A primary goal in the field of neuroscience is understanding how the brain controls movement. By improving pose estimation, neurobiologists can more precisely quantify natural movement and in turn, better understand the neural activity that drives it. This enhances scientists’ ability to characterize animal intelligence, social interaction, and health.
]]>Deep learning models require vast amounts of data to produce accurate predictions, and this need becomes more acute every day as models grow in size and complexity. Even large datasets, such as the well-known ImageNet with more than a million images, are not sufficient to achieve state-of-the-art results in modern computer vision tasks. For this purpose, data augmentation techniques are…
]]>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…
]]>During the 2020 NVIDIA GPU Technology Conference keynote address, NVIDIA founder and CEO Jensen Huang introduced the new NVIDIA A100 GPU based on the NVIDIA Ampere GPU architecture. In this post, we detail the exciting new features of the A100 that make NVIDIA GPUs an ever-better powerhouse for computer vision workloads. We also showcase two recent CV research projects from NVIDIA Research…
]]>Today, smartphones, the most popular device for taking pictures, can capture images as large as 4K UHD (3840×2160 image), more than 25 MB of raw data. Even considering the embarrassingly low HD resolution (1280×720), a raw image requires more than 2.5 MB of storage. Storing as few as 100 UHD images would require almost 3 GB of free space. Clearly, if you store data this way…
]]>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…
]]>