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: Image Classification

License: Apache 2.0

Description

This dataset contains images of fire extinguisher safety pins. This dataset can be used to combine image classification and visual anomaly detection.

Label information:

• rot-powder: Pin for ROT powder fire extinguisher - Yellow - manufacturer reference: 06210409F

• rot-water: Pin for ROT water spray fire extinguisher - Blue - manufacturer reference: 06210410F

• anomaly: Other fire extinguisher safety pins

Compatible Blocks

• Feature extraction: Image

• Learning block: Transfer Learning (Images), NVIDIA TAO, Classification

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

Dataset Details

• Total Data Items: 264

• Labeling Method: single label

• Train/Test Split: 79.92% / 20.08%

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_497409,
    title = {Image Classification - Fire extinguisher safety pin},
    author = {Edge Impulse},
    year = {2024},
    url = {https://studio.edgeimpulse.com/public/497409/latest},
    note = {Apache 2.0}
}

This datasets collection contains publicly available datasets collected, generated or curated by Edge Impulse or its partners.

Each of these datasets highlights a specific use case or machine learning task, helping you understand the types of data commonly encountered in projects like object detection, audio classification, and visual anomaly detection. While not intended for production models, they serve as valuable resources for learning, experimentation, and prototyping. Whether you're a developer, researcher, or student, these datasets are here to support your exploration of edge AI by offering real-world scenarios and insights into data preparation and model development.

Image

Image Classification

Object Detection

Visual Anomaly Detection

Audio

Audio Classification

Time-series

Motion and Vibration Classification

Sensor Fusion Classification

Task: Image Classification

License:

Description

This dataset has been collected by Edge Impulse teams and contains images taken from a smartphone using the NATIONAL GEOGRAPHIC 40x-1280x Microscope.

Compatible Blocks

Dataset Details

• Total Data Items: 246

• Labeling Method: single label

• Train/Test Split: 81.30% / 18.70%

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 has been synthetically generated by Edge Impulse teams using NVIDIA Omniverse and Edge Impulse Omniverse Extension.

You can also have a look at this tutorial created by Edge Impulse expert George Igwegbe to create a synthetic dataset using NVIDIA Omniverse Replicator.

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: 101

• Labeling Method: object detection

• Train/Test Split: 78.22% / 21.78%

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_497676,
    title = {Object Detection - Cans on conveyor belt},
    author = {Edge Impulse},
    year = {2024},
    url = {https://studio.edgeimpulse.com/public/497676/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: 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}
}

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}
}

Task: Visual Anomaly Detection

License: Apache 2.0

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

• 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: 69.74% / 30.26%

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_497422,
    title = {Visual Anomaly Detection - DHT11},
    author = {Edge Impulse},
    year = {2024},
    url = {https://studio.edgeimpulse.com/public/497422/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 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 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 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: Audio Classification

License:

Description

This dataset has been generated by Edge Impulse teams to recognize the sound of glass breaking.

Compatible Blocks

Dataset Details

• Total Data Items: 500

• Total Data Length: 0h 21m 15s

• Axis Summary: audio

• Labeling Method: single label

• Train/Test Split: 80.00% / 20.00%

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: Audio Classification

License:

Description

This dataset has been collected by Edge Impulse teams to recognize the sound of water running from a faucet, even in the presence of other background noise.

Compatible Blocks

Dataset Details

• Total Data Items: 18

• Total Data Length: 0h 15m 40s

• Axis Summary: audio

• Labeling Method: single label

• Train/Test Split: 88.89% / 11.11%

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: Audio Classification

License:

Description

This dataset can be used to build an Edge AI project detecting the "Hello World" keyword phrase.

Compatible Blocks

Dataset Details

• Total Data Items: 2062

• Total Data Length: 0h 34m 22s

• Axis Summary: audio

• Labeling Method: single label

• Train/Test Split: 79.97% / 20.03%

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: Motion and Vibration Classification

License:

Description

Dataset Details

• Total Data Items: 11

• Labeling Method: single label

• Train/Test Split: 72.73% / 27.27%

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: Sensor Fusion Classification

License: Apache 2.0

Description

This dataset has been collected by Edge Impulse team to demonstrate how to perform Sensor Fusion Classification leveraging Neural Networks Embeddings.

Sensor fusion refers to the process of combining data from different types of sensors to give more information to the neural network. To extract meaningful information from this data, you can use the same DSP block, multiples DSP blocks, or use neural networks embeddings.

You can have a look at this tutorial for more information to see how to use this dataset.

This dataset combines audio data and accelerometer data.

Recommended Impulse

• Feature extraction: Audio Embeddings (Spectrogram + Convolution Neural Network Embeddings) + Spectral Analysis (accelerometer)

• Learning block: Classification using a fully-connected network

Dataset Details

• Total Data Items: 36

• Labeling Method: single label

• Train/Test Split: 91.67% / 8.33%

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_497832,
    title = {Sensor Fusion Classification - Coffee machine stages},
    author = {Edge Impulse},
    year = {2024},
    url = {https://studio.edgeimpulse.com/public/497832/latest},
    note = {Apache 2.0}
}

Task: Motion and Vibration Classification

License: Apache 2.0

Description

This is a prebuilt dataset, collected by Edge Impulse teams, for a gesture recognition system based on continuous motion, for the Continous Motion Recognition tutorial. It contains 15 minutes of data sampled from a MEMS accelerometer at 62.5Hz over the following four classes:

• Idle - board sits idly on your desk. There might be some movement detected, e.g. from typing while the board is present.

• Snake - board moves over the desk as a snake.

• Updown - board moves up and down in a continuous motion.

• Wave - board moves left and right like you're waving to someone.

Compatible Blocks

• Feature extraction: Spectral Features (FFTs or Wavelets)

• Learning block: Classification + optionally Anomaly Detection (K-Means) or Anomaly Detection (GMM)

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

Dataset Details

• Total Data Items: 101

• Labeling Method: single label

• Train/Test Split: 84.16% / 15.84%

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_497631,
    title = {Motion Classification - Continuous motion recognition},
    author = {Edge Impulse},
    year = {2024},
    url = {https://studio.edgeimpulse.com/public/497631/latest},
    note = {Apache 2.0}
}