Computer vision algorithms are at the core of many deep learning applications. Self-driving cars, security systems, healthcare, logistics, and image processing all incorporate various aspects of computer vision. But despite their ubiquity, training computer vision algorithms, like Mask or Cascade RCNN, is hard. These models employ complex architectures, train on large datasets, and require computer clusters, often requiring dozens of GPUs.
Last year at AWS re:Invent we announced record-breaking Mask RCNN training times of 6:45 minutes on PyTorch and 6:12 minutes on TensorFlow, which we achieved through a series of algorithmic, system, and infrastructure improvements. Our model made heavy use of half precision computation, state-of-the-art optimizers and loss functions, the AWS Elastic Fabric Adapter, and a new parameter server distribution approach.
Now, we’re making these optimizations available in Amazon SageMaker in our new SageMakerCV package. SageMakerCV takes all the high performance tools we developed last year and combines them with the convenience

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