DHT11
Last updated
Last updated
Task: Visual Anomaly Detection
License: Apache 2.0
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.
Feature extraction: Image
Learning block: Visual Anomaly Detection (FOMO-AD)
Not sure what to choose? Try out this dataset with the EON Tuner.
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 |
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
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:
Upload portals (Enterprise feature)
If you use this dataset in your research paper, please cite it using the following BibTeX:
