json file, which was generated by the model_converter in the privious step. For the parameter value copy the content from the. To create a new modelConfig click on “Remote Config” in the left navigation panel. This can be very useful when you want to compare your debug version with the latest release version. This has the advantage, that the debug version does not overwrite a realease version installed on your phone. Why the second app configuration with the “.debug” package name is needed, is because the app project is configured to create the beta and release versions of the app under the package name “at.” and the version which is used for debugging under “at.debug”. to connect the app with this specific Firebase project. You must copy this file to the Android Studio folder into the /app folder. When asked for the package name of the app - type inĪnd at the end save the google-services.json file to your local drive. If you haven’t already an active Firebase project connected with the app or if u want to create a new Firebase project with another Google account you first need to configure Google Firebase and setup a new android project.įor this just follow the steps from the Google guide. ![]() When the user has selected a model, this specific model file is requested from Firebase. The app uses this information to generate a list of the model names and present it to the user. The config contains a list of all available models in the cloud with their specific properties. The communication happens in the following manner: The app is requesting the remote config from Firebase. In this key we store the model label, the linked model file from the machine learning, the model input size and the labelmap. ![]() The remote Config API is responsible for downloading a specific key from the google cloud. The machine learning API is responsible for downloading the models from the Google cloud. The big advantage with this implementation is, that you don’t have to recompile the app when you want to change a model.įor this the app is using 2 Google Firebase APIs: Machine Learning and Remote Config. The app is designed to work with the object detection models hosted on Google Firebase. tflite model file, and one is the JSON file for the remote configuration. ![]() After the conversion is finished, you should see a folder TFLITE_OUTPUT in your OUTPUT_DIR directory. The conversion can take up to a few minutes. If true, you must also provide the dataset_size parameter. Set to True, when a post training quantization should be performed. In the Output folder there will be a TFLITE_OUTPUT folder generated. Run the installer and leave all settings on default.Īfter finishing open up the Anaconda Prompt with admin permissions.Ĭonvert.py -model_dir MODEL_DIR -output_dir OUTPUT_DIRĭirectory which must contain a saved_model in a folder “saved_model”ĭirectory where the converted TFLite model should be saved”. Setup the environment Installing Anacondaįirst go to the official Anaconda Website and download the Windows 圆4 installer for the individual edition. In the image you can see a structured overview of the three main components of this project: The model conversion, the remote model hosting and the app itself.Ģ. The goal is to get a detailed understanding of what steps it takes to get an existing model into the app and how the app is structured. This guide should act as a single source for all tools you need to use the app. Therefore you have to search multiple sites for answers. But due to the rapid development of the tensorflow ecosystem sometimes commands or APIs are deprecated or got replaced, workflows have changed with newer versions of the tools and so forth. tflite format and bringing it to an Android Smartphone. Also there are a lot of good tutorials on converting models to the. You can find a lot of object detection demonstration apps (like the official one from the TensorFlow repository) and many code pieces and snippets. Google is heavily pushing this development with Tensorflow Lite, Edge TPUs, Androids NN API and so forth. Subjectsĭue to increasing mobile computing capabilities, machine learning applications on mobile Devices became more commonly used over the last years. ![]() When you just want to convert models and do no training, you probably see no notable speed increase with GPU support at all. If you dont have CUDA support, just skip the CUDA and cuDNN intallation. The guide is written for Windows 10 with a CUDA capable GPU. It includes a step by step guide to set up a tensorflow environment for converting TensorFlow models to app-compatible Tensorflow Lite models and to host them in the cloud with Google Firebase. This entry contains a documentation of the EML Object Detection Android app.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |