improve clarity

README.md

@@ -62,7 +62,7 @@ model = torch.load(model_name)
 ### Top-1 Accuracy Comparisons
 I finetuned the existing models on either 299x299, 304x304, 320x320, or 384x384 resolution, depending on the input size used during pretraining and the VRAM usage.
 
-`efficientnet_b3_pruned` achieved the second highest top-1 accuracy as well as the highest epoch-1 training accuracy on my task, out of all previous EfficientNet models my 24 GB VRAM RTX 3090 could handle.
+Aside from EfficientNetV2.5, `efficientnet_b3_pruned` achieved the highest top-1 accuracy as well as the highest epoch-1 training accuracy on my task, out of all existing EfficientNet models my 24 GB VRAM RTX 3090 could handle.
 
 I will publish the detailed report in another model repository, including the link to the GVNS benchmarks. 
 This repository is only for the base model, pretrained on ImageNet, not my task.