Simplifying using pre-trained COSEM models #356
Description
Hi there,
Finally found the time to look around and start playing with dacapo! I have a few questions and remarks regarding the COSEM models, some of which might just be born out of missing something. 😄
Fine-tuning COSEM requires knowing architecture and task
The documentation lists available COSEM models, but not their architecture parameters. Looking into the model repo (example for setup04), the parameters are different from those of a CNNectomeUNetConfig.
It would make sense to me to also list the parameters somewhere on the dacapo documentation, or better yet, provide ready-to-use convenience configuration.
Example:
from dacapo.experiments.architectures import COSEM_1820500
architecture_config = COSEM_1820500()More generally, the example from the documentation leaves out all the configuration that are necessary to create a RunConfig. But not knowing anything but the setup04 and 1820500 makes it unlikely that people will know how to instantiate the correct configurations!
Predicting with pre-trained COSEM model requires RunConfig
This point probably has to do with the fundamental aim of dacapo, but if one wants to simply load a model and predict on some images, the overhead is quite large! This notebook seems to do that (predict using fine-tuned), and a lot of the lines in the notebooks will be irrelevant to users.
Simplifying using pre-trained models
In my opinion, all this could be made more intuitive by (or/and):
- Providing ready-made
RunConfigfor the pre-trained models. These would allow simply predicting usingdacapo.predict.predict. Obviously, this would not be usable for training and would need to be documented. Alternatively, the sub-configurations could be retrieved that way, giving users the opportunity to not use thetrainanddatasplitconfigs from the pre-trained model. - Providing in the documentation the architecture parameters and tasks used to train the various pre-trained models would also allow a better understanding of the models, and help users set up the configurations correctly when fine-tuning.
- Providing a
predictfunction that does not require a fullRunConfig. I haven't looked too deep in the code yet, so I am not sure how feasible it is to only pass the relevant configurations (starter,architecture, etc..). This could be alleviated by using the ready-made config.
Apologies if this all stem from missing some important points!
Thanks!!