Model Name

Use-cases

Model use cases

Description

Description of model

Model

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)

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 to model

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

Preprocessing steps

Preprocessing required

Output of model

Output of network

Postprocessing steps

Post processing and meaning of output

Dataset (Train and validation)

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

Validation accuracy

Details of experiments leading to accuracy and comparison with the reference paper.

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

Validation script/notebook used to obtain accuracy reported above along with details of how to use it and reproduce accuracy.

References

Link to references

Contributors

Contributors' name

License

Add license information