Datasets:

tfnn
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HeadsNet

@@ -90,4 +90,5 @@ The basic premise of how this network is trained and thus how the dataset is gen
 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.
 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

 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.
 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