YOLOv11多类别野生动物识别目标检测数据集
📊 数据集基本信息
- 目标类别： [‘antelope’, ‘badger’, ‘bat’, ‘bear’, ‘bee’, ‘beetle’, ‘bison’, ‘boar’, ‘butterfly’, ‘cat’, ‘caterpillar’, ‘chimpanzee’, ‘cockroach’, ‘cow’, ‘coyote’, ‘crab’, ‘cranefly’, ‘crow’, ‘deer’, ‘dog’, ‘dolphin’, ‘donkey’, ‘dragonfly’, ‘duck’, ‘eagle’, ‘elephant’, ‘flamingo’, ‘fly’, ‘fox’, ‘goat’, ‘goldfish’, ‘goose’, ‘gorilla’, ‘grasshopper’, ‘hamster’, ‘hare’, ‘hedgehog’, ‘hippopotamus’, ‘hornbill’, ‘horse’, ‘hummingbird’, ‘hyena’, ‘jellyfish’, ‘kangaroo’, ‘koala’, ‘ladybug’, ‘leopard’, ‘lion’, ‘lizard’, ‘lobster’, ‘mosquito’, ‘moth’, ‘mouse’, ‘octopus’, ‘okapi’, ‘orangutan’, ‘otter’, ‘owl’, ‘ox’, ‘oyster’, ‘panda’, ‘parrot’, ‘pelecaniformes’, ‘penguin’, ‘pig’, ‘pigeon’, ‘porcupine’, ‘possum’, ‘raccoon’, ‘rat’, ‘reindeer’, ‘rhinoceros’, ‘sandpiper’, ‘seahorse’, ‘seal’, ‘shark’, ‘sheep’, ‘snake’, ‘sparrow’, ‘squid’, ‘squirrel’, ‘starfish’, ‘swan’, ‘tiger’, ‘turkey’, ‘turtle’, ‘whale’, ‘wolf’, ‘wombat’, ‘woodpecker’, ‘zebra’]
- 中文类别：[‘羚羊’, ‘獾’, ‘蝙蝠’, ‘熊’, ‘蜜蜂’, ‘甲虫’, ‘野牛’, ‘野猪’, ‘蝴蝶’, ‘猫’, ‘毛毛虫’, ‘黑猩猩’, ‘蟑螂’, ‘牛’, ‘郊狼’, ‘螃蟹’, ‘蚊科昆虫’, ‘乌鸦’, ‘鹿’, ‘狗’, ‘海豚’, ‘驴’, ‘蜻蜓’, ‘鸭子’, ‘鹰’, ‘大象’, ‘火烈鸟’, ‘苍蝇’, ‘狐狸’, ‘山羊’, ‘金鱼’, ‘鹅’, ‘大猩猩’, ‘蚱蜢’, ‘仓鼠’, ‘野兔’, ‘刺猬’, ‘河马’, ‘犀鸟’, ‘马’, ‘蜂鸟’, ‘鬣狗’, ‘水母’, ‘袋鼠’, ‘考拉’, ‘瓢虫’, ‘豹’, ‘狮子’, ‘蜥蜴’, ‘龙虾’, ‘蚊子’, ‘飞蛾’, ‘老鼠’, ‘章鱼’, ‘㺢㹢狓’, ‘猩猩’, ‘水獭’, ‘猫头鹰’, ‘牛’, ‘牡蛎’, ‘熊猫’, ‘鹦鹉’, ‘鹈形目鸟类’, ‘企鹅’, ‘猪’, ‘鸽子’, ‘豪猪’, ‘负鼠’, ‘浣熊’, ‘老鼠’, ‘驯鹿’, ‘犀牛’, ‘鹬鸟’, ‘海马’, ‘海豹’, ‘鲨鱼’, ‘羊’, ‘蛇’, ‘麻雀’, ‘鱿鱼’, ‘松鼠’, ‘海星’, ‘天鹅’, ‘老虎’, ‘火鸡’, ‘乌龟’, ‘鲸鱼’, ‘狼’, ‘袋熊’, ‘啄木鸟’, ‘斑马’]
- 训练集：3779 张
- 验证集：1080 张
- 测试集：540 张
- 总计：5399 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:91names:['antelope','badger','bat','bear','bee','beetle','bison','boar','butterfly','cat','caterpillar','chimpanzee','cockroach','cow','coyote','crab','cranefly','crow','deer','dog','dolphin','donkey','dragonfly','duck','eagle','elephant','flamingo','fly','fox','goat','goldfish','goose','gorilla','grasshopper','hamster','hare','hedgehog','hippopotamus','hornbill','horse','hummingbird','hyena','jellyfish','kangaroo','koala','ladybug','leopard','lion','lizard','lobster','mosquito','moth','mouse','octopus','okapi','orangutan','otter','owl','ox','oyster','panda','parrot','pelecaniformes','penguin','pig','pigeon','porcupine','possum','raccoon','rat','reindeer','rhinoceros','sandpiper','seahorse','seal','shark','sheep','snake','sparrow','squid','squirrel','starfish','swan','tiger','turkey','turtle','whale','wolf','wombat','woodpecker','zebra']🖼️ 标注可视化
📝 数据集分析
该数据集聚焦于多种陆生与水生野生动物的精准识别，涵盖哺乳类、鸟类、爬行类、昆虫类等广泛生物类别，图像采集场景多样，包括自然栖息地、动物园环境及特写镜头，充分体现了真实生态条件下的视觉多样性。数据集通过高精度标注实现了对不同体型、姿态和背景复杂度动物的有效区分，为野生动物监测、生态保护研究以及生物多样性分析提供了高质量的视觉基础支持。
该数据集在训练集、验证集与测试集之间实现了科学合理的划分，其中训练集包含3779张图像，验证集1080张，测试集540张，总计5399张。这种分布结构确保了模型在充分学习的同时具备良好的泛化能力，能够有效应对未见过的样本，满足深度学习模型训练与评估的标准化需求，保障了实验结果的稳定性和可重复性。
该数据集的标注工作严谨规范，所有目标均采用精确边界框进行标注，覆盖了各类动物在不同光照、角度和遮挡情况下的形态特征。标注框紧贴目标轮廓，无明显偏移或遗漏，尤其在多目标重叠或小目标识别场景中仍保持高度一致性，体现出专业的标注流程与严格的质量控制标准，为后续模型训练提供了可靠的数据支撑。
该数据集可广泛应用于野生动物保护、自然保护区监控、生态学研究以及生物教育等领域。其丰富的物种覆盖和多样化的拍摄环境使其适用于构建智能巡检系统、自动物种分类平台以及濒危动物识别工具，助力科研机构与环保组织实现高效、非侵入式的生物监测，推动人与自然和谐共处的可持续发展目标。
