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# Open Images V7 Dataset

Open Images V7 is a versatile and expansive dataset championed by Google. Aimed at propelling research in the realm of computer vision, it boasts a vast collection of images annotated with a plethora of data, including image-level labels, object bounding boxes, object segmentation masks, visual relationships, and localized narratives.

Watch: Object Detection using OpenImagesV7 Pretrained Model

## Open Images V7 Pretrained Models

Model | size (pixels) | mAPval 50-95 | Speed CPU ONNX (ms) | Speed A100 TensorRT (ms) | params (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

You can use these pretrained models for inference or fine-tuning as follows.

Pretrained Model Usage Example

Python CLI

    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

## Key Features

- Encompasses ~9M images annotated in various ways to suit multiple computer vision tasks.
- Houses a staggering 16M bounding boxes across 600 object classes in 1.9M images. These boxes are primarily hand-drawn by experts ensuring high precision.
- Visual relationship annotations totaling 3.3M are available, detailing 1,466 unique relationship triplets, object properties, and human activities.
- V5 introduced segmentation masks for 2.8M objects across 350 classes.
- V6 introduced 675k localized narratives that amalgamate voice, text, and mouse traces highlighting described objects.
- V7 introduced 66.4M point-level labels on 1.4M images, spanning 5,827 classes.
- Encompasses 61.4M image-level labels across a diverse set of 20,638 classes.
- Provides a unified platform for image classification, object detection, relationship detection, instance segmentation, and multimodal image descriptions.

## Dataset Structure

Open Images V7 is structured in multiple components catering to varied computer vision challenges:

- Images: About 9 million images, often showcasing intricate scenes with an average of 8.3 objects per image.
- Bounding Boxes: Over 16 million boxes that demarcate objects across 600 categories.
- Segmentation Masks: These detail the exact boundary of 2.8M objects across 350 classes.
- Visual Relationships: 3.3M annotations indicating object relationships, properties, and actions.
- Localized Narratives: 675k descriptions combining voice, text, and mouse traces.
- Point-Level Labels: 66.4M labels across 1.4M images, suitable for zero/few-shot semantic segmentation.

## Applications

Open Images V7 is a cornerstone for training and evaluating state-of-the-art models in various computer vision tasks. The dataset's broad scope and high-quality annotations make it indispensable for researchers and developers specializing in computer vision.

Some key applications include:

- Advanced Object Detection: Train models to identify and locate multiple objects in complex scenes with high accuracy.
- Semantic Understanding: Develop systems that comprehend visual relationships between objects.
- Image Segmentation: Create precise pixel-level masks for objects, enabling detailed scene analysis.
- Multi-modal Learning: Combine visual data with text descriptions for richer AI understanding.
- Zero-shot Learning: Leverage the extensive class coverage to identify objects not seen during training.

## Dataset YAML

Typically, datasets come with a YAML (Yet Another Markup Language) file that delineates the dataset's configuration. For the case of Open Images V7, a hypothetical `OpenImagesV7.yaml` might exist. For accurate paths and configurations, one should refer to the dataset's official repository or documentation.

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: Doughnut
      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,
              )

## Usage

To train a YOLO11n model on the Open Images V7 dataset for 100 epochs with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model Training page.

Warning

The complete Open Images V7 dataset comprises 1,743,042 training images and 41,620 validation images, requiring approximately 561 GB of storage space upon download.

Executing the commands provided below will trigger an automatic download of the full dataset if it's not already present locally. Before running the below example it's crucial to:

- Verify that your device has enough storage capacity.
- Ensure a robust and speedy internet connection.

Train Example

Python CLI

    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

## Sample Data and Annotations

Illustrations of the dataset help provide insights into its richness:

- Open Images V7: This image exemplifies the depth and detail of annotations available, including bounding boxes, relationships, and segmentation masks.

Researchers can gain invaluable insights into the array of computer vision challenges that the dataset addresses, from basic object detection to intricate relationship identification. The diversity of annotations makes Open Images V7 particularly valuable for developing models that can understand complex visual scenes.

## Citations and Acknowledgments

For those employing Open Images V7 in their work, it's prudent to cite the relevant papers and acknowledge the creators:

BibTeX

    @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}
    }

A heartfelt acknowledgment goes out to the Google AI team for creating and maintaining the Open Images V7 dataset. For a deep dive into the dataset and its offerings, navigate to the official Open Images V7 website.

## FAQ

### What is the Open Images V7 dataset?

Open Images V7 is an extensive and versatile dataset created by Google, designed to advance research in computer vision. It includes image-level labels, object bounding boxes, object segmentation masks, visual relationships, and localized narratives, making it ideal for various computer vision tasks such as object detection, segmentation, and relationship detection.

### How do I train a YOLO11 model on the Open Images V7 dataset?

To train a YOLO11 model on the Open Images V7 dataset, you can use both Python and CLI commands. Here's an example of training the YOLO11n model for 100 epochs with an image size of 640:

Train Example

Python CLI

    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

For more details on arguments and settings, refer to the Training page.

### What are some key features of the Open Images V7 dataset?

The Open Images V7 dataset includes approximately 9 million images with various annotations:

- Bounding Boxes: 16 million bounding boxes across 600 object classes.
- Segmentation Masks: Masks for 2.8 million objects across 350 classes.
- Visual Relationships: 3.3 million annotations indicating relationships, properties, and actions.
- Localized Narratives: 675,000 descriptions combining voice, text, and mouse traces.
- Point-Level Labels: 66.4 million labels across 1.4 million images.
- Image-Level Labels: 61.4 million labels across 20,638 classes.

### What pretrained models are available for the Open Images V7 dataset?

Ultralytics provides several YOLOv8 pretrained models for the Open Images V7 dataset, each with different sizes and performance metrics:

Model | size (pixels) | mAPval 50-95 | Speed CPU ONNX (ms) | Speed A100 TensorRT (ms) | params (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

### What applications can the Open Images V7 dataset be used for?

The Open Images V7 dataset supports a variety of computer vision tasks including:

- Image Classification
- Object Detection
- Instance Segmentation
- Visual Relationship Detection
- Multimodal Image Descriptions

Its comprehensive annotations and broad scope make it suitable for training and evaluating advanced machine learning models, as highlighted in practical use cases detailed in our applications section.

📅 Created 1 year ago ✏️ Updated 5 months ago

## Comments