Load and Pre-process Data
This topic describes how to load data into PerceptiLabs, pre-process it, and get to a working model:
The first step towards creating a model in PerceptiLabs is to specify the model type:
Follow the steps below to specify a model type:
2. Choose one of the follow model types:
a. Image Classification: You get to choose a top-level directory, where each sub-directory represents a class of images.
b. Segment Images: You get to choose two directories, one containing images and one containing masks.
c. Multi-Modal: For non-image classification/segmentation projects. Here, it's assumed that the data files live in the relative path(s) specified in the CSV file.
You can choose to:
This is a good choice for getting a model up and running quickly using existing, publicly-available data. Scroll to a public dataset, hover your mouse over it, and click Load:
PerceptiLabs will load your public dataset, allowing you to create models from it. Wait for the dataset to complete loading, and then click Create to create your first model:
Click Browse, select the top-level directory containing your sub directories of images, and click Next:
Click Browse for both of the folder selections to specify the directories for the images and masks respectively, and then click Next to continue:
Click Browse, select the CSV to upload, and click Next:
After you have imported a dataset, you must the define your data settings:
2. (Optional) Click on the pre-processing button in the column heading to configure data pre-processing settings:
This allows you to define any pre-processing steps that PerceptiLabs should perform on your data (e.g., resizing images):
After configuring your pre-processing settings, click Save to return to the Data Wizard.