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CulicidaeLab: A Powerful Toolkit for Mosquito Image Analysis

CulicidaeLab is a powerful and flexible Python library designed to provide an end-to-end solution for analyzing mosquito images. Whether you are a biologist, an epidemiologist, or a data scientist, this library provides the tools you need to detect, classify, and segment mosquitoes with state-of-the-art models.

Built on a configuration-driven architecture, CulicidaeLab simplifies complex deep learning pipelines, making them accessible and reproducible for researchers and developers alike.

Key Features

Feature Description
🧠 Pre-trained Models Get started immediately with high-accuracy models for detection, classification, and segmentation. No training required.
⚙️ Configuration-Driven Manage all aspects of the library—from file paths to model parameters—through simple and clear YAML files.
📊 Built-in Evaluation Use integrated tools to assess model performance with standard metrics like Average Precision and IoU.
🧩 Extensible & Modular The library is designed with modularity in mind, allowing you to easily add your own models or data providers.

Practical Applications of the culicidaelab Library

  • Automation in Scientific Laboratories:

    • Bulk Data Processing: Automatically analyzing thousands of images from camera traps or microscopes to assess mosquito populations without manual labor.
    • Reproducibility of Research: Standardizing the data analysis process, which allows other scientists to easily reproduce and verify research results published using this library.

  • Integration into Governmental and Commercial Systems:

    • Building Monitoring Systems: Using the library as the core "engine" for national or regional epidemiological surveillance systems.
    • Developing Custom Solutions: Rapidly prototyping and creating specialized software products for disinsection services, agro-industrial companies, or environmental organizations.

  • Analytics and Data Science:

    • Writing scripts for in-depth data analysis, building distribution maps, and forecasting disease outbreaks based on the presence of vectors.

Found an Issue or Have an Idea?

This project is open-source and thrives on community feedback. If you encounter a bug or have a suggestion for a new feature, please open an issue on GitHub. We'd love to hear from you!