Task: Object Detection

License: Apache 2.0

Description

This dataset has been collected by Edge Impulse teams and is a variant of the "Object Detection - Dice" dataset. It images of dice on a white background labeled by color.

Compatible Blocks

• Feature extraction: Image

• Learning block: Object Detection

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

Dataset Details

• Total Data Items: 85

• Labeling Method: object detection

• Train/Test Split: 80.00% / 20.00%

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_497418,
    title = {Object Detection - Dice colors},
    author = {Edge Impulse},
    year = {2024},
    url = {https://studio.edgeimpulse.com/public/497418/latest},
    note = {Apache 2.0}
}

Task: Object Detection

License: Apache 2.0

Description

This dataset has been collected by Edge Impulse teams and contains images of dice on a white background. It can be used to count the number of items in the frame.

Compatible Blocks

• Feature extraction: Image

• Learning block: Object Detection

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

Dataset Details

• Total Data Items: 85

• Labeling Method: object detection

• Train/Test Split: 80.00% / 20.00%

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_497423,
    title = {Object Detection - Dice},
    author = {Edge Impulse},
    year = {2024},
    url = {https://studio.edgeimpulse.com/public/497423/latest},
    note = {Apache 2.0}
}

Task: Object Detection

License: Apache 2.0

Description

This dataset has been collected by Edge Impulse team and used extensively to design the FOMO (Faster Objects, More Objects) object detection architecture.

See FOMO documentation or the announcement blog post.

Compatible Blocks

• Feature extraction: Image

• Learning block: Object Detection, NVIDIA TAO,

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

Dataset Details

• Total Data Items: 670

• Labeling Method: object detection

• Train/Test Split: 79.70% / 20.30%

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_497627,
    title = {Object Detection - Cubes colors on conveyor belt},
    author = {Edge Impulse},
    year = {2024},
    url = {https://studio.edgeimpulse.com/public/497627/latest},
    note = {Apache 2.0}
}

Task: Object Detection

License:

Description

Compatible Blocks

Dataset Details

• Total Data Items: 101

• Labeling Method: object detection

• Train/Test Split: 78.22% / 21.78%

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: Object Detection

License: Apache 2.0

Description

This dataset has been collected by Edge Impulse teams and contains images of bottle racks on different backgrounds and different orientations. It can be used to count the number of beers in the rack. The two labels - empty or full - is used to distinguish if the beer has a cap or not.

Compatible Blocks

• Feature extraction: Image

• Learning block: Object Detection, NVIDIA TAO,

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

Dataset Details

• Total Data Items: 130

• Labeling Method: object detection

• Train/Test Split: 74.62% / 25.38%

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_497424,
    title = {Object Detection - Bottles rack},
    author = {Edge Impulse},
    year = {2024},
    url = {https://studio.edgeimpulse.com/public/497424/latest},
    note = {Apache 2.0}
}

Task: Object Detection

License: Apache 2.0

Description

This dataset, collected by Edge Impulse team, is a variant of the Cubes on Conveyor Belt dataset. It has been used to perform spatial aware object detection using a micro transformer.

Have a look at this blog post for more information: FOMO Self-Attention

Dataset Details

• Total Data Items: 85

• Labeling Method: object detection

• Train/Test Split: 97.65% / 2.35%

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_497628,
    title = {Object Detection - Self Attention - Cubes on conveyor belt},
    author = {Edge Impulse},
    year = {2024},
    url = {https://studio.edgeimpulse.com/public/497628/latest},
    note = {Apache 2.0}
}