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marine-vessel-detection-yolov8

ultralytics
satellite imagery
marine traffic
sentinel-2
yolov8
Eval Results
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@@ -140,11 +140,11 @@ Precision and Recall with IoU-threshold of 0.5, mAP50 and mAP.
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  | 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') |
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  |:--------|-----------------------:|------------------------:|-----------------------:|--------------------:|---------------------:|--------------------:|-----------------:|------------------:|-----------------:|-------------------:|--------------------:|-------------------:|
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- | yolov8n | 0.81445 | 0.795968 | 0.76041 | 0.78213 | 0.769462 | 0.76529 | 0.38257 | 0.361076 | 0.32913 | 0.8342 | 0.804218 | 0.77208 |
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- | yolov8s | 0.82827 | 0.81588 | 0.80375 | 0.81265 | 0.801532 | 0.78297 | 0.39178 | 0.384716 | 0.37944 | 0.84059 | 0.83342 | 0.82414 |
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- | yolov8m | 0.83499 | 0.82092 | 0.79623 | 0.83049 | 0.811248 | 0.79513 | 0.4072 | 0.394016 | 0.38463 | 0.86221 | 0.839618 | 0.81783 |
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- | yolov8l | 0.83616 | 0.81565 | 0.75919 | 0.82047 | 0.793198 | 0.77523 | 0.40176 | 0.3768 | 0.3372 | 0.85488 | 0.819246 | 0.7434 |
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- | yolov8x | 0.84264 | 0.830032 | 0.81037 | 0.82699 | 0.818172 | 0.80858 | 0.40231 | 0.399892 | 0.39691 | 0.85337 | 0.850808 | 0.84832 |
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  Models with best performance for test set for each model architecture:
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  | 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') |
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  |:--------|-----------------------:|------------------------:|-----------------------:|--------------------:|---------------------:|--------------------:|-----------------:|------------------:|-----------------:|-------------------:|--------------------:|-------------------:|
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+ | yolov8n | 0.84688 | 0.82316 | 0.81273 | 0.79587 | 0.783928 | 0.76895 | 0.38816 | 0.380828 | 0.37637 | 0.84323 | 0.827192 | 0.81776 |
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+ | yolov8s | 0.86363 | 0.834556 | 0.80835 | 0.83639 | 0.825554 | 0.81469 | 0.41098 | 0.406258 | 0.40374 | 0.86933 | 0.858632 | 0.84776 |
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+ | yolov8m | 0.83592 | 0.827938 | 0.81134 | 0.84939 | 0.83238 | 0.80153 | 0.41739 | 0.410742 | 0.40496 | 0.87071 | 0.860378 | 0.84545 |
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+ | yolov8l | 0.8545 | 0.846998 | 0.83973 | 0.84975 | 0.83072 | 0.82398 | 0.41694 | 0.411712 | 0.40505 | 0.87459 | 0.863334 | 0.85743 |
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+ | yolov8x | 0.84028 | 0.834728 | 0.83019 | 0.84891 | 0.828462 | 0.80858 | 0.42065 | 0.411532 | 0.40231 | 0.86203 | 0.858136 | 0.85131 |
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  Models with best performance for test set for each model architecture:
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