fast-neural-style-mosaic-onnx

Use Case and High-Level Description

The fast-neural-style-mosaic-onnx model is one of the style transfer models designed to mix the content of an image with the style of another image. The model uses the method described in Perceptual Losses for Real-Time Style Transfer and Super-Resolution along with Instance Normalization. Original ONNX models are provided in the repository.

Example

Specification

Metric	Value
Type	Style Transfer
GFLOPs	15.518
MParams	1.679
Source framework	PyTorch*

Accuracy

Accuracy metrics are obtained on MS COCO val2017 dataset. Images were resized to input size.

Metric	Original model	Converted model (FP32)	Converted model (FP16)
PSNR	12.03dB	12.03dB	12.04dB

Performance

Input

Original model

Image, name - input1, shape - 1,3,224,224, format is B,C,H,W where:

• B - batch size
• C - channel
• H - height
• W - width

Expected color order: RGB.

Converted model

Image, name - input1, shape - 1,3,224,224, format is B,C,H,W where:

• B - batch size
• C - channel
• H - height
• W - width

Expected color order: BGR.

Output

Original model

Image, name - output1, shape - 1,3,224,224, format is B,C,H,W where:

• B - batch size
• C - channel
• H - height
• W - width

Expected color order: RGB.

Converted model

Image, name - output1, shape - 1,3,224,224, format is B,C,H,W where:

• B - batch size
• C - channel
• H - height
• W - width

Expected color order: RGB.

Legal Information

The original model is distributed under the following license:

MIT License

Copyright (c) ONNX Project Contributors

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