Inter-Realization Channels (InReaCh): Unsupervised Anomaly Detection in Images Beyond One-Class Classification

January 12, 2026 · View on GitHub

This repository contains the implementation of InReaCh, a fully unsupervised method for anomaly detection and localization in images. The method was introduced in ICCV 2023 and achieves state-of-the-art performance in unsupervised anomaly detection tasks.

Also check out the newer Online-InReaCh for better performance, tolerance to non-stationary distributions and online adaptation! Online InReaCh code can be found here!

Overview

InReaCh leverages the concept of Inter-Realization Channels to extract high-confidence nominal patches from training data by associating them across image realizations. These nominal patches are used to build a model for detecting and localizing anomalies in test images. Unlike supervised one-class classification methods, InReaCh is fully unsupervised, simplifying dataset creation and broadening its applicability.

Key features of InReaCh:

Unsupervised anomaly detection: No need for labeled training data.
High precision: Achieves 99.9% precision in extracting nominal patches from the MVTec AD dataset.
Robust performance: Maintains high accuracy even with up to 40% corrupted training data.
Competitive results: Achieves 0.968 AUROC in localization and 0.923 AUROC in detection.

Tables and Figures

defect_segmentation

Repository Structure

InReaCh.py: Main implementation of the InReaCh algorithm, including training and testing pipelines.
FeatureDescriptors.py: Code for feature extraction and descriptor generation.
mvtec_loader.py: Utilities for loading and preprocessing the MVTec AD dataset.
utils.py: Helper functions for alignment, seeding, and positional tests.
model.py: Code for loading and configuring the Wide ResNet-50 model for feature extraction.
requirements.txt: Python dependencies for the project.
conda_inreach.yml: Conda environment configuration file.
images/: Contains visualizations of the method, results, and qualitative examples.
data/: Placeholder for dataset-related files.

Installation

Using Conda

Clone the repository:

git clone https://github.com/your-repo/InReaCh.git
cd InReaCh

Create the Conda environment:
```
conda env create -f conda_inreach.yml
```
Activate the environment:
```
conda activate inreach
```

Using Pip

Install the required Python packages:
```
pip install -r requirements.txt
```

Train and test the InReaCh model:
```
python InReaCh.py
```
Modify parameters such as class_names, assoc_depth, and max_channel_std in the InReaCh.py file to customize the training and testing process.

Visualizing Results

The repository includes visualizations of the method and results in the images/ directory:

Method.png: Overview of the InReaCh methodology.
Qualitative.png: Qualitative results of anomaly detection.
Results.png: Quantitative results and performance metrics.

Dependencies

The project requires the following key libraries:

Python 3.12
PyTorch 2.6.0
torchvision 0.21.0
OpenCV 4.11.0
FAISS (GPU version)
NumPy, SciPy, scikit-learn, tqdm

For a complete list of dependencies, refer to the conda_inreach.yml or requirements.txt file.

Citation

If you use this code in your research, please cite the following paper:

@misc{mcintoshInReaCh,
      title={Inter-Realization Channels: Unsupervised Anomaly Detection in Images Beyond One-Class Classification},
      author={Declan McIntosh and Alexandra Branzan Albu},
      year={2023},
      booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
      month     = {October}
}

Acknowledgments

This implementation builds upon the concepts introduced in the ICCV 2023 paper. Special thanks to the authors and contributors for their work