Tensorflow/models 使用 COCO 90 类 ID 虽然 COCO 只有 80 个类别

Question

Tensorflows object_detection 项目的 labelmaps 包含 90 个类，虽然 COCO 只有 80 个类别。因此num_classes所有示例配置中的参数都设置为 90。

如果我现在下载并使用 COCO 2017 数据集，我需要将此参数设置为 80 还是保留为 90？

如果80（因为COCO有80个类）我需要调整labelmap，所以标准mscoco_label_map.pbtxt不正确，对吧？

如果有人能对此有所启发，我将非常感激:)

以下是标准的 80 个 COCO 类：

person
bicycle
car
motorbike
aeroplane
bus
train
truck
boat
traffic light
fire hydrant
stop sign
parking meter
bench
bird
cat
dog
horse
sheep
cow
elephant
bear
zebra
giraffe
backpack
umbrella
handbag
tie
suitcase
frisbee
skis
snowboard
sports ball
kite
baseball bat
baseball glove
skateboard
surfboard
tennis racket
bottle
wine glass
cup
fork
knife
spoon
bowl
banana
apple
sandwich
orange
broccoli
carrot
hot dog
pizza
donut
cake
chair
sofa
pottedplant
bed
diningtable
toilet
tvmonitor
laptop
mouse
remote
keyboard
cell phone
microwave
oven
toaster
sink
refrigerator
book
clock
vase
scissors
teddy bear
hair drier
toothbrush

