This project proposes an end-to-end framework for semi-supervised Anomaly Detection and Segmentation in images based on Deep Learning.

Anomaly detection in industrial dataset(MVTEC) like capsules, texture, bottle tec... with simple layers and high performance

The LOCO-Annotations dataset is a specialized extension of the MVTec LOCO dataset, focusing on detecting and analyzing high-level semantic logical anomalies in industrial settings. This dataset provides detailed annotations designed to evaluate and improve logical anomaly detection methods.

The solutions for the dacon competition (1st place).

Anomaly detection autoencoder model with a focus on industrial images inspection.

Implementation of anomaly detection detection on MVTec 3D-AD dataset using point-voxel diffusion.

Toolbox for Unsupervised Anomaly Detection on MVTec AD

CVPR24 Anomaly detection solution for MvTec

A Curated List of Awesome Unsupervised Anomaly Detection on MVTec AD Dataset

Unofficial pytorch dataset class for MVTec Anomaly Detection Dataset

Context In the context of textile fabric, rare anomaly can occurs, hence compromising the quality of the tissues. In order to avoid that in some scenario, it is crucial to detect the defect. This dataset is for educational purposes Content Image size: 32x32 or 64x64 classes: ['good', 'color', 'cut', 'hole', 'thread', 'metal contamination'] rotations: 8 different rotations in [0, 20, 40, 60, 80, 100, 120, 140] Given an image size, a train and test dataset are available with randomly generated patches. Source images from the train and test are non-overlapping different tasks are possible: classification of the classes type classification of angles using only "good" images and testing of other classes texture representation learning / self-supervised learning Acknowledgements Based on the public dataset by the MVTec company Paul Bergmann, Michael Fuser, David Sattlegger, Carsten Steger. MVTec AD - A Comprehensive Real-World Dataset for Unsupervised Anomaly Detection; in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2019 Inspiration the main goal of this dataset is to explore self-supervised learning on texture images in order to solve anomaly detection problems and learn a robust representation of texture in lieu of traditional image processing features (e.g. glcm, gabor,….)

Using SPADE for anomaly detection on MVTEC LOCO AD dataset

Data loader for the MVTec dataset, a comprehensive real-world dataset for Unsupervised Anomaly Detection

Zero and few-shot industrial image anomaly detection framework comparing AnomalyDINO & MuSc models across MVTec AD, BTAD, and ViSA datasets with MLflow tracking and flexible configuration.

The MVTEC Anomaly Detection Dataset

A system capable of anomaly detection for two distinct products from the MVTec Anomaly Detection dataset.

Comprehensive benchmark for anomaly detection models on MVTec AD dataset using Anomalib. Includes PatchCore, EfficientAD, FastFlow, STFPM, PaDiM with Gradio demo.

Anomaly detection on the MVTec Dataset using ResNet50, KNN, Autoencoders, and synthetic data to detect industrial defects. 🚀

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Zero-Shot Anomaly Detection and Segmentation using DINOv3 without additional training or fine-tuning

Anomaly detection on MVTec AD using VQ-VAE-2

Goal: Apply pre-trained foundation models (like DINOv3, Mirroring DINO, or SAM) to detect surface defects and irregular textures in industrial images — specifically using the MVTec Anomaly Detection (AD) dataset).

VAE-based visual anomaly detection system built on the MVTec AD (Hazelnut) dataset. Trained using only normal images, the model detects defects via reconstruction error. Includes image-level anomaly scoring, pixel-level defect localization with heatmaps, and statistically derived thresholds for unsupervised industrial inspection tasks.

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Anomaly Detection on MVTecAD with PatchCore

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Anomaly detection on MVTec AD dataset

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