Edge Impulse Experts / AI-driven Interactive Lab Assistant
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About this project
This object detection (FOMO) model detects diverse lab equipment with the applied OpenCV modification feature:
- spoon_spatula
- forcep
- dynamometer
- bunsen_burner
- alcohol_burner
- test_tube
- skeleton_model
- microscope
- hatchery
- microscope_slide
After building my object detection model, I deployed my model as a fully optimized and customizable Linux (AARCH64) application (.eim) and uploaded it to NVIDIA® Jetson Nano. Also, I developed a user interface (Tkinter) from scratch to generate ChatGPT-powered lessons about the detected lab equipment.
Download block output
| Title | Type | Size | |
|---|---|---|---|
| Image training data | NPY file | 211 windows | |
| Image training labels | JSON file | 211 windows | |
| Image testing data | NPY file | 33 windows | |
| Image testing labels | JSON file | 33 windows | |
| Object detection model | TensorFlow Lite (float32) | 83 KB | |
| Object detection model | TensorFlow Lite (int8 quantized) | 56 KB | |
| Object detection model | TensorFlow SavedModel | 187 KB | |
| Object detection model | Keras h5 model | 89 KB |
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Summary
Data collected
244 itemsProject info
| Project ID | 323065 |
| Project version | 2 |
| License | Apache 2.0 |