这是 Tensorflows object_detection API 的 MS COCO 标签图：

item {
  name: "/m/01g317"
  id: 1
  display_name: "person"
}
item {
  name: "/m/0199g"
  id: 2
  display_name: "bicycle"
}
item {
  name: "/m/0k4j"
  id: 3
  display_name: "car"
}
item {
  name: "/m/04_sv"
  id: 4
  display_name: "motorcycle"
}
item {
  name: "/m/05czz6l"
  id: 5
  display_name: "airplane"
}
item {
  name: "/m/01bjv"
  id: 6
  display_name: "bus"
}
item {
  name: "/m/07jdr"
  id: 7
  display_name: "train"
}
item {
  name: "/m/07r04"
  id: 8
  display_name: "truck"
}
item {
  name: "/m/019jd"
  id: 9
  display_name: "boat"
}
item {
  name: "/m/015qff"
  id: 10
  display_name: "traffic light"
}
item {
  name: "/m/01pns0"
  id: 11
  display_name: "fire hydrant"
}
item {
  name: "/m/02pv19"
  id: 13
  display_name: "stop sign"
}
item {
  name: "/m/015qbp"
  id: 14
  display_name: "parking meter"
}
item {
  name: "/m/0cvnqh"
  id: 15
  display_name: "bench"
}
item {
  name: "/m/015p6"
  id: 16
  display_name: "bird"
}
item {
  name: "/m/01yrx"
  id: 17
  display_name: "cat"
}
item {
  name: "/m/0bt9lr"
  id: 18
  display_name: "dog"
}
item {
  name: "/m/03k3r"
  id: 19
  display_name: "horse"
}
item {
  name: "/m/07bgp"
  id: 20
  display_name: "sheep"
}
item {
  name: "/m/01xq0k1"
  id: 21
  display_name: "cow"
}
item {
  name: "/m/0bwd_0j"
  id: 22
  display_name: "elephant"
}
item {
  name: "/m/01dws"
  id: 23
  display_name: "bear"
}
item {
  name: "/m/0898b"
  id: 24
  display_name: "zebra"
}
item {
  name: "/m/03bk1"
  id: 25
  display_name: "giraffe"
}
item {
  name: "/m/01940j"
  id: 27
  display_name: "backpack"
}
item {
  name: "/m/0hnnb"
  id: 28
  display_name: "umbrella"
}
item {
  name: "/m/080hkjn"
  id: 31
  display_name: "handbag"
}
item {
  name: "/m/01rkbr"
  id: 32
  display_name: "tie"
}
item {
  name: "/m/01s55n"
  id: 33
  display_name: "suitcase"
}
item {
  name: "/m/02wmf"
  id: 34
  display_name: "frisbee"
}
item {
  name: "/m/071p9"
  id: 35
  display_name: "skis"
}
item {
  name: "/m/06__v"
  id: 36
  display_name: "snowboard"
}
item {
  name: "/m/018xm"
  id: 37
  display_name: "sports ball"
}
item {
  name: "/m/02zt3"
  id: 38
  display_name: "kite"
}
item {
  name: "/m/03g8mr"
  id: 39
  display_name: "baseball bat"
}
item {
  name: "/m/03grzl"
  id: 40
  display_name: "baseball glove"
}
item {
  name: "/m/06_fw"
  id: 41
  display_name: "skateboard"
}
item {
  name: "/m/019w40"
  id: 42
  display_name: "surfboard"
}
item {
  name: "/m/0dv9c"
  id: 43
  display_name: "tennis racket"
}
item {
  name: "/m/04dr76w"
  id: 44
  display_name: "bottle"
}
item {
  name: "/m/09tvcd"
  id: 46
  display_name: "wine glass"
}
item {
  name: "/m/08gqpm"
  id: 47
  display_name: "cup"
}
item {
  name: "/m/0dt3t"
  id: 48
  display_name: "fork"
}
item {
  name: "/m/04ctx"
  id: 49
  display_name: "knife"
}
item {
  name: "/m/0cmx8"
  id: 50
  display_name: "spoon"
}
item {
  name: "/m/04kkgm"
  id: 51
  display_name: "bowl"
}
item {
  name: "/m/09qck"
  id: 52
  display_name: "banana"
}
item {
  name: "/m/014j1m"
  id: 53
  display_name: "apple"
}
item {
  name: "/m/0l515"
  id: 54
  display_name: "sandwich"
}
item {
  name: "/m/0cyhj_"
  id: 55
  display_name: "orange"
}
item {
  name: "/m/0hkxq"
  id: 56
  display_name: "broccoli"
}
item {
  name: "/m/0fj52s"
  id: 57
  display_name: "carrot"
}
item {
  name: "/m/01b9xk"
  id: 58
  display_name: "hot dog"
}
item {
  name: "/m/0663v"
  id: 59
  display_name: "pizza"
}
item {
  name: "/m/0jy4k"
  id: 60
  display_name: "donut"
}
item {
  name: "/m/0fszt"
  id: 61
  display_name: "cake"
}
item {
  name: "/m/01mzpv"
  id: 62
  display_name: "chair"
}
item {
  name: "/m/02crq1"
  id: 63
  display_name: "couch"
}
item {
  name: "/m/03fp41"
  id: 64
  display_name: "potted plant"
}
item {
  name: "/m/03ssj5"
  id: 65
  display_name: "bed"
}
item {
  name: "/m/04bcr3"
  id: 67
  display_name: "dining table"
}
item {
  name: "/m/09g1w"
  id: 70
  display_name: "toilet"
}
item {
  name: "/m/07c52"
  id: 72
  display_name: "tv"
}
item {
  name: "/m/01c648"
  id: 73
  display_name: "laptop"
}
item {
  name: "/m/020lf"
  id: 74
  display_name: "mouse"
}
item {
  name: "/m/0qjjc"
  id: 75
  display_name: "remote"
}
item {
  name: "/m/01m2v"
  id: 76
  display_name: "keyboard"
}
item {
  name: "/m/050k8"
  id: 77
  display_name: "cell phone"
}
item {
  name: "/m/0fx9l"
  id: 78
  display_name: "microwave"
}
item {
  name: "/m/029bxz"
  id: 79
  display_name: "oven"
}
item {
  name: "/m/01k6s3"
  id: 80
  display_name: "toaster"
}
item {
  name: "/m/0130jx"
  id: 81
  display_name: "sink"
}
item {
  name: "/m/040b_t"
  id: 82
  display_name: "refrigerator"
}
item {
  name: "/m/0bt_c3"
  id: 84
  display_name: "book"
}
item {
  name: "/m/01x3z"
  id: 85
  display_name: "clock"
}
item {
  name: "/m/02s195"
  id: 86
  display_name: "vase"
}
item {
  name: "/m/01lsmm"
  id: 87
  display_name: "scissors"
}
item {
  name: "/m/0kmg4"
  id: 88
  display_name: "teddy bear"
}
item {
  name: "/m/03wvsk"
  id: 89
  display_name: "hair drier"
}
item {
  name: "/m/012xff"
  id: 90
  display_name: "toothbrush"
}

编辑：在仔细比较两个列表后，很明显它们都包含相同的 80 个类，但默认情况下，tensorflow 使用的标签映射缺少 10 个类 id，似乎是随机分布的。

有人知道这是为什么吗？

Answer 1

MSCOCO 论文描述了该数据集实际上有 91 个类别，但在 2014 年的数据集中，他们仅发布了 80 个类别的子集，因为他们没有注释其余 11 个类别的分割。似乎 tensorflow 模型是使用 90 个类进行训练的。

MSCOCO 论文：https ://arxiv.org/pdf/1405.0312.pdf

来自附录 II：“我们的数据集包含 91 个对象类别（2014 年版本包含其中 80 个类别的分割掩码）。”

-里卡多