brunojtfernandes/PILAR
AI model by brunojtfernandes
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Code and reference results for Pyramidal Inhibitory Lightweight Autoassociative Reconstruction for Cost-Constrained Visual Anomaly Detection. Published in Discover Artificial Intelligence (2026). PILAR learns to reconstruct normal images or frozen pretrained features using a compact pyramidal encoder, lateral inhibition, a spatial bottleneck, and a reconstruction decoder. Anomaly scores are reconstruction residuals. Feature-PILAR-FCN uses ResNet18 in both training and inference; only the PILAR heads are trained. This repository reproduces the paper's MVTec AD and VisA experiments, full-category MVTec ablations, and complementary image and RGB reconstruction experiments. The Python package is named lipnet; its models implement PILAR. This repository distributes the source code accompanying the published PILAR article. No trained checkpoints or dataset images are included. To run inference, first train a category-specific model with the commands below, or use your own compatible checkpoint. This release is not a hosted inference service. Download the archive using the link above, then extract it: Alternatively,…
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Model overview
brunojtfernandes/PILAR is a task-unspecified model repository published by brunojtfernandes on Hugging Face. A library was not reported.
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- 2026-11-14T06:58:17.000Z
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- 2026-11-14T06:58:17.000Z
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