> ## Documentation Index
> Fetch the complete documentation index at: https://docs.edgeimpulse.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Flat washers

**Task:** Visual Anomaly Detection

**License:** [BSD 3-Clause Clear](https://spdx.org/licenses/BSD-3-Clause-Clear.html)

<Frame caption="Dataset Screenshot">
  <img src="https://mintcdn.com/edgeimpulse/pssAI2UB-dmQQKtc/.assets/images/dataset-screenshot/497653.png?fit=max&auto=format&n=pssAI2UB-dmQQKtc&q=85&s=b7420dbf7c11756a5558c7af6806b769" width="1600" height="719" data-path=".assets/images/dataset-screenshot/497653.png" />
</Frame>

## Description

This dataset has been collected by Edge Impulse teams and contains a single flat washer, randomly located in the frame. The dataset has been collected using two different backgrounds - a white background and textured dark-grey background. The training dataset only contains "nominal" (no anomaly) images whereas the testing dataset contains both nominal and anomalous images.

### Compatible Blocks

* **Feature extraction**: [Image](/studio/projects/processing-blocks/blocks/image)
* **Learning block**: [Visual Anomaly Detection (FOMO-AD)](/studio/projects/learning-blocks/blocks/visual-anomaly-detection-fomo-ad)

*Not sure what to choose? Try out this dataset with the [EON Tuner](/studio/projects/eon-tuner).*

### Dataset Details

* **Total Data Items:** 464

* **Labeling Method:** single label

* **Train/Test Split:** 58.84% / 41.16%

|                      | **Training Set** | **Testing Set**     |
| -------------------- | ---------------- | ------------------- |
| **Total Data Items** | 273              | 191                 |
| **Labels**           | no anomaly       | anomaly, no anomaly |

## Usage

The dataset can be added to a project in two ways: cloning the dataset public project or downloading the dataset and importing it into another project.

* **Clone the [public project](https://studio.edgeimpulse.com/public/497653/latest)**

  To clone and use this project, visit the [Edge Impulse Studio link](https://studio.edgeimpulse.com/public/497653/latest), click on the **Clone** button on the top-right corner and follow the cloning instructions.

* **Download and import**

  * [Download link](https://cdn.edgeimpulse.com/datasets/Visual+Anomaly+Detection+-+Flat+washers.zip)

  This dataset uses the Edge Impulse Exporter Format (`info.labels`). See [Edge Impulse labels](/tools/specifications/data-annotation/ei-labels) for more info.

  Edge Impulse also supports different [data acquisition formats](/tools/specifications/data-acquisition/json-cbor) and [dataset annotation formats](/tools/specifications/data-annotation/ei-labels) that you can import into your project to build your edge AI models:

  * [Studio uploader](/studio/projects/data-acquisition/uploader)
  * [CLI uploader](/tools/clis/edge-impulse-cli/uploader)
  * [CSV Wizard](/studio/projects/data-acquisition/csv-wizard)
  * [Python SDK](/tutorials/tools/sdks/studio/python/upload-download-data)
  * [Ingestion API](/apis/ingestion)
  * [Import from S3 buckets](/studio/projects/data-acquisition/data-sources)
  * [Upload portals](/studio/organizations/upload-portals)

## Citation

If you use this dataset in your research paper, please cite it using the following BibTeX:

```
@misc{edgeimpulse_dataset_497653,
    title = {Visual Anomaly Detection - Flat washers},
    author = {Edge Impulse},
    year = {2024},
    url = {https://studio.edgeimpulse.com/public/497653/latest},
    note = {BSD 3-Clause Clear}
}
```