@article{fdi:010088932,
  title = {{A}n annotated wing interferential pattern dataset of dipteran insects of medical interest for deep learning [{D}ata paper]},
  author = {{C}annet, {A}. and {S}imon-chane, {C}. and {H}istace, {A}. and {A}khoundi, {M}. and {R}omain, {O}. and {S}ouchaud, {M}. and {J}acob, {P}. and {S}ereno, {D}. and {B}ouss{\`e}s, {P}hilippe and {S}ereno, {D}enis},
  editor = {},
  language = {{ENG}},
  abstract = {{S}everal {D}iptera species are known to transmit pathogens of medical and veterinary interest. {H}owever, identifying these species using conventional methods can be time-consuming, labor-intensive, or expensive. {A} computer vision-based system that uses {W}ing interferential patterns ({WIP}s) to identify these insects could solve this problem. {T}his study introduces a dataset for training and evaluating a recognition system for dipteran insects of medical and veterinary importance using {WIP}s. {T}he dataset includes pictures of {C}ulicidae, {C}alliphoridae, {M}uscidae, {T}abanidae, {C}eratopogonidae, and {P}sychodidae. {T}he dataset is complemented by previously published datasets of {G}lossinidae and some {C}ulicidae members. {T}he new dataset contains 2,399 pictures of 18 genera, with each genus documented by a variable number of species and annotated as a class. {T}he dataset covers species variation, with some genera having up to 300 samples.},
  keywords = {},
  booktitle = {},
  journal = {{S}cientific {D}ata},
  volume = {11},
  numero = {1},
  pages = {4 [6 p.]},
  year = {2024},
  DOI = {10.1038/s41597-023-02848-y},
  URL = {https://www.documentation.ird.fr/hor/fdi:010088932},
}