Deep learning models require hundreds of gigabytes of data to generalize well on unseen samples. Data augmentation helps by increasing the variability of examples in datasets. The traditional approach to data augmentation dates to statistical learning when the choice of augmentation relied on the domain knowledge, skill, and intuition of the engineers that set up the model training.
]]>This post is an update to an older post. Deep learning models require training with vast amounts of data to achieve accurate results. Raw data usually cannot be directly fed into a neural network due to various reasons such as different storage formats, compression, data format and size, and limited amount of high-quality data. Addressing these issues requires extensive data preparation…
]]>Join the NVIDIA Triton and NVIDIA TensorRT community to stay current on the latest product updates, bug fixes, content, best practices, and more. When you are working on optimizing inference scenarios for the best performance, you may underestimate the effect of data preprocessing. These are the operations required before forwarding an input sample through the model. This post highlights the…
]]>Editor’s Note: This post has been updated. Here is the revised post. Training deep learning models with vast amounts of data is necessary to achieve accurate results. Data in the wild, or even prepared data sets, is usually not in the form that can be directly fed into neural network. This is where NVIDIA DALI data preprocessing comes into play. There are various reasons for that…
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