Albumentations on GitHub in Industrial and manufacturing

#1

open-edge-platform/anomalib

An anomaly detection library comprising state-of-the-art algorithms and features such as experiment management, hyper-parameter optimization, and edge inference.

#2

khornlund/severstal-steel-defect-detection

Kaggle Segmentation Challenge

#3

blaz-r/SuperSimpleNet

Official implementation of SuperSimpleNet [ICPR 2024, JIMS 2025]

#4

siddhartamukherjee/NEU-DET-Steel-Surface-Defect-Detection

This project is about detecting defects on steel surface using Unet. The dataset used for this project is the NEU-DET database.

#5

Marfbin/NEU-DET-with-yolov8

The dataset I am using is NEU-DET, which uses yolov8 and its improved models (including Coordinate Attention and Swin Transformer) for defect detection

#6

open-edge-platform/edge-ai-suites

A curated collection of sample applications intended for reference in developing optimized AI solutions and testing hardware performance across various industry use cases.

#7

vnk8071/anomaly-detection-in-industry-manufacturing

Pipeline training and inference Anomalib models UI in Anomaly Detection

#8

fengyan-cv/WPFormer

[CVPR 2025] | Wavelet and Prototype Augmented Query-based Transformer for Pixel-level Surface Defect Detection

#9

ORippler/gaussian-ad-mvtec

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

#10

taikiinoue45/STAD

Uninformed Students: Student-Teacher Anomaly Detection with Discriminative Latent Embeddings

#11

xcyao00/ADPretrain

[NeurIPS 2025] Pytorch Implementation for NeurIPS 2025 paper: ADPretrain: Advancing Industrial Anomaly Detection via Anomaly Representation Pretraining

#12

Anionex/yolov10-pcb-detect

使用yolov10目标检测模型进行电路板缺陷检测 | Using yolov10 for circuit board (PCB) defect detection

#13

taikiinoue45/RIAD

Reconstruction by Inpainting Based Anomaly Detection

#14

Diyago/Severstal-Steel-Defect-Detection

Can you detect and classify defects in steel? Segmentation in Pytorch

#15

awslabs/sagemaker-defect-detection

Detect Defects in Products from their Images using Amazon SageMaker

#16

haichao67/GD-YOLOv8

Lightweight Rail Surface Defect Detection Algorithm Based on an Improved YOLOv8

#17

SAP-archive/btp-ai-sustainability-bootcamp

This github repository contains the sample code and exercises of btp-ai-sustainability-bootcamp, which showcases how to build Intelligence and Sustainability into Your Solutions on SAP Business Technology Platform with SAP AI Core and SAP Analytics Cloud for Planning.

#18

aws-samples/amazon-sagemaker-edge-defect-detection-computer-vision

Workshop showcasing how to run defect detection using computer vision at the edge with Amazon SageMaker

#19

zdaiot/Kaggle-Steel-Defect-Detection

My solution to the Severstal: Steel Defect Detection on Kaggle, which got the 96th place. (Top4%)

#20

LZY-233/yolov8_Imporved-Defect_detection

Steel defect detection system based on improved YOLOv8 algorithm(基于改进YOLOv8算法的钢材瑕疵辅助检测系统)

#21

li-phone/DefectNet

DefectNet: Towards Fast and Effective Defect Detection

#22

djene-mengistu/dseg_models

This repo contains implementation of deep learning-based steel surface defect segmentation models. Extensive experiments on several deep learning frameworks have been presented with various performance analysis and comparison.

#23

enrico310786/Image_Anomaly_Detection

Train and test image anomaly detection models with Anomalib. Examples on a custom dataset

#24

xiuqhou/CANet

[Pattern Recognition] Official implementation of the paper "CANet: Contextual Information and Spatial Attention Based Network for Detecting Small Defects in Manufacturing Industry"

#25

xiaohuanlaile/steel-defect-detection

描述: 这是一个深度学习项目，利用 HRNet 和 FPN 架构进行钢材表面缺陷检测。本项目针对噪声较大的比赛数据进行了优化，包括椒盐噪声模拟和拼接数据处理，采用自定义增强技术、高级损失函数及多尺度特征融合。仓库包含训练脚本、模型定义和评估指标。

#27

ma0ho/pvinspect

Collection of methods for the analysis of solar modules

#28

covisionlab/diffusion_labeling

Official implementation of "Bounding Box-Guided Diffusion for Synthesizing Industrial Images and Segmentation Map" accepted at Synthetic Data for Computer Vision Workshop - CVPR 2025

#29

taikiinoue45/mvtec-utils

Toolbox for Unsupervised Anomaly Detection on MVTec AD

#30

djene-mengistu/simEps

This repo contains implementation of semi-supervised defect segmentation based on pairwise similarity map consistency and ensemble-based cross pseudo labels

#31

hmyao22/PSA

Implementation for paper:"Scalable Industrial Visual Anomaly Detection with Partial Semantics Aggregation Vision Transformer"

#32

janmayer15/Optimizing-Production-Profitability-through-Deep-Reinforcement-Learning-Driven-Quality-Prediction

Reinforcement Learning, specifically Deep Q-Networks, was applied to semiconductor manufacturing data to efficiently identify substandard products, with a model optimized using the F1-Metric achieving 87% accuracy and a 22.4% time-saving, highlighting RL's potential in enhancing manufacturing processes.

