YOLOv11野生动物园大型猫科动物目标检测数据集-8075张-Animal-detection-yolov8-1

YOLOv11野生动物园大型猫科动物目标检测数据集 数据集基本信息目标类别 [‘Bird’, ‘Cat’, ‘Cross-Cattle’, ‘Dog’, ‘Elephant’, ‘Houseplant’, ‘Human-beard’, ‘Human-ear’, ‘Human-eye’, ‘Human-face’, ‘Human-hand’, ‘Human-head’, ‘Human-leg’, ‘Human-mouth’, ‘Human-nose’, ‘Insect’, ‘Jeans’, ‘Land-vehicle’, ‘Lemon’, ‘Loveseat’, ‘Mammal’, ‘Man’, ‘Marine-mammal’, ‘Motorcycle’, ‘nail’, ‘Paddle’, ‘Pen’, ‘Person’, ‘Picture-frame’, ‘Pig’, ‘Pillow’, ‘Plant’, ‘Plate’, ‘Platter’, ‘Poster’, ‘Printer’, ‘Rabbit’, ‘Sculpture’, ‘Seat-belt’, ‘Shelf’, ‘Sink’, ‘Stairs’, ‘Swimming-pool’, ‘Swimwear’, ‘Tap’, ‘Teddy-bear’, ‘Tennis-ball’, ‘Tikus’, ‘Tire’, ‘Toy’, ‘Tree’, ‘Vehicle’, ‘bunny’, ‘cattle’, ‘leopard’, ‘lion’, ‘rat’, ‘tiger’]中文类别[‘鸟’, ‘猫’, ‘牛’, ‘狗’, ‘大象’, ‘室内植物’, ‘胡须’, ‘耳朵’, ‘眼睛’, ‘人脸’, ‘手’, ‘头部’, ‘腿’, ‘嘴’, ‘鼻子’, ‘昆虫’, ‘牛仔裤’, ‘陆地车辆’, ‘柠檬’, ‘双人沙发’, ‘哺乳动物’, ‘男性’, ‘海洋哺乳动物’, ‘摩托车’, ‘指甲’, ‘桨’, ‘钢笔’, ‘人’, ‘相框’, ‘猪’, ‘枕头’, ‘植物’, ‘盘子’, ‘托盘’, ‘海报’, ‘打印机’, ‘兔子’, ‘雕塑’, ‘安全带’, ‘架子’, ‘水槽’, ‘楼梯’, ‘游泳池’, ‘泳装’, ‘水龙头’, ‘泰迪熊’, ‘网球’, ‘老鼠’, ‘轮胎’, ‘玩具’, ‘树’, ‘车辆’, ‘兔子’, ‘牛’, ‘豹’, ‘狮子’, ‘老鼠’, ‘老虎’]训练集6951 张验证集692 张测试集432 张总计8075 张 data.yaml 配置信息该数据集提供了data.yaml文件内容如下train:../train/imagesval:../valid/imagestest:../test/imagesnc:58names:[Bird,Cat,Cross-Cattle,Dog,Elephant,Houseplant,Human-beard,Human-ear,Human-eye,Human-face,Human-hand,Human-head,Human-leg,Human-mouth,Human-nose,Insect,Jeans,Land-vehicle,Lemon,Loveseat,Mammal,Man,Marine-mammal,Motorcycle,nail,Paddle,Pen,Person,Picture-frame,Pig,Pillow,Plant,Plate,Platter,Poster,Printer,Rabbit,Sculpture,Seat-belt,Shelf,Sink,Stairs,Swimming-pool,Swimwear,Tap,Teddy-bear,Tennis-ball,Tikus,Tire,Toy,Tree,Vehicle,bunny,cattle,leopard,lion,rat,tiger]️ 标注可视化 数据集分析该数据集聚焦于野生动物园场景下的大型猫科动物识别涵盖老虎、狮子、豹等典型物种结合自然栖息环境与人工饲养环境的多角度拍摄具备高度真实性和多样性。图像内容清晰呈现动物形态特征与行为状态为复杂背景下的目标检测任务提供了高质量视觉样本具有显著的生态监测与动物保护应用价值。该数据集在训练、验证与测试集之间实现了科学合理的分布训练集包含6951张图像验证集692张测试集432张总量达8075张确保了模型训练的充分性与评估的可靠性。各阶段数据比例协调能够有效支持模型迭代优化与性能验证满足深度学习任务对数据规模与结构的严格要求。标注工作严格按照规范执行所有目标均通过精确边界框进行定位标注覆盖全面且边界清晰尤其在动物面部细节、身体轮廓及遮挡情况处理上表现出高水准的专业性。标注一致性良好未发现明显错标或漏标现象整体标注质量达到行业领先水平为后续算法训练提供坚实基础。该数据集可广泛应用于动物园智能监控、野生动物保护、濒危物种识别等领域支持自动化动物行为分析、种群数量统计与异常活动预警等实际需求。其丰富的物种多样性与真实场景覆盖能力也为科研机构、环保组织及智慧园区建设提供了可靠的数据支撑推动生态保护与智能化管理深度融合。保组织及智慧园区建设提供了可靠的数据支撑推动生态保护与智能化管理深度融合。数据集下载参考小郭AI日志 https://mp.weixin.qq.com/s/7H8uEjxlIYnWA4uOdGyHpA?payreadticketHHYehCnhWslEeKolx_w__Pz9JF1fIYcQux0KLxGFu2R7CxAeyBjULVDDtsYzxUNkfqXvyUc