Open Images V7 数据集
Open Images V7 是 Google 倡导的通用且广泛的数据集。 旨在推动计算机视觉领域的研究，它拥有大量的图像集合，这些图像都标有大量数据，包括图像级标签、对象边界框、对象分割掩码、视觉关系和本地化叙述。
观看：目标检测 使用 OpenImagesV7 预训练模型
Open Images V7 预训练模型
| 模型 | 尺寸 (像素) | mAPval 50-95 | 速度 CPU ONNX (毫秒) | 速度 A100 TensorRT (毫秒) | 参数 (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 |
您可以按如下方式使用这些预训练模型进行推理或微调。
预训练模型使用示例
from ultralytics import YOLO
# Load an Open Images Dataset 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)
# Predict using an Open Images Dataset V7 pretrained model
yolo detect predict source=image.jpg model=yolov8n-oiv7.pt
# Start training from an Open Images Dataset V7 pretrained checkpoint
yolo detect train data=coco8.yaml model=yolov8n-oiv7.pt epochs=100 imgsz=640
主要功能
- 包含约 900 万张以各种方式标注的图像，以适应多种计算机视觉任务。
- 在 190 万张图像中包含惊人的 1600 万个边界框，涵盖 600 个对象类别。这些框主要由专家手工绘制，以确保高精度。
- 提供总计 330 万个视觉关系注释，详细描述了 1,466 个独特的关联三元组、对象属性和人类活动。
- V5 引入了 280 万个对象的分割掩码，涵盖 350 个类别。
- V6 引入了 67.5 万个本地化叙述，融合了语音、文本和鼠标轨迹，突出了所描述的对象。
- V7 在 140 万张图像上引入了 6640 万个点级标签，涵盖 5,827 个类别。
- 包含 6140 万个图像级标签，涵盖 20,638 个不同的类别。
- 为图像分类、目标检测、关系检测、实例分割和多模态图像描述提供统一的平台。
数据集结构
Open Images V7 在多个组件中构建，以满足各种计算机视觉挑战：
- 图像：约 900 万张图像，通常展示复杂的场景，平均每张图像有 8.3 个对象。
- 边界框：超过 1600 万个框，用于标定 600 个类别中的对象。
- 分割掩码：这些详细描述了 350 个类别中 280 万个对象的精确边界。
- 视觉关系：330 万个注释，指示对象关系、属性和动作。
- 本地化叙述：67.5 万个描述，结合了语音、文本和鼠标轨迹。
- 点级标签：140 万张图像上的 6640 万个标签，适用于零样本/少样本语义分割。
应用
Open Images V7 是训练和评估各种计算机视觉任务中最先进模型的基石。该数据集的广泛范围和高质量的注释使其对于专门从事计算机视觉的研究人员和开发人员来说是不可或缺的。
一些关键应用包括：
- 高级目标检测: 训练模型以高精度识别和定位复杂场景中的多个目标。
- 语义理解：开发能够理解对象之间视觉关系的的系统。
- 图像分割：为对象创建精确的像素级掩码，从而实现详细的场景分析。
- 多模态学习: 将视觉数据与文本描述相结合，以实现更丰富的 AI 理解。
- Zero-shot Learning：利用广泛的类别覆盖来识别训练期间未见过的对象。
数据集 YAML
Ultralytics 维护着一个 open-images-v7.yaml 文件，其中指定了训练所需的数据集路径、类名和其他配置细节。
OpenImagesV7.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,
)
用法
要在 Open Images V7 数据集上训练 YOLO11n 模型 100 个 epochs，图像大小为 640，您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。
警告
完整的 Open Images V7 数据集包含 1,743,042 张训练图像和 41,620 张验证图像，下载后大约需要 561 GB 的存储空间。
如果本地尚不存在完整数据集，执行以下命令将触发自动下载。 在运行以下示例之前，至关重要的是：
- 确认您的设备有足够的存储容量。
- 确保网络连接稳定且快速。
训练示例
from ultralytics import YOLO
# Load a COCO-pretrained YOLO11n model
model = YOLO("yolo11n.pt")
# Train the model on the Open Images V7 dataset
results = model.train(data="open-images-v7.yaml", epochs=100, imgsz=640)
# Train a COCO-pretrained YOLO11n model on the Open Images V7 dataset
yolo detect train data=open-images-v7.yaml model=yolo11n.pt epochs=100 imgsz=640
样本数据和注释
数据集的图示有助于深入了解其丰富性：
- Open Images V7：此图像例证了可用注释的深度和细节，包括边界框、关系和分割掩码。
研究人员可以深入了解数据集所解决的各种计算机视觉挑战，从基本目标检测到复杂的关联识别。 注释的多样性 使 Open Images V7 在开发能够理解复杂视觉场景的模型方面具有特殊的价值。
引用和致谢
对于在其工作中采用 Open Images V7 的用户，建议引用相关论文并感谢创建者：
@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}
}
衷心感谢 Google AI 团队创建和维护 Open Images V7 数据集。要深入了解数据集及其产品，请访问 Open Images V7 官方网站。
常见问题
什么是 Open Images V7 数据集？
Open Images V7 是 Google 创建的一个广泛而通用的数据集，旨在推进计算机视觉领域的研究。它包括图像级标签、对象边界框、对象分割掩码、视觉关系和本地化叙述，使其成为各种计算机视觉任务（如对象检测、分割和关系检测）的理想选择。
如何在 Open Images V7 数据集上训练 YOLO11 模型？
要在 Open Images V7 数据集上训练 YOLO11 模型，您可以使用 python 和 CLI 命令。以下是使用图像大小为 640 训练 YOLO11n 模型 100 个 epoch 的示例：
训练示例
from ultralytics import YOLO
# Load a COCO-pretrained YOLO11n model
model = YOLO("yolo11n.pt")
# Train the model on the Open Images V7 dataset
results = model.train(data="open-images-v7.yaml", epochs=100, imgsz=640)
# Train a COCO-pretrained YOLO11n model on the Open Images V7 dataset
yolo detect train data=open-images-v7.yaml model=yolo11n.pt 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。
Open Images V7 数据集有哪些主要功能？
Open Images V7 数据集包含大约 900 万张带有各种注释的图像：
- 边界框：跨 600 个对象类别的 1600 万个边界框。
- 分割掩码：跨 350 个类别的 280 万个对象的掩码。
- 视觉关系：330 万个注释，指示关系、属性和动作。
- 本地化叙述：675,000 个描述，结合了语音、文本和鼠标轨迹。
- 点级标签：跨 140 万张图像的 6640 万个标签。
- 图像级标签：跨 20,638 个类别的 6140 万个标签。
Open Images V7 数据集有哪些可用的预训练模型？
Ultralytics 为 Open Images V7 数据集提供了多个 YOLOv8 预训练模型，每个模型都有不同的大小和性能指标：
| 模型 | 尺寸 (像素) | mAPval 50-95 | 速度 CPU ONNX (毫秒) | 速度 A100 TensorRT (毫秒) | 参数 (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 |
Open Images V7 数据集可以用于哪些应用？
Open Images V7 数据集支持各种计算机视觉任务，包括：
- 图像分类
- 目标检测
- 实例分割
- 视觉关系检测
- 多模态图像描述
其全面的注释和广泛的范围使其适合训练和评估高级机器学习模型，正如我们的应用部分中详细介绍的实际用例中所强调的那样。
📅创建于 2 年前 ✏️已更新 5 天前
