Pocket Kitchen is a system for managing food more efficiently in the home towards reducing household food waste. These scripts send detected incoming/outgoing food items in a fridge to a database.
- On-device object detection - Pocket Kitchen uses the Coral TPU and EdgeTPU TensorFlow models to perform object detection on-device. This means that no data is processed on the cloud, and all processing is done on the device itself.
- Customizable - Pocket Kitchen is designed to be customizable. For example, the user can add their own model trained on their own objects.
- Open Source - Pocket Kitchen is open source, so anyone can contribute to the project.
While similar to an API, these scripts are running locally and populating a database. The database is then accessed by the Pocket Kitchen app to display the data.
- Raspberry Pi 4
- Coral TPU
- Pi Camera (We use HD Camera Module V2, but any Pi camera should work. Autofocus is recommended.)
- 3D-Printed Camera Mount
- You can use the enclosed model or create your own custom fine-tune EfficientDet-Lite0 model from TensorFlow Edge Object Detection
$ bash setup.sh
$ python3 detect.py --model <model>
- Add support for true API