Bark-101 is a challenging dataset made of 101 bark classes. It has been designed on top of the LifeCLEF challenge to evaluate both texture and bark recognition algorithms in the wild. Bark-101 answers the lack of extended and segmented dataset in the context of bark recognition.
If you find Bark-101 useful in your research, please, cite us :
Rémi Ratajczak, Sarah Bertrand, Carlos Crispim-Junior, Laure Tougne. Efficient Bark Recognition in the Wild. International Conference on Computer Vision Theory and Applications (VISAPP 2019)