mayrajeo
/

marine-vessel-detection-yolov8

@@ -125,7 +125,7 @@ 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 |
 |:--------|-----------------------:|------------------------:|-----------------------:|--------------------:|---------------------:|--------------------:|-----------------:|------------------:|-----------------:|-------------------:|--------------------:|-------------------:|
 | 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 |
@@ -133,16 +133,15 @@ 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 |
-Test set results for each model type:
-| 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 |               0.773252 |                0.755268 |               0.738894 |            0.833611 |             0.821598 |            0.811092 |            0.29  |            0.2844 |            0.277 |              0.766 |              0.7466 |              0.732 |
-| yolov8s |               0.792886 |                0.779485 |               0.768628 |            0.845133 |             0.832765 |            0.825042 |            0.311 |            0.3054 |            0.298 |              0.784 |              0.7756 |              0.764 |
-| yolov8m |               0.825823 |                0.792868 |               0.772218 |            0.857435 |             0.839866 |            0.807621 |            0.324 |            0.318  |            0.314 |              0.801 |              0.7808 |              0.769 |
-| yolov8l |               0.801311 |                0.789472 |               0.772199 |            0.851569 |             0.844953 |            0.835289 |            0.326 |            0.323  |            0.317 |              0.797 |              0.7854 |              0.776 |
-| yolov8x |               0.801969 |                0.784667 |               0.773465 |            0.845454 |             0.832825 |            0.799519 |            0.322 |            0.3154 |            0.304 |              0.788 |              0.7776 |              0.755 |
 ### Compute Infrastructure

 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 |
 |:--------|-----------------------:|------------------------:|-----------------------:|--------------------:|---------------------:|--------------------:|-----------------:|------------------:|-----------------:|-------------------:|--------------------:|-------------------:|
 | 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 |
 | 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 |
+|:------|------|-----------|--------|-------|-----|
+|yolov8n|     1|     0.7639|0.833611|  0.766|0.290|
+|yolov8s|     4|   0.776751|0.845133|  0.784|0.304|
+|yolov8m|     4|   0.790741|0.857435|  0.801|0.324|
+|yolov8l|     1|   0.772199|0.851569|  0.797|0.326|
+|yolov8x|     1|   0.780507|0.838783|  0.788|0.319|
 ### Compute Infrastructure