Each model in the SparseZoo has a specific stub that identifies it. The stubs are made up of the following structure:


The properties within each model stub are defined as the following:

Model Property Definition Examples
DOMAIN The type of solution the model is architected and trained for cv, nlp
SUB_DOMAIN The sub type of solution the model is architected and trained for classification, segmentation
ARCHITECTURE The name of the guiding setup for the network's graph resnet_v1, mobilenet_v1
SUB_ARCHITECTURE (optional) The scaled version of the architecture such as width or depth 50, 101, 152
FRAMEWORK The machine learning framework the model was defined and trained in pytorch, tensorflow_v1
REPO The model repository the model and baseline weights originated from sparseml, torchvision
DATASET The dataset the model was trained on imagenet, cifar10
TRAINING_SCHEME (optional) A description on how the model was trained augmented, lower_lr
SPARSE_NAME An overview of what was done to sparsify the model base, pruned, quant (quantized), pruned_quant, arch (architecture modified)
SPARSE_CATEGORY Descriptor on the degree to which the model is sparsified as compared with the baseline metric none, conservative (100% baseline), moderate (>= 99% baseline), aggressive (< 99%)
SPARSE_TARGET (optional) Descriptor for the target environment the model was sparsified for disk, edge, deepsparse, gpu

The contents of each model are made up of the following:

  • The model card containing metadata, descriptions, and information for the model.

  • model.onnx: The [ONNX]( representation of the model’s graph.

  • model.onnx.tar.gzA compressed format for the ONNX file.

    Currently, ONNX does not support sparse tensors and quantized sparse tensors well for compression.

  • [FRAMEWORK]/model.[EXTENSION]The native ML framework file(s) for the model in which it was originally trained.

    Such as PyTorch, Keras, TensorFlow V1

  • recipes/original.[md|yaml]: The original sparsification recipe used to create the model.

  • recipes/[NAME].[md|yaml]: Additional sparsification recipes that can be used with the model such as transfer learning.

  • sample-originals: The original sample data without any pre-processing for use with the model.

  • sample-inputs: The sample data after pre-processing for use with the model.

  • sample-outputs: The outputs after running the sample inputs through the model.

  • sample-labels: The labels that classify the sample inputs.

Image Classification

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Object Detection

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