#33

VCUAAM/am-powder-cleanup-workflow

The project on the path to develop automation of post-processing in additive manufacturing

#34

zxm97/anomalib-tensorrt-python

Anomalib inference with TensorRT (python).

#35

Analytics-Everywhere-Lab/vqixai

The implementation for the paper "XEdgeAI: A Human-centered Industrial Inspection Framework with Data-centric Explainable Edge AI Approach"

#36

IshanTiwari-030800/Steel-Defect-detection

In this repository, I have made a simple UNet architecture for detection and segmentation of defects on steel surface images. The dataset has been downloaded from kaggle.

#37

TheoViel/kaggle_severstal

Solution for the Severstal Steel Defect Detection Challenge

#38

GrayLand119/unsupervised_automatic_defect_detection

基于 OpenVINO 的无监督缺钱检测系统 Demo

#40

collinswakholi/Potato-defect-detection

The project is about detecting defects on potatoes using RGB images and a deep learning classification model.

#43

cepdnaclk/e18-4yp-Machine-Vision-For-Quality-Inspection

This project pioneers a machine vision solution for automated manufacturing quality control, mitigating human errors and fatigue. Leveraging cameras and algorithms, the system detects defects, boosting productivity and cutting operational costs. Ideal for industries prioritizing efficient and precise defect identification in production.

#45

LSU-LAMDA/crack-pore-detection

A CNN-based workflow for detecting cracks and pores in tomography data of LPBF additive manufacturing metallic sample

#46

x-mint/see-me

Defect detection prototype and baseline for X4Vision

#47

kbrodt/severstal

Severstal: Steel Defect Detection

#48

Bugates/PCB_defect_detection

This PCB defect detection system uses PyTorch and a Kaggle dataset to identify defects in printed circuit boards. It employs advanced computer vision techniques with preprocessing, data augmentation, and evaluation pipelines, ensuring efficient and accurate defect classification.

#49

DevWithJas/PCB-Defect-detection-dashboard

his application leverages a Faster R-CNN object detection model to identify defects on Printed Circuit Boards (PCBs). By uploading your custom trained model and a dataset of PCB images, you can visualize, test, and even run real-time detection.

#50

Maherstad/Metal-Surface-Defect-Detection

metal surface detection using yolov5

#51

MohitGoyal09/severstal-steel-defect-detection

Computer vision notebooks for Severstal steel defect detection and segmentation experiments.

#52

nurulashraf/dbscan-silicon-defect-detection

This project uses machine learning to detect anomalies in silicon wafers. It employs DBSCAN clustering and a Gradio interface for user interaction, enabling automated defect detection and enhancing quality control in semiconductor manufacturing

#54

PinWheel-hub/My_Anomalib

Forked from openvinotoolkit/anomalib

#55

Rukhsar111/Automated-Defect-Detection

This project aims to detect the Anomalies present on the objects.

#56

sneha11naidu/PCB_Defect_Detection_using_Computer_Vision_and_Genetic_Algorithms

"PCB Defect Detection using Computer Vision and Genetic Algorithms" uses image analysis to identify flaws in printed circuit boards, like missing components or misalignments. Genetic algorithms optimize detection, improving accuracy and efficiency in the inspection process.

#57

thetoby9944/tfds_defect_detection

A tensorflow dataset builder for semantic defect segmentation datasets like MVTEC and VisA. Includes options to generate synthetic anomalies

#58

tsyncIO/Industrial-Defect-Segmentation

An AI-powered defect detection system that automatically identifies and segments manufacturing flaws using deep learning for quality control automation

#61

Ahmed-TAIBECHE/water-bottle-visual-inspection-based-on-transfer-learning

my engineering final project: development of visual inpection system for water bottle based on deep learning (transfer learning)

#63

aaajfe/Weakly-Supervised-Metallographic-Defect-Segmentation

基于SegFormer的工业纯铁金相缺陷分割系统，使用定位令牌和记忆引导的伪标签来提高分割精度。

#67

DavidHribek/anomalib

An anomaly detection library comprising state-of-the-art algorithms and features such as experiment management, hyper-parameter optimization, and edge inference.

#71

GowthamRam2000/defect_detection

try the app at the link below

#76

MayurPD28/OP_FractureScope

"Operation FractureScope" – Industrial Anomaly Detection in Harsh Environments

#78

nonreaction123/industrial-vision-inspection

End-to-end industrial defect inspection (NEU classification + MVTec anomaly/segmentation) with reproducible training/evaluation and a Streamlit demo.

#79

notvicent3/anomalib

An anomaly detection library comprising state-of-the-art algorithms and features such as experiment management, hyper-parameter optimization, and edge inference.

#80

sarvex/anomalib

An anomaly detection library comprising state-of-the-art algorithms and features such as experiment management, hyper-parameter optimization, and edge inference.

#81

sathyendranv/edge-ai-suites

Curated collections of sample applications designed to help you develop optimized AI solutions. Tailored to specific use cases, covering retail, manufacturing, metro, and media & entertainment.