iloncka/mosquito_data_raw

Dataset Card for Mosquito dataset

This dataset contain mosquito species images collected from scientific open data repositories.

Dataset Structure

Uranotaenia_unguiculata: 8 images
Aedes_intrudens: 5 images
Culex_vagans: 3 images
Anopheles_superpictus: 1 images
Anopheles_claviger: 35 images
Anopheles_maculipennis: 17 images
Aedes_cataphylla: 1 images
Aedes_hexodontus: 2 images
Anopheles_plumbeus: 62 images
Aedes_annulipes: 24 images
Culex_torrentium: 12 images
Aedes_canadensis: 324 images
Aedes_atropalpus: 35 images
Culex_modestus: 19 images
Aedes_geniculatus: 122 images
Aedes_rossicus: 2 images
Culiseta_annulata: 597 images
Anopheles_algeriensis: 6 images
Aedes_cantans: 28 images
Culex_cinereus: 1 images
Culiseta_morsitans: 39 images
Culiseta_longiareolata: 209 images
Aedes_cretinus: 109 images
Aedes_scutellaris: 7 images
Aedes_triseriatus: 478 images
Aedes_punctor: 37 images
Aedes_pullatus: 13 images
Aedes_flavescens: 45 images
Aedes_togoi: 30 images
Culiseta_alaskaensis: 43 images
Aedes_cinereus: 35 images
Aedes_caspius: 153 images
Aedes_communis: 113 images
Culex_apicalis: 2 images
Culiseta_fumipennis: 5 images
Aedes_pulcritarsis: 77 images
Culex_theileri: 11 images
Anopheles_pulcherrimus: 8 images
Aedes_diantaeus: 2 images
Aedes_sticticus: 61 images
Aedes_riparius: 9 images
Coquillettidia_richiardii: 74 images
Anopheles_messeae: 1 images
Aedes_excrucians: 34 images
Anopheles_hyrcanus: 12 images

Total: 45 mosquito species on 2911 images

This dataset is part of the Culicidaelab project - open-source system for mosquito research and analysis, which includes components:

• Data:

  • Base diversity dataset (46 species, 3139 images under CC-BY-SA-4.0 license.
  • Specialized derivatives: classification, detection, and segmentation datasets under CC-BY-SA-4.0 licenses.

• Models:

  • Top-1 models (see reports), used as default by culicidaelab library: classification (Apache 2.0), detection (AGPL-3.0), segmentation (Apache 2.0)
  • Top-5 classification models collection with accuracy >90% for 17 mosquito species.

• Protocols:

  All training parameters and metrics available at:

  • Detection model reports
  • Segmentation model reports
  • Classification experiment reports - 1st round
  • Classification experiment reports - 2nd round

• Applications:

  • Python library (AGPL-3.0) providing core ML functionality
  • Web server (AGPL-3.0) hosting API services
  • Mobile apps (AGPL-3.0): mosquitoscan for independent use with optimized models and culicidaelab-mobile for educational and research purposes as part of the CulicidaeLab Ecosystem.

These components form a cohesive ecosystem where datasets used for training models that power applications, the Python library provides core functionality to the web server, and the server exposes services consumed by the mobile application. All components are openly licensed, promoting transparency and collaboration.

This integrated approach enables comprehensive mosquito research, from data collection to analysis and visualization, supporting both scientific research and public health initiatives.

Practical Applications of the Dataset

• Scientific Research and Development:

  • Training New Models: Using the datasets to train more accurate or faster AI models tailored for specific tasks (e.g., for deployment on low-performance devices).
  • Comparative Analysis (Benchmarking): Researchers worldwide can use these datasets as a standard benchmark to compare the performance of their own detection and classification algorithms.
  • Transfer Learning: Adapting existing models to recognize mosquito species that were not included in the original dataset but are endemic to a specific region.
  • Studying Correlations: Analyzing images to identify non-obvious visual markers or relationships between species, their posture, and their environment.

• Education:

  • Educational Courses: Serving as practical material in university courses on machine learning, computer vision, and bioinformatics.
  • Training Specialists: Training future entomologists and epidemiologists to work with modern data analysis tools.

• Validation and Testing:

  • Verifying the accuracy and completeness of commercial and private insect identification systems.

License

Creative Commons Attribution Share Alike 4.0 International (CC-BY-SA-4.0)

Acknowledgments

CulicidaeLab development is supported by a grant from the Foundation for Assistance to Small Innovative Enterprises (FASIE).

Dataset Card Authors

Kovaleva Ilona

Dataset Card Contact

[email protected]