Models
Each model in the SparseZoo has a specific stub that identifies it. The stubs are made up of the following structure:
DOMAIN/SUB_DOMAIN/ARCHITECTURE{-SUB_ARCHITECTURE}/FRAMEWORK/REPO/DATASET{-TRAINING_SCHEME}/SPARSE_NAME-SPARSE_CATEGORY-{SPARSE_TARGET}
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:
model.md: The model card containing metadata, descriptions, and information for the model.
model.onnx: The ONNX representation of the model’s graph.
- model.onnx.tar.gz: A 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 preprocessing 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.