- #Cartoon photo converter how to#
- #Cartoon photo converter generator#
- #Cartoon photo converter android#
Add metadata to enable easy integration with a mobile app.
Run inference in Python with the converted model.Convert SavedModel with post-training quantization using the latest TFLiteConverter.Generate a SavedModel out of the pre-trained model checkpoints.Here is a step-by-step summary of what we will be covering in this section: All of the code discussed in this section is available on GitHub here. This is why we will first convert these pre-trained weights to TFLite which would be much more suitable to go inside a mobile application. However, those weights are not ideal if we were to develop a mobile application without having to make API calls to fetch them. Create the TensorFlow Lite ModelThe authors of White-box CartoonGAN provide pre-trained weights that can be used for making inference on images.
#Cartoon photo converter generator#
For this tutorial, we used the generator part of White-box CartoonGAN. For more details about the model check out the paper Learning to Cartoonize Using White-box Cartoon Representations by Xinrui Wang and Jinze Yu. The goal here is to produce a cartoonized image from an input image that is visually and semantically aesthetic. White-box CartoonGAN is a type of generative adversarial network that is capable of transforming an input image (preferably a natural image) to its cartoonized representation.
#Cartoon photo converter how to#
While this tutorial discusses the steps of how to create the TFLite models, feel free to download them directly from TensorFlow Hub here and get started using them in your own applications. If you are not familiar with the SavedModel format, please refer to the TensorFlow documentation for details.
#Cartoon photo converter android#
Please follow along with the notebooks here for model saving/conversion, populating metadata and the Android code on GitHub here. Guide developers on how to create a mobile application with TFLite models easily, with ML Model Binding feature from Android Studio.Understand how to use the TFLite tools such as the Android Benchmark Tool, Model Metadata, and Codegen.tflite models directly from TensorFlow Hub if you are only interested in using the models for deployment. (In order to convert TensorFlow 2.x models in TFLite please follow this guide.) Provide a reference for the developers looking to convert models written in TensorFlow 1.x to their TFLite variants using the new features of the latest (v2) converter - for example, the MLIR-based converter, more supported ops, and improved kernels, etc.We created this end-to-end tutorial to help developers with these objectives: This is an end-to-end tutorial on how to convert a TensorFlow model to TensorFlow Lite (TFLite) and deploy it to an Android app for cartoonizing an image captured by the camera. A guest post by ML GDEs Margaret Maynard-Reid (Tiny Peppers) and Sayak Paul (PyImageSearch)
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