: Best for those with GPU access, this article uses the NVIDIA NGC catalog to provide optimized containers and pretrained models. It is particularly useful for industrial tasks like identifying defective parts on assembly lines.
: A highly interactive notebook using the Oxford-IIIT Pet Dataset. It demonstrates how to use the U-Net architecture to classify every pixel into categories like "pet," "border," or "surrounding". image-segmentation-jupyter-notebook
: CellPose with SimpleITK is an excellent resource for researchers needing to segment cells or nuclei in microscopy images. : Best for those with GPU access, this
: For 3D MRI or CT scan segmentation, NVIDIA's guide on Medical 3D Image Segmentation focuses on brain tumor prediction. It demonstrates how to use the U-Net architecture
: If you need to label your own data, Towards Data Science describes building a custom labeling tool directly within a single Jupyter notebook using transfer learning.