--- license: apache-2.0 dataset_info: - config_name: raw_data features: - name: image dtype: image - name: label dtype: class_label: names: '0': Aedes_atropalpus '1': Aedes_canadensis '2': Aedes_caspius '3': Aedes_cinereus '4': Aedes_communis '5': Aedes_cretinus '6': Aedes_excrucians '7': Aedes_flavescens '8': Aedes_geniculatus '9': Aedes_pulcritarsis '10': Aedes_punctor '11': Aedes_sticticus '12': Aedes_togoi '13': Aedes_triseriatus '14': Anopheles_claviger '15': Anopheles_plumbeus '16': Coquillettidia_richiardii '17': Culiseta_alaskaensis '18': Culiseta_annulata '19': Culiseta_longiareolata '20': Culiseta_morsitans splits: - name: train num_bytes: 1420154012.504 num_examples: 2712 download_size: 1604079364 dataset_size: 1420154012.504 - config_name: raw_data_diversity features: - name: image dtype: image - name: label dtype: class_label: names: '0': Aedes_annulipes '1': Aedes_atropalpus '2': Aedes_canadensis '3': Aedes_cantans '4': Aedes_caspius '5': Aedes_cataphylla '6': Aedes_cinereus '7': Aedes_communis '8': Aedes_cretinus '9': Aedes_diantaeus '10': Aedes_excrucians '11': Aedes_flavescens '12': Aedes_geniculatus '13': Aedes_hexodontus '14': Aedes_intrudens '15': Aedes_pulcritarsis '16': Aedes_pullatus '17': Aedes_punctor '18': Aedes_riparius '19': Aedes_rossicus '20': Aedes_scutellaris '21': Aedes_sticticus '22': Aedes_togoi '23': Aedes_triseriatus '24': Anopheles_algeriensis '25': Anopheles_claviger '26': Anopheles_hyrcanus '27': Anopheles_maculipennis '28': Anopheles_messeae '29': Anopheles_plumbeus '30': Anopheles_pulcherrimus '31': Anopheles_superpictus '32': Coquillettidia_richiardii '33': Culex_apicalis '34': Culex_cinereus '35': Culex_modestus '36': Culex_theileri '37': Culex_torrentium '38': Culex_vagans '39': Culiseta_alaskaensis '40': Culiseta_annulata '41': Culiseta_fumipennis '42': Culiseta_longiareolata '43': Culiseta_morsitans '44': Uranotaenia_unguiculata splits: - name: train num_bytes: 1626526244.873 num_examples: 2911 download_size: 1739817419 dataset_size: 1626526244.873 - config_name: raw_data_full features: - name: image dtype: image - name: label dtype: class_label: names: '0': Aedes_annulipes '1': Aedes_atropalpus '2': Aedes_canadensis '3': Aedes_cantans '4': Aedes_caspius '5': Aedes_cinereus '6': Aedes_communis '7': Aedes_cretinus '8': Aedes_excrucians '9': Aedes_flavescens '10': Aedes_geniculatus '11': Aedes_pulcritarsis '12': Aedes_pullatus '13': Aedes_punctor '14': Aedes_sticticus '15': Aedes_togoi '16': Aedes_triseriatus '17': Anopheles_claviger '18': Anopheles_hyrcanus '19': Anopheles_maculipennis '20': Anopheles_plumbeus '21': Coquillettidia_richiardii '22': Culex_modestus '23': Culex_theileri '24': Culex_torrentium '25': Culiseta_alaskaensis '26': Culiseta_annulata '27': Culiseta_longiareolata '28': Culiseta_morsitans splits: - name: train num_bytes: 1593764367.752 num_examples: 2848 download_size: 1711743772 dataset_size: 1593764367.752 configs: - config_name: raw_data data_files: - split: train path: raw_data/train-* - config_name: raw_data_diversity data_files: - split: train path: raw_data_diversity/train-* - config_name: raw_data_full data_files: - split: train path: raw_data_full/train-* --- # 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](https://huggingface.co/datasets/iloncka/mosquito_dataset_46_3139) under CC-BY-SA-4.0 license. - Specialized derivatives: [classification](https://huggingface.co/datasets/iloncka/mosquito-species-classification-dataset), [detection](https://huggingface.co/datasets/iloncka/mosquito-species-detection-dataset), and [segmentation](https://huggingface.co/datasets/iloncka/mosquito-species-segmentation-dataset) datasets under CC-BY-SA-4.0 licenses. - **Models**: - Top-1 models (see reports), used as default by `culicidaelab` library: [classification (Apache 2.0)](https://huggingface.co/iloncka/culico-net-cls-v1), [detection (AGPL-3.0)](https://huggingface.co/iloncka/culico-net-det-v1), [segmentation (Apache 2.0)](https://huggingface.co/iloncka/culico-net-segm-v1-nano) - [Top-5 classification models collection](https://huggingface.co/collections/iloncka/mosquito-classification-17-top-5-68945bf60bca2c482395efa8) with accuracy >90% for 17 mosquito species. - **Protocols**: All training parameters and metrics available at: - [Detection model reports](https://gitlab.com/mosquitoscan/experiments-reports-detection-models) - [Segmentation model reports](https://gitlab.com/mosquitoscan/experiments-reports-segmentation-models) - [Classification experiment reports - 1st round](https://gitlab.com/iloncka/mosal-reports) - [Classification experiment reports - 2nd round](https://gitlab.com/mosquitoscan/experiments-reports) - **Applications**: - [Python library (AGPL-3.0)](https://github.com/iloncka-ds/culicidaelab) providing core ML functionality - [Web server (AGPL-3.0)](https://github.com/iloncka-ds/culicidaelab-server) hosting API services - Mobile apps (AGPL-3.0): [mosquitoscan](https://gitlab.com/mosquitoscan/mosquitoscan-app) for independent use with optimized models and [culicidaelab-mobile](https://gitlab.com/iloncka-ds/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)**](https://fasie.ru/). ## Dataset Card Authors Kovaleva Ilona ## Dataset Card Contact iloncka.ds@gmail.com