Repository for the Explainable Deep One-Class Classification paper

Official PyTorch code for WACV 2022 paper "CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flows"

unoffical and work in progress PyTorch implementation of CutPaste

Vanilla torch and timm industrial knn-based anomaly detection for images.

Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders

This is an unofficial implementation of Reconstruction by inpainting for visual anomaly detection (RIAD).

Code underlying our publication "Modeling the Distribution of Normal Data in Pre-Trained Deep Features for Anomaly Detection" at ICPR2020

Official Implementation for the "Back to the Feature: Classical 3D Features are (Almost) All You Need for 3D Anomaly Detection" paper.

Anomaly detection method that incorporates multi-scale features to sparse coding

Semi-Orthogonal Embedding for Efficient Unsupervised Anomaly Segmentation

🪥 Unofficial re-implementation of Semi-orthogonal Embedding for Efficient Unsupervised Anomaly Segmentation

Code to reproduce 'Combining GANs and AutoEncoders for efficient anomaly detection'

Repository for the Exposing Outlier Exposure paper

This is an unofficial implementation of ' Anomaly localization by modeling perceptual features'

EfficientNetV2 based PaDiM

PatchCore method for Industrial Anomaly Detection + CLIP

🐬 Re-implementation of PaDiM and code for the article "Weakly Supervised Detection of Marine Animals in High Resolution Aerial Images"

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

This repository is an unofficial implementation of the network described in Wang, G., Han, S., Ding, E., & Huang, D. (2021). Student-Teacher Feature Pyramid Matching for Anomaly Detection. The British Machine Vision Conference (BMVC).

This project detects anomalies in 2D data using PyTorch for model training and Flutter for a cross-platform application. Key features include 📊 pre-trained models, a 📱 Flutter mobile app that shows heat maps, a 🌐 Flask server backend, and a 🖥️ Tkinter desktop app.