diff --git a/README.md b/README.md index 1f88d81..34a634a 100644 --- a/README.md +++ b/README.md @@ -22,7 +22,7 @@ The easiest way to install the package is to first create a new conda environmen After installation, two new commands will be available from your terminal whilst your environment is activated, `model-train-2d` and `model-predict-2d`. -These commands require access to some settings stored in YAML files. These need to be located in a directory named `volseg-settings` within the directory where you are running the commands. The settings files can be copied from [here](https://github.com/DiamondLightSource/volume-segmantics/releases/download/v0.2.7/volseg-settings.zip). +These commands require access to some settings stored in YAML files. These need to be located in a directory named `volseg-settings` within the directory where you are running the commands. The settings files can be copied from [here](https://github.com/DiamondLightSource/volume-segmantics/releases/download/v0.2.8/volseg-settings.zip). The file `2d_model_train_settings.yaml` can be edited in order to change training parameters such as number of epochs, loss functions, evaluation metrics and also model and encoder architectures. The file `2d_model_predict_settings.yaml` can be edited to change parameters such as the prediction "quality" e.g "low" quality refers to prediction of the volume segmentation by taking images along a single axis (images in the (x,y) plane). For "medium" and "high" quality, predictions are done along 3 axes and in 12 directions (3 axes, 4 rotations) respectively, before being combined by maximum probability. diff --git a/pyproject.toml b/pyproject.toml index 9d9859b..5017410 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "volume-segmantics" -version = "0.2.7" +version = "0.2.8" description = "A toolkit for semantic segmentation of volumetric data using pyTorch deep learning models" authors = ["Oliver King "] license = "Apache-2.0"