Datasets:

tfnn
/

HeadsNet

@@ -111,4 +111,5 @@ More about this type of network topology can be read here: https://gist.github.c
 * 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.
 * Added [HeadsNet3](https://huggingface.co/datasets/tfnn/HeadsNet/resolve/main/HeadsNet3.7z?download=true) using the [FaceTo3D](https://huggingface.co/datasets/tfnn/FaceTo3D) dataset 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.
-* Added [HeadsNet4](https://huggingface.co/datasets/tfnn/HeadsNet/resolve/main/HeadsNet4.7z?download=true) this version uses 32^3 micro MLP's with a single voxel grayscale output per network, the trained datasetd are included and the program to generate them but you will need to download the PLY files to generate the dataset from the [FaceTo3D](https://huggingface.co/datasets/tfnn/FaceTo3D) repository.

 * 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.
 * Added [HeadsNet3](https://huggingface.co/datasets/tfnn/HeadsNet/resolve/main/HeadsNet3.7z?download=true) using the [FaceTo3D](https://huggingface.co/datasets/tfnn/FaceTo3D) dataset 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.
+* Added [HeadsNet4](https://huggingface.co/datasets/tfnn/HeadsNet/resolve/main/HeadsNet4.7z?download=true) this version uses 32^3 micro MLP's with a single voxel grayscale output per network, the trained datasetd are included and the program to generate them but you will need to download the PLY files to generate the dataset from the [FaceTo3D](https://huggingface.co/datasets/tfnn/FaceTo3D) repository.
+* This is being continued with more success over at GitHub: https://github.com/mrbid/FaceTo3D