Merge pull request #707 from AntonOellerer/patch-3

Update README.md

README.md

@@ -12,7 +12,7 @@ The code is tested using Tensorflow r1.7 under Ubuntu 14.04 with Python 2.7 and
 ## News
 | Date     | Update |
 |----------|--------|
-| 2018-04-10 | Added new models trained on Casia-WebFace and VGGFace2 (see below). Note that the models uses fixed image standardization (see [wiki](Training-using-the-VGGFace2-dataset)). |
+| 2018-04-10 | Added new models trained on Casia-WebFace and VGGFace2 (see below). Note that the models uses fixed image standardization (see [wiki](https://github.com/davidsandberg/facenet/wiki/Training-using-the-VGGFace2-dataset)). |
 | 2018-03-31 | Added a new, more flexible input pipeline as well as a bunch of minor updates. |
 | 2017-05-13 | Removed a bunch of older non-slim models. Moved the last bottleneck layer into the respective models. Corrected normalization of Center Loss. |
 | 2017-05-06 | Added code to [train a classifier on your own images](https://github.com/davidsandberg/facenet/wiki/Train-a-classifier-on-own-images). Renamed facenet_train.py to train_tripletloss.py and facenet_train_classifier.py to train_softmax.py. |
@@ -40,7 +40,7 @@ The best performing model has been trained on the [VGGFace2](https://www.robots.
 ## Pre-processing
 
 ### Face alignment using MTCNN
-One problem with the above approach seems to be that the Dlib face detector misses some of the hard examples (partial occlusion, silhouettes, etc). This makes the training set to "easy" which causes the model to perform worse on other benchmarks.
+One problem with the above approach seems to be that the Dlib face detector misses some of the hard examples (partial occlusion, silhouettes, etc). This makes the training set too "easy" which causes the model to perform worse on other benchmarks.
 To solve this, other face landmark detectors has been tested. One face landmark detector that has proven to work very well in this setting is the
 [Multi-task CNN](https://kpzhang93.github.io/MTCNN_face_detection_alignment/index.html). A Matlab/Caffe implementation can be found [here](https://github.com/kpzhang93/MTCNN_face_detection_alignment) and this has been used for face alignment with very good results. A Python/Tensorflow implementation of MTCNN can be found [here](https://github.com/davidsandberg/facenet/tree/master/src/align). This implementation does not give identical results to the Matlab/Caffe implementation but the performance is very similar.