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Transfer Learning (Image Classification) in Colab for Edge TPU

Most visual deep learning applications used an existing model and performed transfer learning to classify the images or detect the objects within the image. To use the Coral USB accelerator for these operations, the model has to be converted to Tensorflow lite model and then to model understood by Edge TPU.

There is a good article on model optimisation but for this blog post, I am using the Quantization Aware approach for the transfer learning. In Google example, it is required to install a docker and perform the transfer learning within docker. In this blog post, I have moved the retraining process to Colab and only the final step of converting to Edge TPU format is done on the local machine.

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