Task: Visual Anomaly Detection

License: Apache 2.0

Description

This dataset has been collected by Edge Impulse teams and contains a single thermostatic valves, centered in the frame, with a similar size and a uniform 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: 195

• Labeling Method: single label

• Train/Test Split: 62.05% / 37.95%

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 (Enterprise feature)

Citation

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

@misc{edgeimpulse_dataset_497420,
    title = {Visual Anomaly Detection - Thermostatic valves},
    author = {Edge Impulse},
    year = {2024},
    url = {https://studio.edgeimpulse.com/public/497420/latest},
    note = {Apache 2.0}
}

Task: Visual Anomaly Detection

License:

Description

This dataset has been collected by Edge Impulse teams and contains a single DHT11 sensor, centered in the frame, with a similar size and a uniform background.

The training dataset only contains "nominal" (no anomaly) images whereas the testing dataset contains both nominal and anomalous images.

The DHT11 have been used to teach IoT classes in the past and have been manipulated by students extensively. When not wiring the pins properly, it can cause an overheat which often lead to the plastic melting. Some other anomalous images are missing wiring pins.

Compatible Blocks

Dataset Details

• Total Data Items: 195

• Labeling Method: single label

• Train/Test Split: 69.74% / 30.26%

Usage

• Download

  • HuggingFace - Soon

  • Kaggle - Soon

• Import this dataset to your Edge Impulse project

Citation

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

Task: Visual Anomaly Detection

License:

Description

This dataset has been collected by Edge Impulse teams and contains a single red-white capsule, centered in the frame, with a similar size and a uniform background.

The training dataset only contains "nominal" (no anomaly) images whereas the testing dataset contains both nominal and anomalous images.

Compatible Blocks

Dataset Details

• Total Data Items: 149

• Labeling Method: single label

• Train/Test Split: 64.43% / 35.57%

Usage

• Download

  • HuggingFace - Soon

  • Kaggle - Soon

• Import this dataset to your Edge Impulse project

Citation

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

Task: Visual Anomaly Detection

License: Apache 2.0

Description

This dataset has been collected by Edge Impulse teams and contains a single fire extinguisher head thread, centered in the frame, with a similar size and a variable background.

The training dataset only contains "good" (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: 204

• Labeling Method: single label

• Train/Test Split: 58.82% / 41.18%

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 (Enterprise feature)

Citation

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

@misc{edgeimpulse_dataset_497408,
    title = {Visual Anomaly Detection - Fire extinguisher head thread},
    author = {Edge Impulse},
    year = {2024},
    url = {https://studio.edgeimpulse.com/public/497408/latest},
    note = {Apache 2.0}
}

Task: Visual Anomaly Detection

License: Apache 2.0

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%

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 (Enterprise feature)

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}
}