Flat washers

Task: Visual Anomaly Detection

License: BSD 3-Clause Clear

Dataset Screenshot

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

• Learning block: Visual Anomaly Detection (FOMO-AD)

Not sure what to choose? Try out this dataset with the 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

• Clone the public project

  To clone and use this project, visit the Edge Impulse Studio link, click on the Clone button on the top-right corner and follow the cloning instructions.

• Download

  • Direct link

  • HuggingFace - Soon

  • Kaggle - Soon

• Import this dataset to your Edge Impulse project

  This project uses the Edge Impulse Exporter Format (info.labels). See this documentation page for more info.

  Edge Impulse also supports different data sample formats and dataset annotation formats that you can import into your project to build your edge AI models:

  • Studio uploader

  • CLI uploader

  • CSV Wizard

  • Python SDK

  • Ingestion API

  • Import from S3 buckets

  • 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 = {Apache 2.0}
}