Update README.md

README.md

@@ -66,7 +66,7 @@ Users (both direct and downstream) should be made aware of the risks, biases and
 
 ## How to Get Started with the Model
 
-TBA
+[https://github.com/mayrajeo/ship-detection] provides examples on how to use the models.
 
 ## Training Details
 
@@ -139,7 +139,7 @@ Precision and Recall with IoU-threshold of 0.5, mAP50 and mAP.
 
 5-fold cross-validation results: 
 
-| Model   |   Precision, max |   Precision, mean |   Precision, min |   Recall, max |   Recall, mean |   Recall, min |   mAP, max |   mAP, mean |   mAP, min |   mAP50, max |   mAP50, mean |   mAP50, min |
+| Model   |   ('Precision', 'max') |   ('Precision', 'mean') |   ('Precision', 'min') |   ('Recall', 'max') |   ('Recall', 'mean') |   ('Recall', 'min') |   ('mAP', 'max') |   ('mAP', 'mean') |   ('mAP', 'min') |   ('mAP50', 'max') |   ('mAP50', 'mean') |   ('mAP50', 'min') |
 |:--------|-----------------------:|------------------------:|-----------------------:|--------------------:|---------------------:|--------------------:|-----------------:|------------------:|-----------------:|-------------------:|--------------------:|-------------------:|
 | yolov8n |                0.85001 |                0.840136 |                0.82782 |             0.82951 |             0.804012 |             0.78738 |          0.38816 |          0.380828 |          0.37637 |            0.84525 |            0.833424 |            0.81883 |
 | yolov8s |                0.86717 |                0.854216 |                0.84347 |             0.84939 |             0.84065  |             0.83222 |          0.41098 |          0.406258 |          0.40374 |            0.86933 |            0.861404 |            0.84934 |
@@ -147,6 +147,7 @@ Precision and Recall with IoU-threshold of 0.5, mAP50 and mAP.
 | yolov8l |                0.86911 |                0.863254 |                0.85604 |             0.86468 |             0.841572 |             0.82725 |          0.41694 |          0.411712 |          0.40505 |            0.88288 |            0.867134 |            0.85743 |
 | yolov8x |                0.86411 |                0.856008 |                0.85045 |             0.86086 |             0.845044 |             0.83029 |          0.42065 |          0.411532 |          0.40231 |            0.87069 |            0.863538 |            0.85316 |
 
+
 Models with best performance for test set for each model architecture:
 
 | Model | Fold | Precision | Recall | mAP50 | mAP |