Link to this sectionADE20K 数据集#

ADE20K 数据集是由 MIT CSAIL 发布的大规模语义分割和场景解析基准。它提供了涵盖各种室内、室外、物体和背景类别的密集标注图像，使其成为研究人员和开发人员使用 Ultralytics YOLO 模型进行密集场景理解任务的重要资源。

Link to this section主要特性#

ADE20K 包含 20,210 张训练图像、2,000 张验证图像和 3,352 张测试图像。
该数据集涵盖了 150 个语义类别，跨越了室内、室外、物体和背景类别。
标注是适用于密集场景解析的像素级分割掩码。

Link to this section数据集结构#

Ultralytics 配置要求采用官方的 ADEChallengeData2016 布局：

ADEChallengeData2016/
├── images/
│   ├── training/
│   └── validation/
└── annotations/
    ├── training/
    └── validation/

masks_dir 字段设置为 annotations，因此 images/ 下的每张图像都与 annotations/ 下的对应掩码配对。原始 ADE20K 掩码使用源标签 ID，其中 0 被忽略，label_mapping 部分将有效的 1 到 150 标签转换为连续的训练 ID 0 到 149，并将忽略的像素映射为 255。

Link to this section应用#

ADE20K 被广泛用于训练和评估语义分割和场景解析中的深度学习模型。其多样的类别集和复杂的场景使其在自动导航、机器人、增强现实和图像编辑等应用中极具价值。

室内和室外场景的广度也使 ADE20K 成为评估模型跨域泛化能力的有力基准。

Link to this section数据集 YAML#

数据集 YAML 文件定义了 ADE20K 的路径、类别、掩码目录和标签映射。ade20k.yaml 文件维护在 https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/ade20k.yaml。

ultralytics/cfg/datasets/ade20k.yaml

# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license

# ADE20K semantic segmentation dataset (150 classes)
# Documentation: https://docs.ultralytics.com/datasets/semantic/ade20k/
# Example usage: yolo semantic train data=ade20k.yaml model=yolo26n-sem.pt
# parent
# ├── ultralytics
# └── datasets
#     └── ADEChallengeData2016 ← downloads here (1 GB)
#         └── images
#         └── annotations

# Dataset root directory
path: ADEChallengeData2016
train: images/training
val: images/validation

masks_dir: annotations # semantic mask directory

# ADE20K 150-class labels
names:
  0: wall
  1: building
  2: sky
  3: floor
  4: tree
  5: ceiling
  6: road
  7: bed
  8: windowpane
  9: grass
  10: cabinet
  11: sidewalk
  12: person
  13: earth
  14: door
  15: table
  16: mountain
  17: plant
  18: curtain
  19: chair
  20: car
  21: water
  22: painting
  23: sofa
  24: shelf
  25: house
  26: sea
  27: mirror
  28: rug
  29: field
  30: armchair
  31: seat
  32: fence
  33: desk
  34: rock
  35: wardrobe
  36: lamp
  37: bathtub
  38: railing
  39: cushion
  40: base
  41: box
  42: column
  43: signboard
  44: chest of drawers
  45: counter
  46: sand
  47: sink
  48: skyscraper
  49: fireplace
  50: refrigerator
  51: grandstand
  52: path
  53: stairs
  54: runway
  55: case
  56: pool table
  57: pillow
  58: screen door
  59: stairway
  60: river
  61: bridge
  62: bookcase
  63: blind
  64: coffee table
  65: toilet
  66: flower
  67: book
  68: hill
  69: bench
  70: countertop
  71: stove
  72: palm
  73: kitchen island
  74: computer
  75: swivel chair
  76: boat
  77: bar
  78: arcade machine
  79: hovel
  80: bus
  81: towel
  82: light
  83: truck
  84: tower
  85: chandelier
  86: awning
  87: streetlight
  88: booth
  89: television receiver
  90: airplane
  91: dirt track
  92: apparel
  93: pole
  94: land
  95: bannister
  96: escalator
  97: ottoman
  98: bottle
  99: buffet
  100: poster
  101: stage
  102: van
  103: ship
  104: fountain
  105: conveyor belt
  106: canopy
  107: washer
  108: plaything
  109: swimming pool
  110: stool
  111: barrel
  112: basket
  113: waterfall
  114: tent
  115: bag
  116: minibike
  117: cradle
  118: oven
  119: ball
  120: food
  121: step
  122: tank
  123: trade name
  124: microwave
  125: pot
  126: animal
  127: bicycle
  128: lake
  129: dishwasher
  130: screen
  131: blanket
  132: sculpture
  133: hood
  134: sconce
  135: vase
  136: traffic light
  137: tray
  138: ashcan
  139: fan
  140: pier
  141: crt screen
  142: plate
  143: monitor
  144: bulletin board
  145: shower
  146: radiator
  147: glass
  148: clock
  149: flag

