Update README.md- Description, contribution and License

[elboyran]

Description text, License and Contributers

Model Name

Description

Description of model - What task does it address (i.e. object detection, image classification)? What is the main advantage or feature of this model's architecture?

All ONNX models must pass the ONNX model checker before contribution. The snippet of code below can be used to perform the check. If any errors are encountered, it implies the check has failed.

import onnx
from onnx import checker
model_proto = onnx.load("path to .onnx file")
checker.check_model(model_proto)

Contribute a Gradio Demo to ONNX Organization on Hugging Face

• Create an account on Hugging Face: https://huggingface.co/join
• See list of models left to add to ONNX organization, please refer to the table with the Models list
• Add Gradio Demo under your username, see blog for setting up Gradio Demo on Hugging Face: https://huggingface.co/blog/gradio-spaces
• Request to join ONNX Organization: https://huggingface.co/onnx
• Once approved transfer model from your username to ONNX organization
• Add a badge for model in model table, see examples in Models list

Model

Model	Download	Download (with sample test data)	ONNX version	Opset version	Accuracy
Model Name	Relative link to ONNX Model with size	tar file containing ONNX model and synthetic test data (in .pb format)	ONNX version used for conversion	Opset version used for conversion	Accuracy values
Example (VGG 19)	548.1 MB	508.5 MB	1.2.1	7	73.72

Please submit new models with Git LFS by committing directly to the repository, and using relative links (i.e. model/vgg19-7.onnx) in the table above. In this file name example, vgg19 is the name of the model and 7 is the opset number.

Source

Source Framework ==> ONNX model
i.e. Caffe2 DenseNet-121 ==> ONNX DenseNet

Inference

Step by step instructions on how to use the pretrained model and link to an example notebook/code. This section should ideally contain:

Input

Input to network (Example: 224x224 pixels in RGB)

Preprocessing

Preprocessing required

Output

Output of network

Postprocessing

Post processing and meaning of output

Model Creation

Dataset (Train and validation)

This section should discuss datasets and any preparation steps if required.

Training

Training details (preprocessing, hyperparameters, resources and environment) along with link to a training notebook (optional).

Also clarify in case the model is not trained from scratch and include the source/process used to obtain the ONNX model.

Validation accuracy

Validation script/notebook used to obtain accuracy reported above along with details of how to use it and reproduce accuracy. Details of experiments leading to accuracy from the reference paper.

Test Data Creation

Creating test data for uploaded models can help CI to verify the uploaded models by ONNXRuntime utilties. Please upload the ONNX model with created test data (test_data_set_0) in the .tar.gz.

Requirement

pip install onnx onnxruntime numpy
git clone https://github.com/onnx/models.git

Usage

def create_test_dir(model_path, root_path, test_name,
                    name_input_map=None, symbolic_dim_values_map=None,
                    name_output_map=None):
    """
    Create a test directory that can be used with onnx_test_runner, onnxruntime_perf_test.
    Generates random input data for any missing inputs.
    Saves output from running the model if name_output_map is not provided.

    :param model_path: Path to the onnx model file to use.
    :param root_path: Root path to create the test directory in.
    :param test_name: Name for test. Will be added to the root_path to create the test directory name.
    :param name_input_map: Map of input names to numpy ndarray data for each input.
    :param symbolic_dim_values_map: Map of symbolic dimension names to values to use for the input data if creating
                                    using random data.
    :param name_output_map: Optional map of output names to numpy ndarray expected output data.
                            If not provided, the model will be run with the input to generate output data to save.
    :return: None
    """

Example

The input/output .pb files will be produced under temp/examples/test1/test_data_set_0.

import sys
sys.path.append('<onnx/models root dir>/workflow_scripts/')
import ort_test_dir_utils
import numpy as np

# example model with two float32 inputs called 'input' [batch_size, 1, 224, 224])
model_path = '<onnx/models root dir>/vision/super_resolution/sub_pixel_cnn_2016/model/super-resolution-10.onnx'

# If there is no symbolic_dim
ort_test_dir_utils.create_test_dir(model_path, 'temp/examples', 'test1')

# when using the default data generation any symbolic dimension values can be provided
# otherwise the default value for missing symbolic_vals would be 1

symbolic_vals = {'batch_size': 1} # provide value for symbolic dim named 'batch_size'

# let create_test_dir create random input in the (arbitrary) default range of -10 to 10. 
# it will create data of the correct type based on the model.
ort_test_dir_utils.create_test_dir(model_path, 'temp/examples', 'test1', symbolic_dim_values_map=symbolic_vals)

# alternatively some or all input can be provided directly. any missing inputs will have random data generated.
batch_size = 64
inputs = {'input': np.random.rand(batch_size, 1, 224, 224).astype(np.float32)}

ort_test_dir_utils.create_test_dir(model_path, 'temp/examples', 'test2', name_input_map=inputs)

More details

https://github.com/microsoft/onnxruntime/blob/master/tools/python/PythonTools.md

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Update ONNX_HUB_MANIFEST.json for ONNX Hub

If this PR does update/add .onnx or .tar.gz files, please use python workflow_scripts/generate_onnx_hub_manifest.py --target diff to update ONNX_HUB_MANIFEST.json with according model information (especially SHA) for ONNX Hub.

References

Link to paper or references.

Contributors

Contributors' names

License

Add license information - on default, Apache 2.0

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[cwmeijer]

Clear intro text 👍

[cwmeijer]

Suggested change

LeafSnap30 is a Neural Network model trained on the [LeafSnap 30 dataset](https://zenodo.org/record/5061353/). It addresses an image classificaiton task- identifying 30 tree species from images of their leaves. This task has been approached with classical computer vision methods a decade ago on more species dataset containing artifacts, while this model is trained on a smaller and cleaner dataset, particularly useful for demonstrating NN classification on simple, yet realistic scientific task.

LeafSnap30 is a Neural Network model trained on the [LeafSnap 30 dataset](https://zenodo.org/record/5061353/). It addresses an image classification task- identifying 30 tree species based on the images of their leaves. This task has been approached with classical computer vision methods a decade ago on the full [leafsnap dataset](http://leafsnap.com/dataset/). This model is trained on Leafsnap30 dataset, a cleaned version of a subset of the 30 largest classes, particularly useful for demonstrating NN classification on a simple, yet realistic scientific task.