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README.md

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+---
+language: en
+license: apache-2.0
+model_name: candy-8.onnx
+tags:
+- validated
+- vision
+- style_transfer
+- fast_neural_style
+---
+<!--- SPDX-License-Identifier: BSD-3-Clause -->
+
+# Fast Neural Style Transfer
+
+## Use-cases
+This artistic style transfer model mixes the content of an image with the style of another image. Examples of the styles can be seen [in this PyTorch example](https://github.com/pytorch/examples/tree/master/fast_neural_style#models).
+
+
+## Description
+The model uses the method described in [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://arxiv.org/abs/1603.08155) along with [Instance Normalization](https://arxiv.org/pdf/1607.08022.pdf).
+
+
+## Model
+|Model        |Download  |Download (with sample test data)|ONNX version|Opset version|
+|-------------|:--------------|:--------------|:--------------|:--------------|
+|Mosaic|[6.6 MB](model/mosaic-9.onnx)  | [7.2 MB](model/mosaic-9.tar.gz)|1.4|9|
+|Candy|[6.6 MB](model/candy-9.onnx)  | [7.2 MB](model/candy-9.tar.gz)|1.4|9|
+|Rain Princess|[6.6 MB](model/rain-princess-9.onnx)  |[7.2 MB](model/rain-princess-9.tar.gz)|1.4|9|
+|Udnie|[6.6 MB](model/udnie-9.onnx)  | [7.2 MB](model/udnie-9.tar.gz)|1.4|9|
+|Pointilism|[6.6 MB](model/pointilism-9.onnx)  | [7.2 MB](model/pointilism-9.tar.gz)|1.4|9|
+|Mosaic|[6.6 MB](model/mosaic-8.onnx)  | [7.2 MB](model/mosaic-8.tar.gz)|1.4|8|
+|Candy|[6.6 MB](model/candy-8.onnx)  | [7.2 MB](model/candy-8.tar.gz)|1.4|8|
+|Rain Princess|[6.6 MB](model/rain-princess-8.onnx)  |[7.2 MB](model/rain-princess-8.tar.gz)|1.4|8|
+|Udnie|[6.6 MB](model/udnie-8.onnx)  | [7.2 MB](model/udnie-8.tar.gz)|1.4|8|
+|Pointilism|[6.6 MB](model/pointilism-8.onnx)  | [7.2 MB](model/pointilism-8.tar.gz)|1.4|8|
+<hr>
+
+## Inference
+Refer to [style-transfer-ort.ipynb](dependencies/style-transfer-ort.ipynb) for detailed preprocessing and postprocessing.
+
+### Input to model
+The input to the model are 3-channel RGB images. The images have to be loaded in a range of [0, 255]. If running into memory issues, try resizing the image by increasing the scale number.
+
+### Preprocessing steps
+```
+from PIL import Image
+import numpy as np
+
+# loading input and resize if needed
+image = Image.open("PATH TO IMAGE")
+size_reduction_factor = 1
+image = image.resize((int(image.size[0] / size_reduction_factor), int(image.size[1] / size_reduction_factor)), Image.ANTIALIAS)
+
+# Preprocess image
+x = np.array(image).astype('float32')
+x = np.transpose(x, [2, 0, 1])
+x = np.expand_dims(x, axis=0)
+```
+
+### Output of model
+The converted ONNX model outputs a NumPy float32 array of shape [1, 3, ‘height’, ‘width’]. The height and width of the output image are the same as the height and width of the input image.
+
+### Postprocessing steps
+```
+result = np.clip(result, 0, 255)
+result = result.transpose(1,2,0).astype("uint8")
+img = Image.fromarray(result)
+```
+<hr>
+
+## Dataset (Train and validation)
+The original fast neural style model is from [pytorch/examples/fast_neural_style](https://github.com/pytorch/examples/tree/master/fast_neural_style). All models are trained using the [COCO 2014 Training images dataset](http://cocodataset.org/#download) [80K/13GB].
+<hr>
+
+## Training
+Refer to [pytorch/examples/fast_neural_style](https://github.com/pytorch/examples/tree/master/fast_neural_style) for training details in PyTorch. Refer to [conversion.ipynb](dependencies/conversion.ipynb) to learn how the PyTorch models are converted to ONNX format.
+<hr>
+
+
+## References
+Original style transfer model in PyTorch: <https://github.com/pytorch/examples/tree/master/fast_neural_style>
+<hr>
+
+## Contributors
+[Jennifer Wang](https://github.com/jennifererwangg)
+<hr>
+
+## License
+BSD-3-Clause
+<hr>
+