diff --git a/README.md b/README.md
index e8d04bd6..822b9ffe 100644
--- a/README.md
+++ b/README.md
@@ -6,7 +6,7 @@ Siamese and triplet networks are useful to learn mappings from image to a compac
 
 # Installation
 
-Requires [pytorch](http://pytorch.org/) 0.3.1 with torchvision 0.2.0
+Requires [pytorch](http://pytorch.org/) 0.4 with torchvision 0.2.1
 
 # Code structure
 
@@ -103,7 +103,7 @@ There are couple of problems with siamese and triplet networks:
 2. We generate pairs/triplets *randomly*. As the training continues, more and more pairs/triplets are **easy** to deal with (their loss value is very small or even 0), *preventing the network from training*. We need to provide the network with **hard examples**.
 3. Each image that is fed to the network is used only for computation of contrastive/triplet loss for only one pair/triplet. The computation is somewhat wasted; once the embedding is computed, it could be reused for many pairs/triplets.
 
-To deal with these issues efficiently, we'll feed a network with standard mini-batches as we did for classification. The loss function will be responsible for selection of hard pairs and triplets within mini-batch. If we feed the network with 16 images per 10 classes, we can process up to 159\*160/2 = 12720 pairs and 10\*16\*15/2\*(9\*16) = 172800 triplets, compared to 80 pairs and 53 triplets in previous implementation. 
+To deal with these issues efficiently, we'll feed a network with standard mini-batches as we did for classification. The loss function will be responsible for selection of hard pairs and triplets within mini-batch. If we feed the network with 16 images per 10 classes, we can process up to 159\*160/2 = 12720 pairs and 10\*16\*15/2\*(9\*16) = 172800 triplets, compared to 80 pairs and 53 triplets in previous implementation.
 
 Usually it's not the best idea to process all possible pairs or triplets within a mini-batch. We can find some strategies on how to select triplets in [2] and [3].
 
@@ -191,4 +191,3 @@ Online triplet loss with negative mining
 [4] Brandon Amos, Bartosz Ludwiczuk, Mahadev Satyanarayanan, [OpenFace: A general-purpose face recognition library with mobile applications](http://reports-archive.adm.cs.cmu.edu/anon/2016/CMU-CS-16-118.pdf), 2016
 
 [5] Yi Sun, Xiaogang Wang, Xiaoou Tang, [Deep Learning Face Representation by Joint Identification-Verification](http://papers.nips.cc/paper/5416-deep-learning-face-representation-by-joint-identification-verification), NIPS 2014
-