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:
|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:
model.md: The model card containing metadata, descriptions, and information for the model.
model.onnx: The [ONNX](https://onnx.ai/) 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.