Link to this sectionOpen Images V7 数据集#
Open Images V7 是由 Google 创建的大规模 目标检测 数据集，在 Ultralytics 配置中包含 1,743,042 张训练图像和 41,620 张验证图像，涵盖 601 个对象类别。其原始版本包含约 900 万张标注有图像级标签、边界框、分割掩码、视觉关系和局部描述的图像，使其成为 计算机视觉 领域中最广泛的标注资源之一。
Watch: Object Detection Using an Open Images V7 Pretrained Model
Link to this sectionOpen Images V7 预训练模型#
Ultralytics 发布了五款在 Open Images V7 上预训练的 YOLOv8 模型，因此你无需下载数据集即可检测其 601 个类别：
| 模型 | 尺寸 (像素) | mAPval 50-95 | 速度 CPU ONNX (ms) | 速度 A100 TensorRT (ms) | 参数量 (M) | FLOPs (B) |
|---|---|---|---|---|---|---|
| YOLOv8n | 640 | 18.4 | 142.4 | 1.21 | 3.5 | 10.5 |
| YOLOv8s | 640 | 27.7 | 183.1 | 1.40 | 11.4 | 29.7 |
| YOLOv8m | 640 | 33.6 | 408.5 | 2.26 | 26.2 | 80.6 |
| YOLOv8l | 640 | 34.9 | 596.9 | 2.43 | 44.1 | 167.4 |
| YOLOv8x | 640 | 36.3 | 860.6 | 3.56 | 68.7 | 260.6 |
下方的可视化展示了这些模型可以检测的对象类别范围：
你可以按照以下方式运行 预测 或从这些检查点开始进行微调。
预训练模型使用示例
from ultralytics import YOLO
# Load an Open Images V7 pretrained YOLOv8n model
model = YOLO("yolov8n-oiv7.pt")
# Run prediction
results = model.predict(source="image.jpg")
# Start training from the pretrained checkpoint
results = model.train(data="coco8.yaml", epochs=100, imgsz=640)Link to this section主要特性#
- Open Images V7 包含约 900 万张图像，针对多种计算机视觉任务进行了标注，平均每张图像包含 8.3 个对象。
- 其 190 万张图像上的 1600 万个边界框中，约 90% 是由专业标注员手动绘制的，确保了高 精度。
- 标注不仅限于框：包括涵盖 1,466 个三元组的 330 万个视觉关系标注，350 个类别中 280 万个对象的分割掩码（V5 中添加），67.5 万个局部描述（V6），以及 5,827 个类别中 140 万张图像上的 6,640 万个点级标签（V7）。
- 跨越 20,638 个类别的 6,140 万个图像级标签使该数据集适用于 图像分类 和多模态研究，以及检测和 实例分割。
Link to this section数据集结构#
Ultralytics open-images-v7.yaml 配置文件用于下载 Open Images V7 的检测子集：
| 拆分 | 图像 | 描述 |
|---|---|---|
| 训练 | 1,743,042 | 用于模型训练的带框标注图像 |
| 验证 | 41,620 | 用于评估和基准测试的预留图像 |
该配置定义了 601 个对象类别，索引从 Accordion 到 Zucchini，编号为 0–600。Google 的文档将此数字向下舍入为 600，但官方可检测类别列表和 Ultralytics YAML 均包含 601 个条目。配置中的 test: 键留空。
Link to this section应用#
Open Images V7 支持在多种计算机视觉任务中训练和评估模型：
- 大词汇量目标检测：凭借 601 个类别，在 Open Images V7 上训练的检测器识别的类别远多于在 COCO 上训练的检测器。
- 视觉关系检测：330 万个关系标注支持能够理解对象之间交互的模型。
- 实例分割：280 万个对象的掩码实现了像素级的场景分析。
- 多模态学习：结合了语音、文本和鼠标轨迹的局部描述将视觉数据与丰富的描述配对。
- 零样本评估：广泛的类别覆盖有助于评估模型处理训练期间未见对象的能力。
若要在浏览器中标注你自己的图像、训练并管理大规模数据集，请使用 Ultralytics Platform 运行完整的工作流程。
Link to this section数据集 YAML#
open-images-v7.yaml 文件定义了数据集配置，包括数据集路径、类名和其他元数据。它维护在 Ultralytics 仓库中：https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/open-images-v7.yaml。
ultralytics/cfg/datasets/open-images-v7.yaml
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
# Open Images v7 dataset https://storage.googleapis.com/openimages/web/index.html by Google
# Documentation: https://docs.ultralytics.com/datasets/detect/open-images-v7
# Example usage: yolo train data=open-images-v7.yaml
# parent
# ├── ultralytics
# └── datasets
# └── open-images-v7 ← downloads here (561 GB)
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
path: open-images-v7 # dataset root dir
train: images/train # train images (relative to 'path') 1743042 images
val: images/val # val images (relative to 'path') 41620 images
test: # test images (optional)
# Classes
names:
0: Accordion
