Wildlife and Livestock Dataset with Emphasis on Predators for Vision-Based Herding Systems

This dataset supports real-time detection of herding-related entities including sheep, wolves, dogs, bears, coyotes, foxes, red foxes, wild dogs, and humans for autonomous robotic shepherds. It has been reformatted from raw YOLOv5-style datasets using a custom pipeline that ensures class balancing, strong augmentations via Albumentations, and source diversity based on video origin. It combines three sources: a sheep dataset (Sheep_dataset_link), the COCO dataset (Coco_dataset_link) for person and dog classes, and predator datasets (predator_dataset_link). Rare classes (e.g., wolf, cow, dog, wild dog) have been augmented using a recycling strategy that combines oversampling with high-variance augmentations to increase robustness (Python_Code). Splits (train/validation/test) are carefully balanced to maintain class distribution and diversity. The dataset is compatible with YOLOv5–YOLOv12, RT-DETR, and custom models such as SHEPHERD, and has been validated in real-time on ROS 2 (Humble) deployed on quadruped robots (e.g., Unitree Go1) for flock monitoring, predator deterrence, and shepherd alerting. The archive contains images, YOLO-format labels, metadata CSVs, and summary plots for class balance and entropy, providing a ready-to-use resource for object detection, robotics, and intelligent livestock management.本数据集支持面向自主机器牧羊系统的放牧相关实体实时检测，涵盖绵羊、狼、犬、熊、郊狼、狐狸、赤狐、野犬以及人类。本数据集基于自定义处理流水线，从原始YOLOv5风格数据集重构而来，可确保类别平衡、通过Albumentations实现高质量数据增强，并基于视频来源保证数据源多样性。 数据集整合了三类数据源：绵羊数据集（Sheep_dataset_link）、用于人物与犬类类别的COCO数据集（Coco_dataset_link），以及捕食者数据集（predator_dataset_link）。针对狼、牛、犬、野犬等稀有类别，本数据集采用结合过采样与高方差数据增强的循环增强策略进行扩充，以提升模型鲁棒性（Python_Code）。 训练集、验证集与测试集的划分经过精心平衡，以维持类别分布与数据源多样性。本数据集兼容YOLOv5至YOLOv12、RT-DETR以及SHEPHERD等自定义模型，并已在部署于四足机器人（如Unitree Go1）的ROS 2（Humble）系统上完成实时验证，可用于羊群监测、捕食者驱离与牧羊系统告警。 该压缩包包含图像文件、YOLO格式标注文件、元数据CSV文件以及用于展示类别平衡与信息熵的统计图表，可为目标检测、机器人技术与智能畜牧管理领域提供可直接使用的数据集资源。