marksgraham/ddpm-ood
Official PyTorch code for "Out-of-distribution detection with denoising diffusion models"
This tool helps researchers and practitioners in fields like medical imaging and computer vision to automatically detect unusual or 'out-of-distribution' data within their image datasets. You input a collection of images that represent your 'normal' data, and the system learns its characteristics. It then processes new images and flags those that don't fit the established norm, helping you identify anomalies without manual inspection. This is useful for anyone working with image datasets where identifying unexpected inputs is critical.
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Use this if you need to reliably identify images that deviate significantly from a known set of 'normal' or expected images, especially in computer vision or medical imaging applications.
Not ideal if your primary goal is image generation, image editing, or if you need to classify images into many specific categories rather than simply spotting anomalies.
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Python
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Last pushed
Jun 09, 2024
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