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1. TENSORFLOW BOARD VIEW VALUES ANDROID
2. TENSORFLOW BOARD VIEW VALUES SOFTWARE
3. TENSORFLOW BOARD VIEW VALUES CODE

For some other networks, you may need to look at their original source to find this information. For some of the networks, you can find this information alongside the model in a readme file. In selecting a network, you will also want to check on the information of the preconditions and postconditions for data that the network processes.

TENSORFLOW BOARD VIEW VALUES CODE

Before cloning this repository, activate GIT LFS with the following command:Ĭopy Code git lfs pull -include= " *" -exclude= " "

With GIT LFS, you can clone a repository and then download only the files of interest, without all the large files downloading. If you would like to use models from this repository, you will need to use GIT LFS (Large File System). Within the repository you will find it in /vision/object_detection_segmentation/yolo4/models. I will download one of the YOLO (You Only Look Once) models, a visual classification model. Within this repository you will find models for visual recognition, voice recognition, gesture analysis, and more.

Note that networks that use experimental or less common operations may have limited portability to other frameworks.įor this article, I will use a model that was taken from the ONNX Model Zoo. Networks that use operations that are common to machine learning frameworks can be shared among the ML technologies using ONNX. This means that TensorFlow Lite is not limited to using only models that were implemented with TensorFlow.

TENSORFLOW BOARD VIEW VALUES SOFTWARE

Thanks to ONNX, developers can use their preferred software and frameworks for producing their neural network models and share them with people that may be using other AI technologies. ONNX is supported by a range of companies including Microsoft, Facebook, AMD, IBM, ARM, NVIDIA, Qualcomm, and many others. This format is known as the Open Neural Network Exchange format (ONNX).

To support interoperability among the formats in which neural network models could be saved, there is an intermediate format. Pre-trained networks may have been built using a number of different technologies. A pretrained network gets you past the process of gathering and preparing a large amount of data needed for training a visual recognizer and more quickly to the point of being productive. When using TensorFlow Lite on a mobile device, you will probably want to use a network that has already been trained. We continue here with adapting a network for TensorFlow Lite.

TENSORFLOW BOARD VIEW VALUES ANDROID

In the previous section the development environment was set up for working with TensorFlow models and Android development. This is the second article in a series of articles about using TensorFlow Lite on Android.