Update README.md

README.md

@@ -107,4 +107,5 @@ More about this type of network topology can be read here: https://gist.github.c
 * 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. 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).
+* 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.