Open SilverViking opened 4 years ago
I guess we would later need to be able to filter the test images from the events file and update the UI every now and then.
neuralnets
biosegment branch instead of master branchThe neuralnets
library now has a separate branch biosegment
that is specifically designed for this application. The train_net
on this branch trains for one epoch and tests the model by segmenting the complete volume and computing the IoU. Over time, the model with the highest IoU will be saved in the log directory as best_checkpoint.pytorch
.
A consequence of this branching is that it will no longer be possible to install the correct version of neuralnets
through pip
(this is connected to the master branch). We will have to install neuralnets
by cloning it do a directory NN_DIR
and include NN_DIR/neuralnets
in the PYTHONPATH
. I added this to the to do.
We will have to install
neuralnets
by cloning it do a directoryNN_DIR
and includeNN_DIR/neuralnets
in thePYTHONPATH
. I added this to the to do.
There is no need for manually cloning the neuralnets library, we can have conda do this for us. See https://github.com/vibbic/biosegment/commit/bf2902b691f0e4b3dc6783b81ab700919cfc25d2
Retraining a model takes a lot of time. We should
One simple approach for 2. would be to add a hyperlink to the frontend which opens the Tensorboard, which would should the training progress. Training information is saved by the neuralnets library in data/models/[project e.g. EML]/[name of model e.g. test_run2]/events.out.tfevents. and _checkpoint.pytorch. Eventually we would probably want to incorporate information similar to tensorboard directly in the biosegment frontend itself. (Use MLflow?)