ILSVRC2015_DET

displayName: ILSVRC2015 DET
labelTypes:
- Box2D
license:
- Unknown
mediaTypes:
- Image
paperUrl: ""
publishDate: "2015"
publishUrl: http://image-net.org/challenges/LSVRC/2015/
publisher:
- Carnegie Mellon University
- Stanford University
- University of Michigan
- University of North Carolina at Chapel Hill
tags:
- Human
taskTypes:
- Object Detection

---
# 数据集介绍
## 简介
训练和验证数据集与 ILSVRC2014 相同。共有 456567 张图像用于训练。每个同义词集（类别）的正图像数量范围从 461 到 67513。负图像数量范围从每个同义词集 42945 到 70626。有 20121 个验证图像。所有图像均为 JPEG 格式。通过添加 11142 个新图像来刷新测试数据集。该文件包含 51294 (40152 + 11142) 张图像和 test.txt 文件。测试新文件仅包含 11142 个新图像和 test.txt 文件。
## 类定义
```
n02672831: accordion
n02691156: airplane
n02219486: ant
n02419796: antelope
n07739125: apple
n02454379: armadillo
n07718747: artichoke
n02764044: axe
n02766320: baby bed
n02769748: backpack
n07693725: bagel
n02777292: balance beam
n07753592: banana
n02786058: band aid
n02787622: banjo
n02799071: baseball
n02802426: basketball
n02807133: bathing cap
n02815834: beaker
n02131653: bear
n02206856: bee
n07720875: bell pepper
n02828884: bench
n02834778: bicycle
n02840245: binder
n01503061: bird
n02870880: bookshelf
n02879718: bow
n02883205: bow tie
n02880940: bowl
n02892767: brassiere
n07880968: burrito
n02924116: bus
n02274259: butterfly
n02437136: camel
n02951585: can opener
n02958343: car
n02970849: cart
n02402425: cattle
n02992211: cello
n01784675: centipede
n03000684: chain saw
n03001627: chair
n03017168: chime
n03062245: cocktail shaker
n03063338: coffee maker
n03085013: computer keyboard
n03793489: computer mouse
n03109150: corkscrew
n03128519: cream
n03134739: croquet ball
n03141823: crutch
n07718472: cucumber
n03797390: cup or mug
n03188531: diaper
n03196217: digital clock
n03207941: dishwasher
n02084071: dog
n02121808: domestic cat
n02268443: dragonfly
n03249569: drum
n03255030: dumbbell
n03271574: electric fan
n02503517: elephant
n03314780: face powder
n07753113: fig
n03337140: filing cabinet
n03991062: flower pot
n03372029: flute
n02118333: fox
n03394916: french horn
n01639765: frog
n03400231: frying pan
n02510455: giant panda
n01443537: goldfish
n03445777: golf ball
n03445924: golfcart
n07583066: guacamole
n03467517: guitar
n03483316: hair dryer
n03476991: hair spray
n07697100: hamburger
n03481172: hammer
n02342885: hamster
n03494278: harmonica
n03495258: harp
n03124170: hat with a wide brim
n07714571: head cabbage
n03513137: helmet
n02398521: hippopotamus
n03535780: horizontal bar
n02374451: horse
n07697537: hotdog
n03584254: iPod
n01990800: isopod
n01910747: jellyfish
n01882714: koala bear
n03633091: ladle
n02165456: ladybug
n03636649: lamp
n03642806: laptop
n07749582: lemon
n02129165: lion
n03676483: lipstick
n01674464: lizard
n01982650: lobster
n03710721: maillot
n03720891: maraca
n03759954: microphone
n03761084: microwave
n03764736: milk can
n03770439: miniskirt
n02484322: monkey
n03790512: motorcycle
n07734744: mushroom
n03804744: nail
n03814639: neck brace
n03838899: oboe
n07747607: orange
n02444819: otter
n03908618: pencil box
n03908714: pencil sharpener
n03916031: perfume
n00007846: person
n03928116: piano
n07753275: pineapple
n03942813: ping-pong ball
n03950228: pitcher
n07873807: pizza
n03958227: plastic bag
n03961711: plate rack
n07768694: pomegranate
n07615774: popsicle
n02346627: porcupine
n03995372: power drill
n07695742: pretzel
n04004767: printer
n04019541: puck
n04023962: punching bag
n04026417: purse
n02324045: rabbit
n04039381: racket
n01495701: ray
n02509815: red panda
n04070727: refrigerator
n04074963: remote control
n04116512: rubber eraser
n04118538: rugby ball
n04118776: ruler
n04131690: salt or pepper shaker
n04141076: saxophone
n01770393: scorpion
n04154565: screwdriver
n02076196: seal
n02411705: sheep
n04228054: ski
n02445715: skunk
n01944390: snail
n01726692: snake
n04252077: snowmobile
n04252225: snowplow
n04254120: soap dispenser
n04254680: soccer ball
n04256520: sofa
n04270147: spatula
n02355227: squirrel
n02317335: starfish
n04317175: stethoscope
n04330267: stove
n04332243: strainer
n07745940: strawberry
n04336792: stretcher
n04356056: sunglasses
n04371430: swimming trunks
n02395003: swine
n04376876: syringe
n04379243: table
n04392985: tape player
n04409515: tennis ball
n01776313: tick
n04591157: tie
n02129604: tiger
n04442312: toaster
n06874185: traffic light
n04468005: train
n04487394: trombone
n03110669: trumpet
n01662784: turtle
n03211117: tv or monitor
n04509417: unicycle
n04517823: vacuum
n04536866: violin
n04540053: volleyball
n04542943: waffle iron
n04554684: washer
n04557648: water bottle
n04530566: watercraft
n02062744: whale
n04591713: wine bottle
n02391049: zebra
```
## 引文
```
@article{ILSVRC15,
Author = {Olga Russakovsky and Jia Deng and Hao Su and Jonathan Krause and Sanjeev Satheesh and Sean Ma and Zhiheng Huang and Andrej Karpathy and Aditya Khosla and Michael Bernstein and Alexander C. Berg and Li Fei-Fei},
Title = {{ImageNet Large Scale Visual Recognition Challenge}},
Year = {2015},
journal = {International Journal of Computer Vision (IJCV)},
doi = {10.1007/s11263-015-0816-y},
volume={115},
number={3},
pages={211-252}
}
```

