Update about.md

about.md

@@ -2,8 +2,16 @@ Trained with 1 GPU H800 Server from AutoDL on 2025.2.3 BJS with Pytroch and conv
 
 Basic Model uses CNN with accuracy of 75% on test data (80.7 MB)
 
-V1 Engine uses CNN with accuracy of 87% on test data (160 MB)
+V1 Engine uses CNN with accuracy of 87% on test data (72.1 MB)
 
 V2 Engine uses ViT with accuracy of at most 40% Keyboard Interrupted 2025.2.3 15:57:37 BJS
 
-V3 Engine uses Hybrid Model( Combination of Convolutional layers and a Multi-Layer Perceptron (MLP)) with accuracy 68.65% on test data. (102 MB)
+V3 Engine uses Hybrid Model( Combination of Convolutional layers and a Multi-Layer Perceptron (MLP)) with accuracy 68.65% on test data. (34.3 MB)
+
+Trained 2025.2.4 BJS with H800
+V4 Engine based of V1 but improve with: More Convolutional Layers. 
+Bottleneck Blocks: We can use bottleneck blocks (1x1 conv before and after 3x3 conv) to reduce computation, and increase depth.
+Residual Connections: Implement residual connections to ease training in the very deep network and to help avoid vanishing gradients. 
+Increased Filters: Use more filters in the layers to increase the learning capacity. 
+Accuracy 89.39% on test data.
+