Dataset Screenshot
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)
Dataset Details
- Total Data Items: 195
- Labeling Method: single label
- Train/Test Split: 69.74% / 30.26%
| Training Set | Testing Set | |
|---|---|---|
| Total Data Items | 136 | 59 |
| 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.
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Download
- Direct link
- HuggingFace - Soon
- Kaggle - Soon
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Import this dataset to your Edge Impulse project
This project uses the Edge Impulse Exporter Format (
info.labels). See Edge Impulse labels for more info. Edge Impulse also supports different data acquisition formats and dataset annotation formats that you can import into your project to build your edge AI models: