in the above table are available. For more information, see the metadata.json file in the src/test/resources/mlrepo/model folder.

Contributor Guides and Documentation

How to add new models to the model zoo