特别注意数据集中里面图片都是从大模型生成的视频截图的，注意查看图片预览

数据集格式：Pascal VOC格式+YOLO格式(不包含分割路径的txt文件，仅仅包含jpg图片以及对应的VOC格式xml文件和yolo格式txt文件)

图片数量(jpg文件个数)：9366

标注数量(xml文件个数)：9366

标注数量(txt文件个数)：9366

标注类别数：69

所在github仓库：firc-dataset

标注类别名称(注意yolo格式类别顺序不和这个对应，而以labels文件夹classes.txt为准):["Apple","Apple_sliced","Book","Book_opened","Bottle","Bottle_filled","Bowl","Bowl_filled","Bread","Bread_cooked_sliced","Bread_sliced","ButterKnife","Cabinet","Cabinet_opened","CoffeeMachine","CounterTop","CreditCard","Cup","Cup_filled","DishSponge","Drawer","Drawer_opened","Egg","Egg_cooked_sliced","Egg_sliced","Faucet","Floor","Fork","Fridge","Fridge_opened","GarbageCan","HousePlant","Kettle","Knife","Lettuce","Lettuce_sliced","LightSwitch","Microwave","Microwave_opened","Mug","Mug_filled","Pan","PaperTowelRoll","PepperShaker","Plate","Pot","Pot_filled","Potato","Potato_cooked","Potato_cooked_sliced","Potato_sliced","SaltShaker","Shelf","ShelvingUnit","Sink","SoapBottle","Spatula","Spoon","Statue","Stool","StoveBurner","StoveKnob","Toaster","Tomato","Tomato_sliced","Vase","Window","WineBottle","WineBottle_filled"]

每个类别标注的框数：

Apple（苹果）框数 = 634  

Apple_sliced（苹果切片）框数 = 837  

Book（书本）框数 = 1575  

Book_opened（打开的书本）框数 = 332  

Bottle（瓶子）框数 = 1355  

Bottle_filled（装满的瓶子）框数 = 344  

Bowl（碗）框数 = 1873  

Bowl_filled（装满的碗）框数 = 458  

Bread（面包）框数 = 2813  

Bread_cooked_sliced（熟面包切片）框数 = 230  

Bread_sliced（面包切片）框数 = 5264  

ButterKnife（黄油刀）框数 = 1802  

Cabinet（橱柜）框数 = 16998  

Cabinet_opened（打开的橱柜）框数 = 1576  

CoffeeMachine（咖啡机）框数 = 2122  

CounterTop（台面）框数 = 10326  

CreditCard（信用卡）框数 = 678  

Cup（杯子）框数 = 2169  

Cup_filled（装满的杯子）框数 = 447  

DishSponge（洗碗海绵）框数 = 1023  

Drawer（抽屉）框数 = 14301  

Drawer_opened（打开的抽屉）框数 = 696  

Egg（鸡蛋）框数 = 222  

Egg_cooked_sliced（熟鸡蛋切片）框数 = 344  

Egg_sliced（鸡蛋切片）框数 = 1239  

Faucet（水龙头）框数 = 1982  

Floor（地板）框数 = 6408  

Fork（叉子）框数 = 2223  

Fridge（冰箱）框数 = 1896  

Fridge_opened（打开的冰箱）框数 = 317  

GarbageCan（垃圾桶）框数 = 989  

HousePlant（室内植物）框数 = 2044  

Kettle（水壶）框数 = 2625  

Knife（刀）框数 = 3119  

Lettuce（生菜）框数 = 1959  

Lettuce_sliced（生菜切片）框数 = 5407  

LightSwitch（电灯开关）框数 = 370  

Microwave（微波炉）框数 = 1856  

Microwave_opened（打开的微波炉）框数 = 371  

Mug（马克杯）框数 = 1470  

Mug_filled（装满的马克杯）框数 = 252  

Pan（平底锅）框数 = 3033  

PaperTowelRoll（厨房纸巾卷）框数 = 1679  

PepperShaker（胡椒瓶）框数 = 1187  

Plate（盘子）框数 = 1589  

Pot（锅）框数 = 2362  

Pot_filled（装满的锅）框数 = 911  

Potato（土豆）框数 = 1327  

Potato_cooked（熟土豆）框数 = 385  

Potato_cooked_sliced（熟土豆切片）框数 = 379  

Potato_sliced（土豆切片）框数 = 1089  

SaltShaker（盐瓶）框数 = 1319  

Shelf（搁板）框数 = 7509  

ShelvingUnit（货架单元）框数 = 3332  

Sink（水槽）框数 = 3715  

SoapBottle（皂液瓶）框数 = 2083  

Spatula（锅铲）框数 = 1033  

Spoon（勺子）框数 = 2356  

Statue（雕像）框数 = 1814  

Stool（凳子）框数 = 2839  

StoveBurner（炉灶燃烧器）框数 = 7571  

StoveKnob（炉灶旋钮）框数 = 3041  

Toaster（烤面包机）框数 = 1855  

Tomato（西红柿）框数 = 1340  

Tomato_sliced（西红柿切片）框数 = 3644  

Vase（花瓶）框数 = 2360  

Window（窗户）框数 = 3616  

WineBottle（葡萄酒瓶）框数 = 1058  

WineBottle_filled（装满的葡萄酒瓶）框数 = 1324

总框数：168696

图片分辨率：720x720

使用标注工具：labelImg

标注规则：对类别进行画矩形框

重要说明：数据集没有划分训练验证测试集需自行划分

特别声明：本数据集不对训练的模型或者权重文件精度作任何保证

图片预览：

标注例子：