A simple single label classification model, ResNet18, to predict the cat or dog breed from the provided image.  The model was created in Fast.ai
and exported to ONNX using PyTorch's ONNX export capabilities.  

The source dataset is the OXFORD-IIIT PET.  Omkar M Parkhi, Andrea Vedaldi, Andrew Zisserman and C. V. Jawahar
We have created a 37 category pet dataset with roughly 200 images for each class.
The images have a large variations in scale, pose and lighting. All images havean
associated ground truth annotation of breed, head ROI, and pixel level trimap segmentation.
 
The ONNX model can be used in other frameworks like Elixir's Axon.  An example of converting the ONNX model into Axon can be found at:
https://github.com/elixir-nx/axon/tree/main/notebooks/onnx_to_axon.livemd.

{"license": "mit"}
onnx license:mit region:us
TAGS
#onnx #license-mit #region-us 

A simple single label classification model, ResNet18, to predict the cat or dog breed from the provided image.  The model was created in URL
and exported to ONNX using PyTorch's ONNX export capabilities.  

The source dataset is the OXFORD-IIIT PET.  Omkar M Parkhi, Andrea Vedaldi, Andrew Zisserman and C. V. Jawahar
We have created a 37 category pet dataset with roughly 200 images for each class.
The images have a large variations in scale, pose and lighting. All images havean
associated ground truth annotation of breed, head ROI, and pixel level trimap segmentation.
 
The ONNX model can be used in other frameworks like Elixir's Axon.  An example of converting the ONNX model into Axon can be found at:
URL

TAGS
#onnx #license-mit #region-us 

passage: TAGS
#onnx #license-mit #region-us 