1: Adhesive tape
2: Aircraft
3: Airplane
4: Alarm clock
5: Alpaca
6: Ambulance
7: Animal
8: Ant
9: Antelope
10: Apple
11: Armadillo
12: Artichoke
13: Auto part
14: Axe
15: Backpack
16: Bagel
17: Baked goods
18: Balance beam
19: Ball
20: Balloon
21: Banana
22: Band-aid
23: Banjo
24: Barge
25: Barrel
26: Baseball bat
27: Baseball glove
28: Bat (Animal)
29: Bathroom accessory
30: Bathroom cabinet
31: Bathtub
32: Beaker
33: Bear
34: Bed
35: Bee
36: Beehive
37: Beer
38: Beetle
39: Bell pepper
40: Belt
41: Bench
42: Bicycle
43: Bicycle helmet
44: Bicycle wheel
45: Bidet
46: Billboard
47: Billiard table
48: Binoculars
49: Bird
50: Blender
51: Blue jay
52: Boat
53: Bomb
54: Book
55: Bookcase
56: Boot
57: Bottle
58: Bottle opener
59: Bow and arrow
60: Bowl
61: Bowling equipment
62: Box
63: Boy
64: Brassiere
65: Bread
66: Briefcase
67: Broccoli
68: Bronze sculpture
69: Brown bear
70: Building
71: Bull
72: Burrito
73: Bus
74: Bust
75: Butterfly
76: Cabbage
77: Cabinetry
78: Cake
79: Cake stand
80: Calculator
81: Camel
82: Camera
83: Can opener
84: Canary
85: Candle
86: Candy
87: Cannon
88: Canoe
89: Cantaloupe
90: Car
91: Carnivore
92: Carrot
93: Cart
94: Cassette deck
95: Castle
96: Cat
97: Cat furniture
98: Caterpillar
99: Cattle
100: Ceiling fan
101: Cello
102: Centipede
103: Chainsaw
104: Chair
105: Cheese
106: Cheetah
107: Chest of drawers
108: Chicken
109: Chime
110: Chisel
111: Chopsticks
112: Christmas tree
113: Clock
114: Closet
115: Clothing
116: Coat
117: Cocktail
118: Cocktail shaker
119: Coconut
120: Coffee
121: Coffee cup
122: Coffee table
123: Coffeemaker
124: Coin
125: Common fig
126: Common sunflower
127: Computer keyboard
128: Computer monitor
129: Computer mouse
130: Container
131: Convenience store
132: Cookie
133: Cooking spray
134: Corded phone
135: Cosmetics
136: Couch
137: Countertop
138: Cowboy hat
139: Crab
140: Cream
141: Cricket ball
142: Crocodile
143: Croissant
144: Crown
145: Crutch
146: Cucumber
147: Cupboard
148: Curtain
149: Cutting board
150: Dagger
151: Dairy Product
152: Deer
153: Desk
154: Dessert
155: Diaper
156: Dice
157: Digital clock
158: Dinosaur
159: Dishwasher
160: Dog
161: Dog bed
162: Doll
163: Dolphin
164: Door
165: Door handle
166: Donut
167: Dragonfly
168: Drawer
169: Dress
170: Drill (Tool)
171: Drink
172: Drinking straw
173: Drum
174: Duck
175: Dumbbell
176: Eagle
177: Earrings
178: Egg (Food)
179: Elephant
180: Envelope
181: Eraser
182: Face powder
183: Facial tissue holder
184: Falcon
185: Fashion accessory
186: Fast food
187: Fax
188: Fedora
189: Filing cabinet
190: Fire hydrant
191: Fireplace
192: Fish
193: Flag
194: Flashlight
195: Flower
196: Flowerpot
197: Flute
198: Flying disc
199: Food
200: Food processor
201: Football
202: Football helmet
203: Footwear
204: Fork
205: Fountain
206: Fox
207: French fries
208: French horn
209: Frog
210: Fruit
211: Frying pan
212: Furniture
213: Garden Asparagus
214: Gas stove
215: Giraffe
216: Girl
217: Glasses
218: Glove
219: Goat
220: Goggles
221: Goldfish
222: Golf ball
223: Golf cart
224: Gondola
