diff --git a/README.md b/README.md
index 0e0344a2..fd1a1499 100644
--- a/README.md
+++ b/README.md
@@ -14,28 +14,6 @@ bioimage_embed includes functions for loading and preprocessing images from micr
     pip install git+https://github.com/ctr26/bioimage_embed
     
    
-
-# Mask-VAE
-
-This project attempts to using modern auto-encoding CNNs to access the latent shape space of a given dataset.
-We demonstrate this on microscopy images of nuclei and C. Elegans (worms).
-The project includes some sensible tricks such as including symmetry $\min|M^T - M|_2^2$ and $\min|diag(M)|$ constraints to help the model learn that it's using distance matrices.
-
-## How it works
-
-- Take image masks (white on black)
-- Find their contour (thx [scikit image]((https://scikit-image.org/docs/dev/api/skimage.measure.html#skimage.measure.find_contours)))
-- Resample the contour to a standard length
-- Create euclidean distance matrix
-- Feed matrix as image into VAE
-- Train model on distance matrix
-- Opt. Convert distance matrix back to mask using MultiDimensionalScaling
-
-Potential uses for this projects are:
-
-- Synthetic shape generation for dataset augmentation
-- Shape-based phenotyping in the latent space
-
 ## Usage
 
 ### Get data
@@ -52,7 +30,11 @@ and or:
 
 ### Run
 
-    python train.py
+    bioimage_embed --help
+    
+    or
+
+    bie --help
 
 ### TODO