Datasets:

tfnn
/

HeadsNet

@@ -104,7 +104,7 @@ More about this type of network topology can be read here: https://gist.github.c
 * Future networks will have 3 additional input parameters one for each x,y,z of a unit vector for the ray direction from the icosphere index.
 * The unit vector used to train the network will just be the vertex normal from the 3D model but inverted.
 * When performing inference more forward-passes would need to be performed as some density of rays in a 30° or similar cone angle pointing to 0,0,0 would need to be performed per icosphere index position.
-* This could result in higher quality outputs.
 ## Updates
 * A new dataset has been generated [HeadsNet-2-6_v2.7z](https://huggingface.co/datasets/tfnn/HeadsNet/resolve/main/HeadsNet-2-6_v2.7z?download=true), the old one uses a 10,242 vertex unit icosphere and the new one uses a 655,362 vertex unit icosphere, this should lead to a higher quality network.

 * Future networks will have 3 additional input parameters one for each x,y,z of a unit vector for the ray direction from the icosphere index.
 * The unit vector used to train the network will just be the vertex normal from the 3D model but inverted.
 * When performing inference more forward-passes would need to be performed as some density of rays in a 30° or similar cone angle pointing to 0,0,0 would need to be performed per icosphere index position.
+* This could result in higher quality outputs; at the cost of an order of magnitude more forward-pass iterations.
 ## Updates
 * A new dataset has been generated [HeadsNet-2-6_v2.7z](https://huggingface.co/datasets/tfnn/HeadsNet/resolve/main/HeadsNet-2-6_v2.7z?download=true), the old one uses a 10,242 vertex unit icosphere and the new one uses a 655,362 vertex unit icosphere, this should lead to a higher quality network.