Model Types

The SCiO SDK supports identification of material by using two different models, Classification and Estimation.

Classification is the differentiation between categories, based on the spectral fingerprint of its components.
For example, if you were building your own app from this type of model, you could classify an apple from an orange, different types of pills, genuine leather vs. fake leather, and more.

Estimation works on a collection of samples which have a common numerical attribute, such as sugar content, fat content, hydration level of plants, and so on. Based on previously measured samples which have a full range of the target attribute, your users can scan their own sample and find out the value of the attribute of interest.

How do I design a new data collection within SCiO Universe?

Ideally, a data collection should be large enough to build a working model.
Let’s use an example of creating a data collection of a certain type of pills (Ibuprofen) for our example.
If you want to build an ideal model, you need to start the first data set with 20 bottles of Ibuprofen from each manufacturer (ideally from different production lots), where you scan 5 pills from each bottle, and scan each pill three separate times.

3x5x20 = 300 total scans (for each manufacturer)
The result of the 300 scans is a baseline for establishing one type of pill from one manufacturer.
Next, you’ll do the same process for another pill from another manufacturer. Again, 20 bottles, where you scan 5 pills from each bottle, and scan each pill three separate times.

3x5x20 x 2 =600 total scans

From here, you’ll have two baselines from two manufacturers and can create your first model.