Update README.md

README.md

@@ -16,6 +16,7 @@ tags:
 
 Based on a class of partial differential equations called **quasi-linear hyperbolic systems** [[Liu et al, 2023](https://github.com/liuyao12/ConvNets-PDE-perspective)], the QLNet breaks into uncharted waters of ConvNet model space marked by the use of (element-wise) multiplication in lieu of ReLU as the primary nonlinearity. It achieves comparable performance as ResNet50 on ImageNet-1k (acc=**78.61**), demonstrating that it has the same level of capacity/expressivity, and deserves more analysis and study (hyper-paremeter tuning, optimizer, etc.) by the academic community.
 
+The overall architecture folllows that of the origianl ConvNet (LeCun) and ResNet (He et al.), with the use of "depthwise" as in MobileNet.
 
 ![](https://huggingface.co/liuyao/QLNet/resolve/main/PDE_perspective.jpeg)
 
@@ -60,7 +61,7 @@ class QLBlock(nn.Module):
 
 - **Developed by:** Yao Liu 刘杳
 - **Model type:** Convolutional Neural Network (ConvNet)
-- **License:** As academic work, it is free for all to use. It is a natural progression from the origianl ConvNet (of LeCun) and ResNet, with the use of "depthwise" as in MobileNet.
+- **License:** As academic work, it is free for all to use.
 - **Finetuned from model:** N/A (*trained from scratch*)
 
 ### Model Sources [optional]