Right now the Repository has its own stand alone code for using the model (training, evaluation). This Code is basically a copy of TensorVision Code. As of now, MediSeg is 100% compatible to TensorVision, so the model can be trained ether by using tv-train or by running python train.py. The question is, whether it should remain like this. It has been slightly touched in #3.
What we can do basically is:
- Remove the control code and ship only ship the core model. Feature Versions can be only trained using TensorVision.
- Keep a copy of controlling code inside the repository, making it possible to use MediSeg as stand alone.
A mixture might also be considered. One might remove training code, forcing to use TV for training, but keep evaluation or prediction code inside the repository.
I am going to keep compatibility between TV and MediSeg, so the question is mainly about whether we should provide a stand-alone Version by hand-copy code.
Right now the Repository has its own stand alone code for using the model (training, evaluation). This Code is basically a copy of TensorVision Code. As of now, MediSeg is 100% compatible to TensorVision, so the model can be trained ether by using
tv-trainor by runningpython train.py. The question is, whether it should remain like this. It has been slightly touched in #3.What we can do basically is:
A mixture might also be considered. One might remove training code, forcing to use TV for training, but keep evaluation or prediction code inside the repository.
I am going to keep compatibility between TV and MediSeg, so the question is mainly about whether we should provide a stand-alone Version by hand-copy code.