Update README.md

README.md

@@ -88,7 +88,7 @@ The basic premise of how this network is trained and thus how the dataset is gen
 2. All model vertices are reverse traced from the vertex position to the perimeter of the unit sphere using the vertex normal.
 3. The nearest position on a 10,242 vertex icosphere is found and the network is trained to output the model vertex position and vertex color (6 components) at the index of the icosphere vertex.
 4. The icosphere vertex index is scaled to a 0-1 range before being input to the network.
-5. The network only has two input parameters, the other parameter is a 0-1 model ID which is randomly selected and all vertices for a specific model are trained into the network using the randomly selected ID.
+5. The network only has two input parameters, the other parameter is a 0-1 model ID which is randomly selected and all vertices for a specific model are trained into the network using the randomly selected ID. This ID does not change per-vertex it only changes per 3D model.
 6. The ID allows one to use this parameter as a random seed, to generate a random Head using this network you would input a random 0-1 seed and then iterate the icosphere index parameter to some sample range between 0-1 so if you wanted a 20,000 vertex head you would iterate between 0-1 at 20,000 increments of 0.00005 as the network outputs one vertex position and vertex color for each forward-pass.
 
 More about this network topology can be read here: https://gist.github.com/mrbid/1eacdd9d9239b2d324a3fa88591ff852