The provided dataset includes two directories "training" (contains 15,000 training images) and "testing" (contains 6,001 test images) and two files named "train.csv" (containing training annotations) and partial.csv (partially pre-filled attributes for submission.). "Partial.csv" contains 8 columns in the follow order:

filename
width
height
class
xmin
ymin
xmax
ymax

The final output should be:

Single file "output.csv" containing 9 attributes: filename (name of images), width (width of image), height (height of image), class, xmin, ymin, xmax, ymax (bounding box co-ordinates) and conf (confidence of prediction).

The order of the attributes must be kept exactly the same, refer to partial.csv for help.

Please note carefully: Each row MUST contain details of single object only. Hence in case of multiple objects in an image they have to be inserted as each row separately with same filename.

A sample screenshot of the "output.csv" is given below for reference.

.....

Quick Tip: You may use partial.csv and insert prediction values within it, as it contains pre-filled values for filename, height and width attributes. Remember to create additional column for confidence ("conf").

How to make a submission?

Click on "My Submission"
On the next page, click on "+ New Submission"
Upload your CSV in the next page and click on "Submit for Review"

Please note:

You must submit your CSV file by uploading the CSV in the "My Submissions" section of this challenge.
Your submission will be auto graded and you will be able to see your results instantly.
If there is any error in the submission, your final score will be marked as 0.

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