Demo Team / Face detection - FOMO
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About this project
Face detection using FOMO
Want to see a deeper explanation about FOMO and a live demo? See Arm AI Tech Talk about FOMO
Dataset Information
This project is using a subset of the FFHQ dataset where the bounding boxes have been labeled manually by Edge Impulse team.
Sensor & Block Information
- Camera module with input images 96 x 96 pixels
- Image block to normalize the image data, and keep the color depth to RGB
- FOMO transfer learning block based on MobileNetV2 0.35
Interested in adding object detection capabilities to your constrained edge devices? Clone this project to build an object detection project to detect cans and bottles on the tiniest of devices!
Read more on our announcement blog to find out more about FOMO and to learn how to build your object detection project, from data collection to deployment on embedded devices.
Download block output
| Title | Type | Size | |
|---|---|---|---|
| Image training data | NPY file | 314 windows | |
| Image training labels | JSON file | 314 windows | |
| Image testing data | NPY file | 99 windows | |
| Image testing labels | JSON file | 99 windows | |
| Object detection model | TensorFlow Lite (float32) | 83 KB | |
| Object detection model | TensorFlow Lite (int8 quantized) | 56 KB | |
| Object detection model | TensorFlow SavedModel | 188 KB | |
| Object detection model | Keras h5 model | 90 KB |
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Summary
Data collected
413 itemsProject info
| Project ID | 87291 |
| Project version | 5 |
| License | No license attached |
