Edge Impulse Inc. / Visual GMM cracks
Dataset acquired from https://digitalcommons.usu.edu/all_datasets/48/
About this project
Visual Anomaly Detection (FOMO-AD)
Read more about FOMO-AD here: https://docs.edgeimpulse.com/docs/edge-impulse-studio/learning-blocks/visual-anomaly-detection
Dataset: https://digitalcommons.usu.edu/all_datasets/48/
Maguire, M., Dorafshan, S., & Thomas, R. J. (2018). SDNET2018: A concrete crack image dataset for machine learning applications. Utah State University. https://doi.org/10.15142/T3TD19
Download block output
| Title | Type | Size | |
|---|---|---|---|
| Image training data | NPY file | 1040 windows | |
| Image training labels | NPY file | 1040 windows | |
| Image testing data | NPY file | 268 windows | |
| Image testing labels | NPY file | 268 windows | |
| Visual anomaly detection model | TensorFlow Lite (float32) | 68 KB | |
| Visual anomaly detection model | TensorFlow Lite (int8 quantized) | 49 KB | |
| Visual anomaly detection model | TensorFlow Lite (float32) - Model head | 19 KB | |
| Visual anomaly detection model | Model evaluation metrics (JSON file) | 89 Bytes | |
| Visual anomaly detection model | TensorFlow SavedModel | 98 KB | |
| Visual anomaly detection model | Keras h5 model | 75 KB |
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Summary
Data collected
1,308 itemsProject info
| Project ID | 288658 |
| License | Apache 2.0 |
