Update about.md

about.md

@@ -15,7 +15,14 @@ Residual Connections: Implement residual connections to ease training in the ver
 Increased Filters: Use more filters in the layers to increase the learning capacity. 
 Accuracy 89.39% on test data.
 
-E1 Engine :
+E1 Engine : 93.78% Accuracy on test data
 The technology used in this solution combines EfficientNet-B0 as the base model, enhanced by knowledge distillation from a ResNet-34 teacher model to improve accuracy, and quantization to reduce the model size.
 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.
+
+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. 
+This allows E2 to better handle complex, real-time data analysis, which was a challenge for E1 due to its more rigid system structures. 
+Additionally, E2 offers increased compatibility with distributed systems, making it ideal for applications requiring higher data throughput and reliability, thus positioning it as the go-to solution for cutting-edge enterprise environments. 
+In essence, E2 is a more adaptive and intelligent evolution of E1's capabilities, with a stronger emphasis on securing and managing large-scale systems effectively.