Kutluhan Aktar / AI-assisted Air Quality Monitor
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Project Description
This model detects air pollution levels (classes) based on ambient nitrogen dioxide concentration, ozone concentration, temperature, humidity, and wind speed:
- Clean
- Risky
- Unhealthy
After building my neural network model, I deployed my model as an Arduino library and uploaded it to FireBeetle ESP32. Also, I employed FireBeetle ESP32 in combination with its media board to capture real-time surveillance footage for further examination and communicate with the web application I developed to display the model detection results.
Download block output
| Title | Type | Size | |
|---|---|---|---|
| CSV Wizard config | JSON file | 345 Bytes | |
| Raw data training data | NPY file | 150 windows | |
| Raw data training labels | NPY file | 150 windows | |
| Raw data testing data | NPY file | 45 windows | |
| Raw data testing labels | NPY file | 45 windows | |
| Classifier model | TensorFlow Lite (float32) | 3 KB | |
| Classifier model | TensorFlow Lite (int8 quantized) | 2 KB | |
| Classifier model | TensorFlow SavedModel | 9 KB | |
| Classifier model | Keras h5 model | 3 KB |
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Summary
Data collected
3m 15sProject info
| Project ID | 192207 |
| Project version | 1 |
| License | Apache 2.0 |