## Download dataset
:modelscope-code[]{type="git"}

displayName: ILSVRC2015目标检测（DET）
labelTypes:
- 二维边界框（Box2D）
license:
- 未知
mediaTypes:
- 图像（Image）
paperUrl: ""
publishDate: "2015"
publishUrl: http://image-net.org/challenges/LSVRC/2015/
publisher:
- 卡内基梅隆大学
- 斯坦福大学
- 密歇根大学
- 北卡罗来纳大学教堂山分校
tags:
- 人类
taskTypes:
- 目标检测

---

# 数据集介绍
## 简介
本数据集的训练集与验证集与ILSVRC2014保持一致。训练集共包含456567张图像，每个同义词集（类别）的正样本图像数量介于461至67513之间，负样本图像数量则为每个同义词集42945至70626不等。验证集共计20121张图像，所有图像均采用JPEG格式存储。测试集通过新增11142张图像完成更新，当前数据集文件包含51294（即40152+11142）张图像及test.txt文件，其中更新后的测试文件仅包含新增的11142张图像与test.txt文件。
## 类别定义

n02672831: 手风琴
n02691156: 飞机
n02219486: 蚂蚁
n02419796: 羚羊
n07739125: 苹果
n02454379: 犰狳
n07718747: 洋蓟
n02764044: 斧头
n02766320: 婴儿床
n02769748: 背包
n07693725: 百吉饼
n02777292: 平衡木
n07753592: 香蕉
n02786058: 创可贴
n02787622: 班卓琴
n02799071: 棒球
n02802426: 篮球
n02807133: 游泳帽
n02815834: 烧杯
n02131653: 熊
n02206856: 蜜蜂
n07720875: 甜椒
n02828884: 长椅
n02834778: 自行车
n02840245: 活页夹
n01503061: 鸟类
n02870880: 书架
n02879718: 弓
n02883205: 领结
n02880940: 碗
n02892767: 胸罩
n07880968: 墨西哥卷饼
n02924116: 公共汽车
n02274259: 蝴蝶
n02437136: 骆驼
n02951585: 开罐器
n02958343: 汽车
n02970849: 手推车
n02402425: 牛
n02992211: 大提琴
n01784675: 蜈蚣
n03000684: 链锯
n03001627: 椅子
n03017168: 编钟
n03062245: 鸡尾酒调酒器
n03063338: 咖啡机
n03085013: 电脑键盘
n03793489: 电脑鼠标
n03109150: 开瓶器
n03128519: 奶油
n03134739: 槌球
n03141823: 拐杖
n07718472: 黄瓜
n03797390: 杯子或马克杯
n03188531: 尿布
n03196217: 数字时钟
n03207941: 洗碗机
n02084071: 狗
n02121808: 家猫
n02268443: 蜻蜓
n03249569: 鼓
n03255030: 哑铃
n03271574: 电风扇
n02503517: 大象
n03314780: 香粉
n07753113: 无花果
n03337140: 文件柜
n03991062: 花盆
n03372029: 长笛
n02118333: 狐狸
n03394916: 法国号
n01639765: 青蛙
n03400231: 煎锅
n02510455: 大熊猫
n01443537: 金鱼
n03445777: 高尔夫球
n03445924: 高尔夫球车
n07583066: 鳄梨酱
n03467517: 吉他
n03483316: 吹风机
n03476991: 发胶
n07697100: 汉堡包
n03481172: 锤子
n02342885: 仓鼠
n03494278: 口琴
n03495258: 竖琴
n03124170: 宽檐帽
n07714571: 卷心菜
n03513137: 头盔
n02398521: 河马
n03535780: 单杠
n02374451: 马
n07697537: 热狗
n03584254: iPod
n01990800: 等足类动物
n01910747: 水母
n01882714: 考拉
n03633091: 长柄勺
n02165456: 瓢虫
n03636649: 灯
n03642806: 笔记本电脑
n07749582: 柠檬
n02129165: 狮子
n03676483: 口红