# Map source label IDs to train IDs; ignore_label is converted to 255.
label_mapping:
  0: ignore_label
  1: 0
  2: 1
  3: 2
  4: 3
  5: 4
  6: 5
  7: 6
  8: 7
  9: 8
  10: 9
  11: 10
  12: 11
  13: 12
  14: 13
  15: 14
  16: 15
  17: 16
  18: 17
  19: 18
  20: 19
  21: 20
  22: 21
  23: 22
  24: 23
  25: 24
  26: 25
  27: 26
  28: 27
  29: 28
  30: 29
  31: 30
  32: 31
  33: 32
  34: 33
  35: 34
  36: 35
  37: 36
  38: 37
  39: 38
  40: 39
  41: 40
  42: 41
  43: 42
  44: 43
  45: 44
  46: 45
  47: 46
  48: 47
  49: 48
  50: 49
  51: 50
  52: 51
  53: 52
  54: 53
  55: 54
  56: 55
  57: 56
  58: 57
  59: 58
  60: 59
  61: 60
  62: 61
  63: 62
  64: 63
  65: 64
  66: 65
  67: 66
  68: 67
  69: 68
  70: 69
  71: 70
  72: 71
  73: 72
  74: 73
  75: 74
  76: 75
  77: 76
  78: 77
  79: 78
  80: 79
  81: 80
  82: 81
  83: 82
  84: 83
  85: 84
  86: 85
  87: 86
  88: 87
  89: 88
  90: 89
  91: 90
  92: 91
  93: 92
  94: 93
  95: 94
  96: 95
  97: 96
  98: 97
  99: 98
  100: 99
  101: 100
  102: 101
  103: 102
  104: 103
  105: 104
  106: 105
  107: 106
  108: 107
  109: 108
  110: 109
  111: 110
  112: 111
  113: 112
  114: 113
  115: 114
  116: 115
  117: 116
  118: 117
  119: 118
  120: 119
  121: 120
  122: 121
  123: 122
  124: 123
  125: 124
  126: 125
  127: 126
  128: 127
  129: 128
  130: 129
  131: 130
  132: 131
  133: 132
  134: 133
  135: 134
  136: 135
  137: 136
  138: 137
  139: 138
  140: 139
  141: 140
  142: 141
  143: 142
  144: 143
  145: 144
  146: 145
  147: 146
  148: 147
  149: 148
  150: 149
# Download URL (manual): http://data.csail.mit.edu/places/ADEchallenge/ADEChallengeData2016.zip

Link to this section使用方法#

若要使用图像尺寸 512 训练 YOLO26n-sem 模型并在 ADE20K 数据集上进行 100 个 epoch 的训练，你可以使用以下代码片段。有关可用参数的完整列表，请参阅模型训练页面。

训练示例

from ultralytics import YOLO

# Load a model
model = YOLO("yolo26n-sem.pt")  # load a pretrained model (recommended for training)

# Train the model
results = model.train(data="ade20k.yaml", epochs=100, imgsz=512)

Link to this section引用与致谢#

如果你在研究或开发工作中使用 ADE20K 数据集，请引用以下论文：

引用

@inproceedings{zhou2017scene,
  title={Scene Parsing through ADE20K Dataset},
  author={Zhou, Bolei and Zhao, Hang and Puig, Xavier and Fidler, Sanja and Barriuso, Adela and Torralba, Antonio},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  year={2017}
}

我们要感谢 MIT CSAIL 计算机视觉组为计算机视觉社区创建并维护了这一宝贵资源。有关 ADE20K 数据集及其创建者的更多信息，请访问 ADE20K 数据集网站。

Link to this section常见问题#

Link to this section什么是 ADE20K 数据集，为什么它对计算机视觉很重要？#

ADE20K 数据集是一个用于语义分割的大规模场景解析基准。它包含 25,562 张密集标注的图像，涵盖了室内、室外、物体和背景类别的 150 个类别。研究人员使用 ADE20K 是因为它拥有多样的场景、细粒度的类别集以及诸如平均交并比 (mIoU) 等标准化评估指标，这使其成为基准测试密集预测模型的理想选择。

Link to this section如何使用 ADE20K 数据集训练 YOLO 模型？#

若要使用图像尺寸 512 训练 YOLO26n-sem 模型并在 ADE20K 数据集上进行 100 个 epoch 的训练，你可以使用以下代码片段。有关可用参数的详细列表，请参阅模型训练页面。

训练示例

from ultralytics import YOLO

# Load a model
model = YOLO("yolo26n-sem.pt")  # load a pretrained model (recommended for training)

# Train the model
results = model.train(data="ade20k.yaml", epochs=100, imgsz=512)