Update README.md

README.md

@@ -13,7 +13,7 @@ tags:
 
 # Model Card for Model ID
 
-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.
+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.
 
@@ -94,7 +94,7 @@ ImageNet-1k
 
 ### Training Procedure 
 
-We use the training script in `timm`
+We used the following training script in `timm`
 
 ```
 python3 train.py ../datasets/imagenet/ --model resnet50 --num-classes 1000 --lr 0.1 --warmup-epochs 5 --epochs 240 --weight-decay 1e-4 --sched cosine --reprob 0.4 --recount 3 --remode pixel --aa rand-m7-mstd0.5-inc1 -b 192 -j 6 --amp --dist-bn reduce