Researchers continue to develop new model architectures for common machine learning (ML) tasks. One such task is image classification, where images are accepted as input and the model attempts to classify the image as a whole with object label outputs. With many models available today that perform this image classification task, an ML practitioner may ask questions like: “What model should I fine-tune and then deploy to achieve the best performance on my dataset?” And an ML researcher may ask questions like: “How can I generate my own fair comparison of multiple model architectures against a specified dataset while controlling training hyperparameters and computer specifications, such as GPUs, CPUs, and RAM?” The former question addresses model selection across model architectures, while the latter question concerns benchmarking trained models against a test dataset.
In this post, you will see how the TensorFlow image classification algorithm of Amazon SageMaker JumpStart can simplify

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