Update README.md

README.md

@@ -16,12 +16,12 @@ Training was performed on labeled 300 x 300 pixel images that were chipped from
 ![](/READMEimages/train_01.png) ![](/READMEimages/train_02.png) ![](/READMEimages/train_03.png) ![](/READMEimages/train_04.png) ![](/READMEimages/train_05.png) ![](/READMEimages/train_06.png)
 
 ## Validation
-A set of unlabeled validation images was kept separate from the train and test sets in order to validate the model. A total of 16 images was used for validation. Due to random data/image augmentation performed during training, the validation results can vary between training runs. However, I've found that at least 15 of the 17 wind turbines in the validation image set are detected with high probability. I have experienced 100% accuracy (all wind turbines detected correctly) however due to the randomness in training, each trained model will likely provide slightly different results. A few results from the validation step are shown below. 
+A set of unlabeled validation images was kept separate from the train and test sets in order to validate the model. A total of 16 images was used for validation. Due to random data/image augmentation performed during training, the validation results can vary between training runs. However, I've found that at least 15 of the 17 wind turbines in the validation image set are detected with high probability with the default training parameters. I have experienced 100% accuracy (all wind turbines detected correctly) however due to the randomness in training, each trained model will likely provide slightly different results. A few results from the validation step are shown below. 
 
 ![](/READMEimages/valid_01.png) ![](/READMEimages/valid_02.png) ![](/READMEimages/valid_03.png) ![](/READMEimages/valid_04.png) ![](/READMEimages/valid_05.png) ![](/READMEimages/valid_08.png) 
 
 ## Wind Turbine Detection and Localization
-Finally, the trained model is applied to large NAIP images covering a 4 mile by 4 mile area, approximately. To perform detection over this large area, a sliding window approach is used to analyze 300 x 300 pixel images over the the 5978 x 7648 pixel original image. Once this analysis is performed, a marker is plotted on the original NAIP image for each detected wind turbine. In addition, the latitude and longitude of each wind turbine is output for verification. Two NAIP images with all the detected wind turbines denoted with red markers are presented below. In addition, a table containing a subset of the latitude and longitude coordinates is shown below. The accuracy of the model is high however there are instances where non-wind turbine objects such as houses, barns, or roads are detected and classified as wind turbines. 
+Finally, the trained model is applied to full NAIP images covering a 4 mile by 4 mile area, approximately. To perform detection over this large area, a sliding window approach is used to analyze 300 x 300 pixel images over the 5978 x 7648 pixel original image. Once this analysis is performed, a marker is plotted on the original NAIP image for each detected wind turbine. In addition, the latitude and longitude of each wind turbine is output for verification. Two NAIP images with all the detected wind turbines denoted with red markers are presented below. In addition, a table containing a subset of the wind turbine latitude and longitude coordinates is shown below. The accuracy of the model is high however there are instances where non-wind turbine objects such as houses, barns, or roads are detected and classified as wind turbines. 
 
 ![](/READMEimages/NAIP_01.png) ![](/READMEimages/NAIP_02.png)