We just made the following updates to the competition dataset:
But there is still the question on different markup (field location) for single image:
There are 4 labeled fields with the following IDs, but all of them lacated on the same image, we can build models that would be able to detect all of these 4 fields - it is not the problem.
But here we have 2 more unlabled IDs (fields) test=True in table above, and they are also lacated on the same image, and this is not so good, and makes possable solution not so good as can be expected, but anyway it is still solvable.
Real problems begins when we need to prepare predictions for fields located on the same image (here we have 2 IDs (fields), but there are samples with even more):
As you can see above for both test IDs we have same image and as a result we will have the same prediciton (predictions on the image just for demonstration purpose and does point to exact location of fields), and there is 2 ways (in case of 2 fields) of assigning fields to each ID - and its totaly random choise. And this can not be solved by droping duplicated, as in such case we still have several GT fields on the same image, and need to eandomly choose one for prediction.
This makes task unsolvable (at least for images where we have 2 or more fields located on the same image)
Data samples that have the same images but different metadata like field size and yield are considered different since they represent different fields on the ground. I will make a post soon to the describe our annotation process to give you a better idea how important is the field size espicially to the problem.
Thanks for update.
Just to confirm (based on how you treat images with 112 bands in the new started notebook) - the *last* 16 bands represent the *most recent* month's spectral images?
Good news, one issue, starter Notebook is inassesible, it doesn't download
Press ctrl + s after clicking on the notebook link.