Training a model
This is a label for your convenience that will be shown in the AI pipelines table. This will help you distinguish between multiple versions.
Select one or more classes that you created to include in training. Any classes you do not check here will be excluded from training. The labels included will be the intersection between the files that were marked as completed, shown in Included images, and the classes that are included.
In this section, you will create a set of data that will not be trained on, and used only to assess performance. This is often called a test set or hold-out set. This is important to realistically evaluate the performance you will get during analysis time. You can read more about training and test sets here.
In your first few iterations, it's more important to use all of your training data, as this won't be your final model. We recommend not using any tiles for testing until your model performance starts to look very good.
Errors: This section shows any items that are required to be resolved before you can submit. One example of an error is having too few annotations. We require more than 50 annotations for each AI training.
Warnings: Indicates items we strongly recommend you resolve to before submitting the training job for best results. These items will not stop you from submitting the training job.
We support the following model types:
- Instance segmentation (classify, segment, and separate each type of object within an image)
- Semantic segmentation (classify and segment out object regions within an image)
We use state-of-the-art models for our segmentation models, transfer learning off an optimal dataset. Our architectures are usually based off of vision transformer bases.