Bumps version to 0.2.8

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. 
 

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 <[email protected]>"]
 license = "Apache-2.0"