225: Goose
226: Grape
227: Grapefruit
228: Grinder
229: Guacamole
230: Guitar
231: Hair dryer
232: Hair spray
233: Hamburger
234: Hammer
235: Hamster
236: Hand dryer
237: Handbag
238: Handgun
239: Harbor seal
240: Harmonica
241: Harp
242: Harpsichord
243: Hat
244: Headphones
245: Heater
246: Hedgehog
247: Helicopter
248: Helmet
249: High heels
250: Hiking equipment
251: Hippopotamus
252: Home appliance
253: Honeycomb
254: Horizontal bar
255: Horse
256: Hot dog
257: House
258: Houseplant
259: Human arm
260: Human beard
261: Human body
262: Human ear
263: Human eye
264: Human face
265: Human foot
266: Human hair
267: Human hand
268: Human head
269: Human leg
270: Human mouth
271: Human nose
272: Humidifier
273: Ice cream
274: Indoor rower
275: Infant bed
276: Insect
277: Invertebrate
278: Ipod
279: Isopod
280: Jacket
281: Jacuzzi
282: Jaguar (Animal)
283: Jeans
284: Jellyfish
285: Jet ski
286: Jug
287: Juice
288: Kangaroo
289: Kettle
290: Kitchen & dining room table
291: Kitchen appliance
292: Kitchen knife
293: Kitchen utensil
294: Kitchenware
295: Kite
296: Knife
297: Koala
298: Ladder
299: Ladle
300: Ladybug
301: Lamp
302: Land vehicle
303: Lantern
304: Laptop
305: Lavender (Plant)
306: Lemon
307: Leopard
308: Light bulb
309: Light switch
310: Lighthouse
311: Lily
312: Limousine
313: Lion
314: Lipstick
315: Lizard
316: Lobster
317: Loveseat
318: Luggage and bags
319: Lynx
320: Magpie
321: Mammal
322: Man
323: Mango
324: Maple
325: Maracas
326: Marine invertebrates
327: Marine mammal
328: Measuring cup
329: Mechanical fan
330: Medical equipment
331: Microphone
332: Microwave oven
333: Milk
334: Miniskirt
335: Mirror
336: Missile
337: Mixer
338: Mixing bowl
339: Mobile phone
340: Monkey
341: Moths and butterflies
342: Motorcycle
343: Mouse
344: Muffin
345: Mug
346: Mule
347: Mushroom
348: Musical instrument
349: Musical keyboard
350: Nail (Construction)
351: Necklace
352: Nightstand
353: Oboe
354: Office building
355: Office supplies
356: Orange
357: Organ (Musical Instrument)
358: Ostrich
359: Otter
360: Oven
361: Owl
362: Oyster
363: Paddle
364: Palm tree
365: Pancake
366: Panda
367: Paper cutter
368: Paper towel
369: Parachute
370: Parking meter
371: Parrot
372: Pasta
373: Pastry
374: Peach
375: Pear
376: Pen
377: Pencil case
378: Pencil sharpener
379: Penguin
380: Perfume
381: Person
382: Personal care
383: Personal flotation device
384: Piano
385: Picnic basket
386: Picture frame
387: Pig
388: Pillow
389: Pineapple
390: Pitcher (Container)
391: Pizza
392: Pizza cutter
393: Plant
394: Plastic bag
395: Plate
396: Platter
397: Plumbing fixture
398: Polar bear
399: Pomegranate
400: Popcorn
401: Porch
402: Porcupine
403: Poster
404: Potato
405: Power plugs and sockets
406: Pressure cooker
407: Pretzel
408: Printer
409: Pumpkin
410: Punching bag
411: Rabbit
412: Raccoon
413: Racket
414: Radish
415: Ratchet (Device)
416: Raven
417: Rays and skates
418: Red panda
419: Refrigerator
420: Remote control
421: Reptile
422: Rhinoceros
423: Rifle
424: Ring binder
425: Rocket
426: Roller skates
