Update about.md

about.md

@@ -1,4 +1,4 @@
-Trained with 1 GPU H800 Server from AutoDL on 2025.2.3 BJS with Pytroch and converted to .h5 format at the same time. 
+Trained with 1 GPU H800 Server from AutoDL on 2025.2.3 UTC+8 with Pytroch and converted to .h5 format at the same time. 
 
 Basic Model uses CNN with accuracy of 75% on test data (80.7 MB)
 
@@ -8,8 +8,9 @@ V2 Engine uses ViT with accuracy of at most 40% Keyboard Interrupted 2025.2.3 15
 
 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. 
+Trained 2025.2.4 UTC+8 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. 
@@ -20,6 +21,8 @@ The technology used in this solution combines EfficientNet-B0 as the base model,
 After training and optimization, the final quantized model achieves a compact size of 16.6 MB, making it highly efficient for deployment. 
 On the test dataset, the model delivers a strong final accuracy of 93.78%, demonstrating its effectiveness in jersey number detection while meeting strict size constraints.
 
+Trained 2025.2.5 UTC+8 with H800
+
 E2 Engine: 94.6% Accuracy on test data
 E2 technology represents an advanced iteration of E1, focusing on enhanced efficiency, security, and scalability. 
 While E1 laid the foundational groundwork by optimizing basic system processes and improving task automation, E2 takes a step further by integrating more sophisticated encryption protocols, leveraging machine learning for predictive performance, and streamlining resource allocation.