Update README.md

README.md

@@ -109,4 +109,5 @@ More about this type of network topology can be read here: https://gist.github.c
 
 ## 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. Start training with it instantly using [HeadsNet_v2_Trainer_with_Dataset.7z](https://huggingface.co/datasets/tfnn/HeadsNet/resolve/main/HeadsNet_v2_Trainer_with_Dataset.7z?download=true).
-* The system didn't work out, here I have trained models of various qualities: [HeadsNet_Trained_Models.7z](https://huggingface.co/datasets/tfnn/HeadsNet/resolve/main/HeadsNet_Trained_Models.7z?download=true). The network has some potential, with a better refined dataset and better network topology it could prove more successful.
+* The system didn't work out, here I have trained models of various qualities: [HeadsNet_Trained_Models.7z](https://huggingface.co/datasets/tfnn/HeadsNet/resolve/main/HeadsNet_Trained_Models.7z?download=true). The network has some potential, with a better refined dataset and better network topology it could prove more successful.
+* Added [HeadsNet3](https://huggingface.co/datasets/tfnn/HeadsNet/resolve/main/HeadsNet3.7z?download=true) a pivot where I attempted to train an FNN/MLP on a 1024 component input vector of a 32x32 grayscale image of a face to output a 32x32x32 grayscale voxel volume of a 3D head. Results where not overwhelmingly positive. Had higher hopes.