Contained 19480 Hand curated, close-up photos of bees, wasps and other insects.
The challenge is primarily to distinguish bees from wasps.
example other insect:
example other non-insect:
we have: bees..........: 3183 wasps.........: 4943 other insects.: 2453 other.........: 845 in that, there is: training photos : 7942 hyperparameter tuning (1st level validation) photos : 1719 final validation (brag about your result with these) photos : 1763 In the final validation, there is 504 bees and 753 wasps, meaning that the resolution of the result is 0.08%
This image dataset collates and refines upon several sources:
The photos have been hand-curated by our expert biologist , Callum Robertson https://www.linkedin.com/in/callum-robertson-358014109/ Collator and Kaggle competitor: George Rey
id,path,is_bee,is_wasp,is_otherinsect,is_other,photo_quality,is_validation,is_final_validation 1,bee1\10007154554_026417cfd0_n.jpg,1,0,0,0,1,0,0 2,bee1\10024864894_6dc54d4b34_n.jpg,1,0,0,0,1,0,1 3,bee1\10092043833_7306dfd1f0_n.jpg,1,0,0,0,1,1,0 6842,wasp2\I00101.jpg,0,1,0,0,0,0,0 6843,wasp2\I00102.jpg,0,1,0,0,0,0,0 6844,wasp2\I00103.jpg,0,1,0,0,0,1,0
id- ordinal - unique index
path- string - relative path to the photo, including extension
is_bee- nominal - 1 if there is a bee in the photo
is_wasp- nominal - 1 if there is a wasp in the photo
is_otherinsect- nominal - 1 if there is other insect prominently in the centre of the photo, but it is not a wasp and not a bee. It might be a fly, but there are other things there too, like beetles
is_other- random photos not containing any insects
photo_quality- 1 for photos where I have very high confidence that it is bee, wasp, or other. 0 for photos of generally low quality or where I am not very confident that it is what it says it is. You can use this to initially reduce the size of the training set
is_validation- you can use this for your training validation, or you can combine these with the training data and split your training/validation differently
is_final_validation- do NOT use these photos for training - use them to compute your final score. This will enable comparing results by different kagglers. Optionally, if you want to deploy an app to actually serve the model, you can then use these for final training too.