n01674464: 蜥蜴
n01982650: 龙虾
n03710721: 连体泳衣
n03720891: 沙球
n03759954: 麦克风
n03761084: 微波炉
n03764736: 牛奶罐
n03770439: 超短裙
n02484322: 猴子
n03790512: 摩托车
n07734744: 蘑菇
n03804744: 钉子
n03814639: 颈托
n03838899: 双簧管
n07747607: 橙子
n02444819: 水獭
n03908618: 铅笔盒
n03908714: 卷笔刀
n03916031: 香水
n00007846: 人物
n03928116: 钢琴
n07753275: 菠萝
n03942813: 乒乓球
n03950228: 水罐
n07873807: 披萨
n03958227: 塑料袋
n03961711: 置物架
n07768694: 石榴
n07615774: 冰棒
n02346627: 豪猪
n03995372: 电钻
n07695742: 椒盐脆饼
n04004767: 打印机
n04019541: 冰球
n04023962: 拳击沙袋
n04026417: 手提包
n02324045: 兔子
n04039381: 球拍
n01495701: 鳐鱼
n02509815: 小熊猫
n04070727: 冰箱
n04074963: 遥控器
n04116512: 橡皮擦
n04118538: 橄榄球
n04118776: 尺子
n04131690: 盐罐或胡椒罐
n04141076: 萨克斯管
n01770393: 蝎子
n04154565: 螺丝刀
n02076196: 海豹
n02411705: 绵羊
n04228054: 滑雪板
n02445715: 臭鼬
n01944390: 蜗牛
n01726692: 蛇
n04252077: 雪地摩托
n04252225: 扫雪车
n04254120: 皂液器
n04254680: 足球
n04256520: 沙发
n04270147: 锅铲
n02355227: 松鼠
n02317335: 海星
n04317175: 听诊器
n04330267: 炉灶
n04332243: 滤器
n07745940: 草莓
n04336792: 担架
n04356056: 太阳镜
n04371430: 游泳裤
n02395003: 猪
n04376876: 注射器
n04379243: 桌子
n04392985: 磁带播放器
n04409515: 网球
n01776313: 蜱虫
n04591157: 领带
n02129604: 老虎
n04442312: 烤面包机
n06874185: 交通信号灯
n04468005: 火车
n04487394: 长号
n03110669: 小号
n01662784: 乌龟
n03211117: 电视或显示器
n04509417: 独轮车
n04517823: 吸尘器
n04536866: 小提琴
n04540053: 排球
n04542943: 华夫饼机
n04554684: 洗衣机
n04557648: 水瓶
n04530566: 船只
n02062744: 鲸
n04591713: 葡萄酒瓶
n02391049: 斑马

## 引用文献

@article{ILSVRC15,
Author = {Olga Russakovsky and Jia Deng and Hao Su and Jonathan Krause and Sanjeev Satheesh and Sean Ma and Zhiheng Huang and Andrej Karpathy and Aditya Khosla and Michael Bernstein and Alexander C. Berg and Li Fei-Fei},
Title = {{ImageNet Large Scale Visual Recognition Challenge}},
Year = {2015},
journal = {International Journal of Computer Vision (IJCV)},
doi = {10.1007/s11263-015-0816-y},
volume={115},
number={3},
pages={211-252}
}

## 数据集下载
:modelscope-code[]{type="git"}

搜集汇总

背景与挑战

背景概述

ILSVRC2015_DET是ImageNet大规模视觉识别挑战赛2015年的物体检测数据集，包含456,567张训练图像和20,121张验证图像，所有图像为JPEG格式；测试集新增11,142张图像，总计51,294张。数据集涵盖200个常见物体类别（如乐器、交通工具、动物等），适用于物体检测任务，是计算机视觉领域的重要基准数据集。