Tutorial: Building Image Segmentation Faster Using Jupyter Notebooks from NGC

The AI containers and models on the NGC Catalog are tuned, tested, and optimized to extract maximum performance from your existing GPU infrastructure.

Image segmentation is the process of partitioning a digital image into multiple segments by changing the representation of an image into something that is more meaningful and easier to analyze. Image segmentation can be used in a variety of domains such as manufacturing to identify defective parts, in medical imaging to detect early onset of diseases, in autonomous driving to detect pedestrians, and more.

However, building, training, and optimizing these models can be complex and quite time consuming. To achieve a state-of-the-art model, you need to set up the right environment, train with the correct hyperparameters, and optimize it to achieve the desired accuracy. Data scientists and developers usually end up spending a considerable amount of looking for the right tools and setting up the environments for their models, which is why we built the NGC Catalog.

A hub for cloud-native, GPU-optimized AI and HPC applications and tools that provides faster access to performance-optimized containers, shortens time-to-solution with pretrained models and provides industry specific software development kits to build end-to-end AI solutions. The catalog hosts a diverse set of assets that can be used for a variety of applications and use cases ranging from computer vision and speech recognition to recommendation systems.

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