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Anomalib notebooks

This is a great place in our repo where you can try some capabilities and functions you can achive with Anomalib. First follow the installation guide and then explore the notebooks that it offer to you.

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⚙️ Installation Guide

To install Python, Git and other required tools, OpenVINO Notebooks repository provides a good documentation. For more details please refer to the Installation Guide.

Windows Ubuntu macOS Red Hat CentOS Azure ML Docker Amazon SageMaker

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Useful Jupyter Notebooks to get started with Anomalib

0. Training and Inference

Notebook GitHub Colab
Getting Started 001_getting_started Open In Colab

1. Data Modules

Notebook GitHub Colab
BTech 101_btech Open In Colab
MVTec 102_mvtec Open In Colab
Folder 103_folder Open In Colab

2. Models

Notebook GitHub Colab
Model 201_fastflow Open In Colab

3. OpenVINO Optimization

Notebook GitHub Colab
Quantization 401_NNCF

4. Use cases

Notebook GitHub Colab
Dobot Dataset Creation 501a_training
Training 501b_training

7. Metrics

Notebook GitHub Colab
AUPIMO basics 701a_aupimo Open In Colab
AUPIMO representative samples and visualization 701b_aupimo_advanced_i Open In Colab
PIMO curve and integration bounds 701c_aupimo_advanced_ii Open In Colab
(AU)PIMO of a random model 701d_aupimo_advanced_iii Open In Colab