Update README.md

README.md

@@ -3,15 +3,15 @@
 ## Summary
 
 This project aim to study the YOLOV3 architecture, and use it with transfert
-learning to be able to detect a new class : Firearm. This project might be
-usefull to alert police when arm robbery take place and a  firearm can be
+learning to be able to detect a new type of object : firearm. This project could be
+usefull to alert the police when an arm robbery take place and a  firearm can be
 detected on a surveillance camera.
 
-## File on the repository
+## Files on the repository
 
 You can find the keras implementation of the YOLOV3 architecture with the 
-custom weight and anchors obtained by transfer-learning. The dataset with the 
-hand made labels. The presentation and a short document explaining the
+custom weight and anchors obtained by transfer-learning, the dataset with the 
+hand made labels, the presentation and a short document explaining the
 architecture.
 
 ## Examples
@@ -36,7 +36,7 @@ python yolo_video.py [video_path] [output_path (optional)]</code>
  - You might have to modify the code to analyse custom video...
 
 ## Possible improvements
-- The architecture is only a keras adaptation of the Darknet architecture and use the original with transfer learning might be more efficient.
+- The architecture is only a keras adaptation of the Darknet architecture and using the original with transfer learning might be more efficient.
 - A lot of new architecture have been released since YOLOV3 and could provide better results. It would be interesting to train model like CenterNet ResNet-18 or  DSOD300 + VoVNet-27-slim.
 - Only a few pictures has been labeled and with not that much diversity on the content. Create a larger dataset would make a more robust model.