Overview
This model generates a new image that mixes the content of an input image with the style of another image. The model consists of a deep feed-forward convolutional net using a ResNet architecture, trained with a perceptual loss function between a dataset of content images and a given style image. The model was trained on the COCO 2014 data set and 4 different style images. The input to the model is an image, and the output is a stylized image. The model is based on the Pytorch Fast Neural Style Transfer Example.
Model Metadata
| Domain | Application | Industry | Framework | Training Data | Input Data Format |
|---|---|---|---|---|---|
| Vision | Style Transfer | General | Pytorch | COCO 2014 | Image (RGB/HWC) |
References
- J. Johnson, A. Alahi, L. Fei-Fei, “Perceptual Losses for Real-Time Style Transfer and Super-Resolution”, 2016
- D. Ulyanov, A. Vedaldi, V. Lempitsky, “Instance Normalization”, 2017
- D. Ulyanov, A. Vedaldi, V. Lempitsky, “Improved Texture Networks: Maximizing Quality and Diversity in Feed-forward Stylization and Texture Synthesis”, 2017
- Pytorch Tutorial
- Pytorch Fast Neural Style Transfer Example
Licenses
| Component | License | Link |
|---|---|---|
| Model GitHub Repository | Apache 2.0 | LICENSE |
| Model Weights | BSD-3-Clause | Pytorch Examples LICENSE |
| Model Code (3rd party) | BSD-3-Clause | Pytorch Examples LICENSE |
| Test Assets | CC0 | Samples README |
Options available for deploying this model
This model can be deployed using the following mechanisms:
Deploy from Dockerhub:
docker run -it -p 5000:5000 codait/max-fast-neural-style-transferDeploy on Red Hat OpenShift:
Follow the instructions for the OpenShift web console or the OpenShift Container Platform CLI in this tutorial and specify
codait/max-fast-neural-style-transferas the image name.Deploy on Kubernetes:
kubectl apply -f https://raw.githubusercontent.com/IBM/MAX-Fast-Neural-Style-Transfer/master/max-fast-neural-style-transfer.yamlA more elaborate tutorial on how to deploy this MAX model to production on IBM Cloud can be found here.
Locally: follow the instructions in the model README on GitHub
Example Usage
Once deployed, you can test the model from the command line. For example:
curl -F "image=@samples/bridge.jpg" -XPOST http://localhost:5000/model/predict?model=udnie > result.jpg && open result.jpg
Resources and Contributions
If you are interested in contributing to the Model Asset Exchange project or have any queries, please follow the instructions here.