eljaimifatima/LaSBiRD

---
license: cc-by-4.0
pretty_name: Large Scale Bird Recognition Dataset
size_categories:
- 1M<n<10M
---

The dataset consists of a large collection of images of about 11,000 different species of birds, with a total of 5 million images. This dataset represents a valuable resource for researchers, conservationists, and bird enthusiasts alike, allowing for a more comprehensive understanding of the diversity and distribution of avian species around the world. The data could be used for a wide range of applications, including species identification, biodiversity monitoring, and ecological research. The sheer size of the dataset makes it a powerful tool for machine learning and computer vision algorithms, enabling the development of accurate and efficient automated bird identification systems. Overall, this dataset represents an unprecedented opportunity to advance our knowledge of birds and their role in the natural world.

Please cite this work while using it for your projects by using:

W. Rabhi, F. Eljaimi, W. Amara, Z. Charouh, A. Ezzouhri, H. Benaboud, M. Saindou, and F. Ouardi, "An Integrated Framework for Bird Recog-nition using Dynamic Machine Learning-based Classification" in IEEEInternational Symposium on Computers and Communications, 2023.

Please unzip using 7-Zip.

许可协议：CC BY 4.0
展示名称：大型鸟类识别数据集（Large Scale Bird Recognition Dataset）
规模区间：100万<样本量<1000万

本数据集收录了约11000种不同鸟类的海量图像，总图像量达500万张。本数据集为研究人员、自然保护工作者及鸟类爱好者提供了宝贵的研究资源，助力学界更全面地掌握全球鸟类物种的多样性与分布格局。该数据集可应用于诸多场景，包括物种识别、生物多样性监测以及生态学研究。其庞大规模使其成为机器学习（Machine Learning）与计算机视觉（Computer Vision）算法的强力支撑工具，能够助力开发精准高效的自动化鸟类识别系统。总体而言，本数据集为深化我们对鸟类及其在自然界中所扮演角色的认知提供了前所未有的契机。

若将本数据集用于您的研究项目，请通过以下方式引用该成果：
W. Rabhi、F. Eljaimi、W. Amara、Z. Charouh、A. Ezzouhri、H. Benaboud、M. Saindou及F. Ouardi：《基于动态机器学习分类的鸟类识别集成框架》，发表于2023年IEEE国际计算机与通信研讨会（IEEE International Symposium on Computers and Communications, 2023）

请使用7-Zip进行解压。