427: Rose
428: Rugby ball
429: Ruler
430: Salad
431: Salt and pepper shakers
432: Sandal
433: Sandwich
434: Saucer
435: Saxophone
436: Scale
437: Scarf
438: Scissors
439: Scoreboard
440: Scorpion
441: Screwdriver
442: Sculpture
443: Sea lion
444: Sea turtle
445: Seafood
446: Seahorse
447: Seat belt
448: Segway
449: Serving tray
450: Sewing machine
451: Shark
452: Sheep
453: Shelf
454: Shellfish
455: Shirt
456: Shorts
457: Shotgun
458: Shower
459: Shrimp
460: Sink
461: Skateboard
462: Ski
463: Skirt
464: Skull
465: Skunk
466: Skyscraper
467: Slow cooker
468: Snack
469: Snail
470: Snake
471: Snowboard
472: Snowman
473: Snowmobile
474: Snowplow
475: Soap dispenser
476: Sock
477: Sofa bed
478: Sombrero
479: Sparrow
480: Spatula
481: Spice rack
482: Spider
483: Spoon
484: Sports equipment
485: Sports uniform
486: Squash (Plant)
487: Squid
488: Squirrel
489: Stairs
490: Stapler
491: Starfish
492: Stationary bicycle
493: Stethoscope
494: Stool
495: Stop sign
496: Strawberry
497: Street light
498: Stretcher
499: Studio couch
500: Submarine
501: Submarine sandwich
502: Suit
503: Suitcase
504: Sun hat
505: Sunglasses
506: Surfboard
507: Sushi
508: Swan
509: Swim cap
510: Swimming pool
511: Swimwear
512: Sword
513: Syringe
514: Table
515: Table tennis racket
516: Tablet computer
517: Tableware
518: Taco
519: Tank
520: Tap
521: Tart
522: Taxi
523: Tea
524: Teapot
525: Teddy bear
526: Telephone
527: Television
528: Tennis ball
529: Tennis racket
530: Tent
531: Tiara
532: Tick
533: Tie
534: Tiger
535: Tin can
536: Tire
537: Toaster
538: Toilet
539: Toilet paper
540: Tomato
541: Tool
542: Toothbrush
543: Torch
544: Tortoise
545: Towel
546: Tower
547: Toy
548: Traffic light
549: Traffic sign
550: Train
551: Training bench
552: Treadmill
553: Tree
554: Tree house
555: Tripod
556: Trombone
557: Trousers
558: Truck
559: Trumpet
560: Turkey
561: Turtle
562: Umbrella
563: Unicycle
564: Van
565: Vase
566: Vegetable
567: Vehicle
568: Vehicle registration plate
569: Violin
570: Volleyball (Ball)
571: Waffle
572: Waffle iron
573: Wall clock
574: Wardrobe
575: Washing machine
576: Waste container
577: Watch
578: Watercraft
579: Watermelon
580: Weapon
581: Whale
582: Wheel
583: Wheelchair
584: Whisk
585: Whiteboard
586: Willow
587: Window
588: Window blind
589: Wine
590: Wine glass
591: Wine rack
592: Winter melon
593: Wok
594: Woman
595: Wood-burning stove
596: Woodpecker
597: Worm
598: Wrench
599: Zebra
600: Zucchini
# Download script/URL (optional) ---------------------------------------------------------------------------------------
download: |
import warnings
from ultralytics.utils import LOGGER, SETTINGS, Path
from ultralytics.utils.checks import check_requirements
check_requirements("fiftyone")
import fiftyone as fo
import fiftyone.zoo as foz
name = "open-images-v7"
fo.config.dataset_zoo_dir = Path(SETTINGS["datasets_dir"]) / "fiftyone" / name
fraction = 1.0 # fraction of full dataset to use
LOGGER.warning("Open Images V7 dataset requires at least **561 GB of free space. Starting download...")
