Kutluhan Aktar / IoT AI-driven Smart Grocery Cart
This is your Edge Impulse project. From here you acquire new training data, design impulses and train models.
About this project
This object detection (FOMO) model detects a small group of food retail products by utilizing the product brand names as labels:
- Barilla
- Milk
- Nutella
- Pringles
- Snickers
After building my object detection (FOMO) model, I deployed my model as an OpenMV firmware and flashed OpenMV Cam H7 with the generated firmware so as to run inferences.
Creating your first impulse (100% complete)
Acquire data
Every Machine Learning project starts with data. You can capture data from a development board or your phone, or import data you already collected.
Design an impulse
Teach the model to interpret previously unseen data, based on historical data. Use this to categorize new data, or to find anomalies in sensor readings.
Deploy
Package the complete impulse up, from signal processing code to trained model, and deploy it on your device. This ensures that the impulse runs with low latency and without requiring a network connection.
Download block output
| Title | Type | Size | |
|---|---|---|---|
| Image training data | NPY file | 60 windows | |
| Image training labels | JSON file | 60 windows | |
| Image testing data | NPY file | 10 windows | |
| Image testing labels | JSON file | 10 windows | |
| Object detection model | TensorFlow Lite (float32) | 82 KB | |
| Object detection model | TensorFlow Lite (int8 quantized) | 56 KB | |
| Object detection model | TensorFlow SavedModel | 187 KB | |
| Object detection model | Keras h5 model | 88 KB |
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Summary
Data collected
70 itemsProject info
| Project ID | 166688 |
| Project version | 1 |
| License | Apache 2.0 |
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