This github repo is used for inferering or tweaking various object detection models and their performance to best suit the kaggle ship detection problem

• YOLOV3 implementation credits

Airbus is excited to challenge Kagglers to build a model that detects all ships in satellite images as quickly as possible

This competition is evaluated on the F2 Score at different intersection over union (IoU) thresholds. The IoU of a proposed set of object pixels and a set of true object pixels is calculated as:

IoU(A,B)=A∩BA∪B

The metric sweeps over a range of IoU thresholds, at each point calculating an F2 Score. The threshold values range from 0.5 to 0.95 with a step size of 0.05: (0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95). In other words, at a threshold of 0.5, a predicted object is considered a "hit" if its intersection over union with a ground truth object is greater than 0.5.

At each threshold value t, the F2 Score value is calculated based on the number of true positives (TP), false negatives (FN), and false positives (FP) resulting from comparing the predicted object to all ground truth objects. The following equation is equivalent to F2 Score when β is set to 2:

Fβ(t)=(1+β2)⋅TP(t)(1+β2)⋅TP(t)+β2⋅FN(t)+FP(t)

A true positive is counted when a single predicted object matches a ground truth object with an IoU above the threshold. A false positive indicates a predicted object had no associated ground truth object. A false negative indicates a ground truth object had no associated predicted object. The average F2 Score of a single image is then calculated as the mean of the above F2 Score values at each IoU threshold:

1|thresholds|∑tF2(t)

Lastly, the score returned by the competition metric is the mean taken over the individual average F2 Scores of each image in the test dataset.

You can make submissions directly from Kaggle Kernels. By adding your teammates as collaborators on a kernel, you can share and edit code privately with them.

Submission File In order to reduce the submission file size, our metric uses run-length encoding on the pixel values. Instead of submitting an exhaustive list of indices for your segmentation, you will submit pairs of values that contain a start position and a run length. E.g. '1 3' implies starting at pixel 1 and running a total of 3 pixels (1,2,3).

The competition format requires a space delimited list of pairs. For example, '1 3 10 5' implies pixels 1,2,3,10,11,12,13,14 are to be included in the mask. The pixels are one-indexed and numbered from top to bottom, then left to right: 1 is pixel (1,1), 2 is pixel (2,1), etc. A prediction of of "no ship in image" should have a blank value in the EncodedPixels column.

The metric checks that the pairs are sorted, positive, and the decoded pixel values are not duplicated. It also checks that no two predicted masks for the same image are overlapping.

The file should contain a header and have the following format. Each row in your submission represents a single predicted ship segmentation for the given image.