diff --git a/README.md b/README.md
index 513524ed..d17a8f42 100644
--- a/README.md
+++ b/README.md
@@ -78,7 +78,7 @@ File [`perceptual_loss.py`](perceptual_loss.py) shows how to iteratively optimiz
 
 We found that deep network activations work surprisingly well as a perceptual similarity metric. This was true across network architectures (SqueezeNet [2.8 MB], AlexNet [9.1 MB], and VGG [58.9 MB] provided similar scores) and supervisory signals (unsupervised, self-supervised, and supervised all perform strongly). We slightly improved scores by linearly "calibrating" networks - adding a linear layer on top of off-the-shelf classification networks. We provide 3 variants, using linear layers on top of the SqueezeNet, AlexNet (default), and VGG networks.
 
-**If you use LPIPS in your publication, please specify which version you are using.** The current version is 0.1. You can set `version='0.0'` for the initial release.
+If you use LPIPS in your publication, please specify which version you are using. The current version is 0.1. You can set `version='0.0'` for the initial release.
 
 ## (2) Berkeley Adobe Perceptual Patch Similarity (BAPPS) dataset