Since arivis Vision4D 3.6 arivis has included Deep Learning inference operations in the analysis panel. As of the release of arivis Pro 4.2, pipelines can run DL Instance segmentation. Like all pipelines, those that include DL Instance segmentation can be shared to use on other systems and with other images, and can also be run in batch. However, DL pipelines must be linked to the specific model on which they are based and these models must be exported as well as the pipeline and linked back to the pipeline before execution.

There are two ways to select a model for use in a pipeline. Whether we are using the Deep Learning Reconstruction or the Deep Learning Segmenter, once it is added to the pipeline we need to select the model to be used:

The model can either be loaded from a file as either and ONNX or CZANN file, or selected from the arivis Cloud model store.

ONNX or CZANN models typically only allow semantic segmentation. If the model has been created outside of the Zeiss ecosystem, other formats are commonly used but these can usually be converted to ONNX and we provide some scripts to do this, including for PyTorch and Cellpose. 

Access to models from arivis Cloud in arivis Pro is managed through access tokens. These access tokens give an arivis Pro installation access to every model in an arivis Cloud account. Therefore, rather than sharing an access token, which would give access to every model linked to an account, it is better to share the specific model with collaborators so that they can access the model by creating their own access token.

Arivis Cloud models can be trained for either instance or semantic segmentation. Semantic models can be exported as ONNX or CZANN directly from your arivis Cloud account. Instance models are only accessible through Access Tokens as mentioned above. Also, please note that DL segmentation in arivis Cloud instance models requires that you have installed and configured Docker on your system.

In either case, it is also a good idea to install and use the GPU acceleration package if you haven't done so already as this can significantly speed up DL and ML tasks.