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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.

Specification

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

Accuracy

Accuracy metrics are obtained on Common Objects in Context (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

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.

Download a Model and Convert it into OpenVINO™ IR Format

You can download models and if necessary convert them into OpenVINO™ IR format using the Model Downloader and other automation tools as shown in the examples below.

An example of using the Model Downloader:

omz_downloader --name <model_name>

An example of using the Model Converter:

omz_converter --name <model_name>

Demo usage

The model can be used in the following demos provided by the Open Model Zoo to show its capabilities:

Legal Information

The original model is distributed under the following license:

MIT License

Copyright (c) ONNX Project Contributors

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.