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## 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](https://onnx.ai/) 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 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

<div>
    <iframe src="https://sparsezoo.neuralmagic.com/tables/models/cv/classification" title="Image Classification Models" width="100%" height="500px"></iframe>
</div>

Image classification table not loading? View full table [here](https://sparsezoo.neuralmagic.com/tables/models/cv/classification). 

### Object Detection

<div>
    <iframe src="https://sparsezoo.neuralmagic.com/tables/models/cv/detection" title="Object Detect Models" width="100%" height="500px"></iframe>
</div>

Object detection table not loading? View full table [here](https://sparsezoo.neuralmagic.com/tables/models/cv/detection).