Sorry in advance for the long list of questions below. I am trying to confirm of the functionality that Prodigy can provide.
First, I am wondering if we’re trying to create multiple named entities which are specifically used to label product concepts, such as ‘size, color, durability, service’ etc., could we use Prodigy to easily annotate multiple product concepts on its UI, thus coming up with appropriate training dataset.
Regarding to image classification, could we also label images to multiple classes via Prodigy UI. Instead of object detection, I am now more interested on image classification. Whether it’s a good shot, has a good composition etc. Thus, we can train model to identify good images from bad ones. We need a more efficient way to come up with training dataset.
Could we export the annotated samples with its corresponding labels for use in other cases?
I know that while in the process of annotation, Prodigy already kicks off refining its model to the new data points. I am wondering if we can set up our own model instead of the default to be used in annotation learning process.
Regarding to the model that’s constantly refined in the annotation process, could we export the model and use it in other cases. I am wondering what framework the default model is built on. I am more familiar with PyTorch, thus, not sure if we can specify the framework to be used in Prodigy’s default model.
Thanks again if anyone could help clarifying these question. Really appreciated.