The Edge TPU API (the
edgetpu module) provides simple APIs that perform image classification and
object detection. It's build on top of the TensorFlow Lite C++ API and abstracts-away a lot of the
code required to handle input tensors and output tensors. The Edge TPU API also includes APIs to
perform on-device transfer-learning with either weight imprinting or backpropagation.
Install the library
If you're using the Dev Board or SoM, the Edge TPU library is included in the Mendel system image (just be sure you've updated to the latest software).
Otherwise, you can install the library from the software downloads page.
Edge TPU API overview
Key APIs in the
edgetpu module that perform inferencing are the following:
ClassificationEngine: Performs image classification. Create an instance by specifying a model, and then pass an image (such as a JPEG) to
ClassifyWithImage()and it returns a list of labels and scores.
DetectionEngine: Performs object detection. Create an instance by specifying a model, and then pass an image (such as a JPEG) to
DetectWithImage()and it returns a list of
DetectionCandidateobjects, each of which contains a label, a score, and the coordinates of the object.
Both engines are subclasses of
you can use to perform different types of inferencing. They both also support all image formats
supported by Pillow (including JPEG, PNG, BMP), except the files
must be in RGB color space (no transparency).
Additionally, we've included two APIs that perform on-device transfer learning (for classification models only):
ImprintingEngine: This implements a transfer-learning technique called weight imprinting that does not require backward propagation. It allows you to teach the model new classifications with very small sample sizes (literally just a few images). For more information, read Retrain an image classification model on-device.
SoftmaxRegression: This offers an abbreviated version of traditional backpropagation—it updates only the fully-connected layer at the end of the graph with new weights. It requires large datasets for training, but is still very fast and may result in more accurate models when the dataset has high intra-class variance. For more information, read Retrain an image classification model on-device.
The Python library takes care of all the low-level Edge TPU configuration for you. Even if you connect multiple Edge TPUs, the Python library automatically delegates separate models to execute on separate Edge TPUs. For more information about using using multiple models, read Run multiple models with multiple Edge TPUs.
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