Update README.md

Author: Kamil A. Kaczmarek

README.md

@@ -7,11 +7,15 @@ Open solution to the [CrowdAI Mapping Challenge](https://www.crowdai.org/challen
 1. Source code and [issues](https://github.com/minerva-ml/open-solution-mapping-challenge/issues) are publicly available.
 
 ## Results
+`0.943` **Average Precision** :rocket:
+
+`0.954` **Average Recall** :rocket:
+
 <img src="https://gist.githubusercontent.com/jakubczakon/cac72983726a970690ba7c33708e100b/raw/0f88863b18904b23d4301611ddf2b532aff8de96/example_output.png"></img>
 
 _No cherry-picking here, I promise :wink:. The results exceded our expectations. The output from the network is so good that not a lot of morphological shenanigans is needed. Happy days:)_
 
-Our approach got `0.943` **Average Precision** :rocket: and `0.954` **Average Recall** :rocket: on [stage 1 data](https://www.crowdai.org/challenges/mapping-challenge/dataset_files). Both were calculated using [pycocotools](https://github.com/cocodataset/cocoapi/tree/master/PythonAPI/pycocotools). Check this [blog post](https://medium.com/@jonathan_hui/map-mean-average-precision-for-object-detection-45c121a31173) for average precision explanation.
+Average Precision and Average Recall wre alculated on [stage 1 data](https://www.crowdai.org/challenges/mapping-challenge/dataset_files) using [pycocotools](https://github.com/cocodataset/cocoapi/tree/master/PythonAPI/pycocotools). Check this [blog post](https://medium.com/@jonathan_hui/map-mean-average-precision-for-object-detection-45c121a31173) for average precision explanation.
 
 # Reproduce it!
 Check [REPRODUCE_RESULTS](REPRODUCE_RESULTS.md)