Kutluhan Aktar / Dental Model Classifier Public

Kutluhan Aktar / Dental Model Classifier

This is your Edge Impulse project. From here you acquire new training data, design impulses and train models.

About this project

Project Description

This object detection (FOMO) model makes predictions on dental cast accuracy categories (classes) of 3D-printed dental casts (impressions):

  • Cast
  • Failed
  • Implant

After building my object detection (FOMO) model, I deployed my model as an Arduino library and uploaded it to Sony Spresense so as to run inferences.

home_2.jpg

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 46 windows
Image training labels JSON file 46 windows
Image testing data NPY file 8 windows
Image testing labels JSON file 8 windows
Object detection model TensorFlow Lite (float32) 82 KB
Object detection model TensorFlow Lite (int8 quantized) 56 KB
Object detection model TensorFlow SavedModel 186 KB
Object detection model Keras h5 model 88 KB

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Summary

Data collected
54 items

Project info

Project ID 120761
Project version 1