This repository implements the INP-Former model for anomaly detection in industrial inspection tasks. The system extracts Intrinsic Normal Prototypes (INPs) directly from test images using a DINOv2 vision transformer encoder, followed by an INP extractor and decoder to reconstruct normal regions. Reconstruction errors serve as anomaly scores. The repository provides training scripts for various settings (single-class, multi-class, few-shot, zero-shot, super-multi-class), a Gradio-based zero-shot application for interactive inference, ONNX conversion and inference tools, and a competition submission kit. It achieves state-of-the-art performance on standard benchmarks like MVTec-AD, VisA, and Real-IAD. The architecture is modular, separating the core model, dataset handling, utilities, and training logic, though training scripts show some duplication across different settings.
This repository implements Dinomaly, a reconstruction-based unsupervised anomaly detection method for industrial inspection datasets. It uses vision transformers (primarily DINOv2) as feature extractors with a noisy bottleneck and linear attention mechanism. The code supports both class-separated and unified training across MVTec-AD, VisA, and Real-IAD datasets. Key components include dataset loaders, custom optimizers, training utilities, and evaluation metrics. While the research code achieves state-of-the-art results, it contains hardcoded paths, version-specific dependencies, and dataset-specific logic that limit immediate reproducibility.