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How to retrain an image classification model?


Got a tutorial from Google Coral Team:
This tutorial shows you how to retrain an image classification model to recognize a new set of classes. You'll use a technique called transfer learning to retrain an existing model and then compile it to run on an Edge TPU device—you can use the retrained model with either the Coral Dev Board or the Coral USB Accelerator.
Specifically, this tutorial shows you how to retrain a quantized MobileNet V1 model to recognize different types of flowers (adopted from TensorFlow's docs). But you can reuse these procedures with your own image dataset, and with a different pre-trained model.
Tip: If you want a shortcut to train an image classification model, try Cloud AutoML Vision. It's a web-based tool that allows you to train a model with your own images, optimize it, and then export it for the Edge TPU.
  1. Set up the Docker container
  2. Prepare your dataset
  3. Retrain your classification model
  4. Compile the model for the Edge TPU
  5. Run the model
Before you retrain an image classification model, you have to get a Cora devices: Dev Board or the Coral USB Accelerator, which you can get there: https://store.gravitylink.com/global

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