Jfinks sparsezoo doc update (#124) (#127)

README.md

@@ -53,7 +53,7 @@ limitations under the License.
 
 ## Overview
 
-SparseZoo is a constantly-growing repository of sparsified (pruned and pruned-quantized) models with matching sparsification recipes for neural networks. 
+[SparseZoo is a constantly-growing repository](https://sparsezoo.neuralmagic.com) of sparsified (pruned and pruned-quantized) models with matching sparsification recipes for neural networks. 
 It simplifies and accelerates your time-to-value in building performant deep learning models with a collection of inference-optimized models and recipes to prototype from. 
 Read more about sparsification [here](https://docs.neuralmagic.com/main/source/getstarted.html#sparsification).
 
@@ -66,8 +66,9 @@ The [GitHub repository](https://github.com/neuralmagic/sparsezoo) contains the P
 
 ## Highlights
 
-- [Available Models Listing](https://github.com/neuralmagic/sparsezoo/blob/main/docs/source/models.md)
-- [Available Recipes Listing](https://github.com/neuralmagic/sparsezoo/blob/main/docs/source/recipes.md)
+- [Model Stub Architecture Overview](https://github.com/neuralmagic/sparsezoo/blob/main/docs/source/models.md)
+- [Available Model Recipes](https://github.com/neuralmagic/sparsezoo/blob/main/docs/source/recipes.md)
+- [sparsezoo.neuralmagic.com](https://sparsezoo.neuralmagic.com)
 
 ## Installation
 
@@ -82,41 +83,6 @@ pip install sparsezoo
 
 ## Quick Tour
 
-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 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.
-
 ### Python APIS
 
 The Python APIs respect this format enabling you to search and download models. Some code examples are given below.

docs/index.rst

@@ -18,7 +18,8 @@
 .. toctree::
     :maxdepth: 3
     :caption: General
-
+
+    SparseZoo Repository <https://github.com/neuralmagic/sparsezoo/issues>
     source/models
     source/recipes