for split in "train", "validation": # 1743042 train, 41620 val images
train = split == "train"
# Load Open Images dataset
dataset = foz.load_zoo_dataset(
name,
split=split,
label_types=["detections"],
max_samples=round((1743042 if train else 41620) * fraction),
)
# Define classes
if train:
classes = dataset.default_classes # all classes
# classes = dataset.distinct('ground_truth.detections.label') # only observed classes
# Export to YOLO format
with warnings.catch_warnings():
warnings.filterwarnings("ignore", category=UserWarning, module="fiftyone.utils.yolo")
dataset.export(
export_dir=str(Path(SETTINGS["datasets_dir"]) / name),
dataset_type=fo.types.YOLOv5Dataset,
label_field="ground_truth",
split="val" if split == "validation" else split,
classes=classes,
overwrite=train,
)Link to this section用法#
561 GB 下载
Open Images V7 在首次使用时会自动下载，其 1,743,042 张训练图像和 41,620 张验证图像需要约 561 GB 的可用磁盘空间。下载脚本会安装 fiftyone 包以获取图像，并将标注转换为 YOLO 格式，这可能需要很长时间，具体取决于你的网络连接和硬件。
要使用 640 的图像尺寸在 Open Images V7 数据集上训练 YOLO26n 模型 100 个 epoch，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 COCO-pretrained YOLO26n model
model = YOLO("yolo26n.pt")
# Train the model on the Open Images V7 dataset
results = model.train(data="open-images-v7.yaml", epochs=100, imgsz=640)Link to this section样本图像和标注#
下面的示例展示了 Open Images V7 在单张图像上叠加的边界框、关系和掩码标注：
Link to this section引用与致谢#
如果你在研究或开发工作中使用了 Open Images V7 数据集，请引用以下论文：
引用
@article{OpenImages,
author = {Alina Kuznetsova and Hassan Rom and Neil Alldrin and Jasper Uijlings and Ivan Krasin and Jordi Pont-Tuset and Shahab Kamali and Stefan Popov and Matteo Malloci and Alexander Kolesnikov and Tom Duerig and Vittorio Ferrari},
title = {The Open Images Dataset V4: Unified image classification, object detection, and visual relationship detection at scale},
year = {2020},
journal = {IJCV}
}我们要感谢 Google AI 团队创建并维护了 Open Images V7 数据集。有关该数据集的更多信息，请访问 官方 Open Images V7 网站。
Link to this section常见问题解答#
Link to this sectionOpen Images V7 数据集有什么用途？#
Open Images V7 数据集 用于在 机器学习 和计算机视觉中训练和评估目标检测模型。Ultralytics 配置提供了涵盖 601 个对象类别的 1,743,042 张训练图像和 41,620 张验证图像，而原始版本还携带用于更广泛研究的分割掩码、视觉关系、局部描述和点级标签。
Link to this sectionOpen Images V7 数据集中有多少图像和类别？#
Ultralytics open-images-v7.yaml 配置涵盖 601 个对象类别，包含 1,743,042 张训练图像和 41,620 张验证图像，没有测试集划分。完整的原始版本包含约 900 万张图像；Google 的文档将可检测类别数量向下舍入为 600，但官方类别列表包含 601 个条目。
Link to this sectionOpen Images V7 数据集下载有多大？#
Open Images V7 需要约 561 GB 的可用磁盘空间，并在你首次使用 data="open-images-v7.yaml" 进行训练时自动下载。下载脚本会安装 fiftyone 包并将标注转换为 YOLO 格式。如果你需要更小的方案，请浏览 检测数据集概览。
Link to this section如何使用 Open Images V7 数据集训练 YOLO26 模型？#
要使用 Open Images V7 数据集训练 YOLO26 模型，你可以使用 Python 和 CLI 命令。以下是训练 YOLO26n 模型 100 个 epoch 且图像尺寸为 640 的示例：
训练示例
from ultralytics import YOLO
# Load a COCO-pretrained YOLO26n model
model = YOLO("yolo26n.pt")
# Train the model on the Open Images V7 dataset
results = model.train(data="open-images-v7.yaml", epochs=100, imgsz=640)有关参数和设置的更多详细信息，请参阅 训练 页面。
Link to this section我可以在不下载数据集的情况下使用 Open Images V7 预训练模型吗？#
可以。五款 Open Images V7 预训练模型（从 yolov8n-oiv7.pt 到 yolov8x-oiv7.pt，mAP 范围为 18.4 到 36.3）开箱即可检测所有 601 个类别，因此你可以在自己的图像上运行 预测，无需下载 561 GB 的数据。
Link to this sectionOpen Images V7 与 COCO 数据集相比如何？#
Open Images V7 更大且涵盖范围更广：拥有 601 个对象类别和 1,743,042 张训练图像，而 COCO 拥有 80 个类别和 118,287 张训练图像。COCO 仍然是比较检测器的标准基准，而 Open Images V7 更适合大词汇量检测和需要广泛类别覆盖的预训练模型。