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对象 365 数据集

Objects365数据集是一个大规模、高质量的数据集,旨在促进物体检测研究,重点关注野生环境中的各种物体。该数据集由Megvii研究团队创建,提供了大量高分辨率图像,并附有涵盖 365 个物体类别的全面注释边界框。

主要功能

  • Objects365 包含 365 个对象类别,其中有 200 万张图像和 3,000 多万个边界框。
  • 该数据集包括各种场景中的各种物体,为物体检测任务提供了一个丰富而具有挑战性的基准。
  • 注释包括物体的边界框,因此适合用于训练和评估物体检测模型。
  • Objects365 预先训练的模型明显优于 ImageNet 预先训练的模型,从而在各种任务中实现了更好的泛化。

数据集结构

Objects365 数据集被编排成带有相应注释的单一图像集:

  • 图像数据集包括 200 万张高分辨率图像,每张图像都包含 365 个类别中的各种物体。
  • 注释图像上标注了 3,000 多万个边界框,为物体检测任务提供了全面的地面实况信息。

应用

Objects365 数据集被广泛用于训练和评估物体检测任务中的深度学习模型。该数据集包含多种物体类别和高质量注释,是计算机视觉领域研究人员和从业人员的宝贵资源。

数据集 YAML

YAML(另一种标记语言)文件用于定义数据集配置。它包含数据集的路径、类和其他相关信息。就 Objects365 数据集而言,YAML 文件包括 Objects365.yaml 文件保存在 https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/Objects365.yaml.

ultralytics/cfg/datasets/Objects365.yaml

# Ultralytics YOLO 🚀, AGPL-3.0 license
# Objects365 dataset https://www.objects365.org/ by Megvii
# Documentation: https://docs.ultralytics.com/datasets/detect/objects365/
# Example usage: yolo train data=Objects365.yaml
# parent
# ├── ultralytics
# └── datasets
#     └── Objects365  ← downloads here (712 GB = 367G data + 345G zips)

# 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: ../datasets/Objects365 # dataset root dir
train: images/train # train images (relative to 'path') 1742289 images
val: images/val # val images (relative to 'path') 80000 images
test: # test images (optional)

# Classes
names:
  0: Person
  1: Sneakers
  2: Chair
  3: Other Shoes
  4: Hat
  5: Car
  6: Lamp
  7: Glasses
  8: Bottle
  9: Desk
  10: Cup
  11: Street Lights
  12: Cabinet/shelf
  13: Handbag/Satchel
  14: Bracelet
  15: Plate
  16: Picture/Frame
  17: Helmet
  18: Book
  19: Gloves
  20: Storage box
  21: Boat
  22: Leather Shoes
  23: Flower
  24: Bench
  25: Potted Plant
  26: Bowl/Basin
  27: Flag
  28: Pillow
  29: Boots
  30: Vase
  31: Microphone
  32: Necklace
  33: Ring
  34: SUV
  35: Wine Glass
  36: Belt
  37: Monitor/TV
  38: Backpack
  39: Umbrella
  40: Traffic Light
  41: Speaker
  42: Watch
  43: Tie
  44: Trash bin Can
  45: Slippers
  46: Bicycle
  47: Stool
  48: Barrel/bucket
  49: Van
  50: Couch
  51: Sandals
  52: Basket
  53: Drum
  54: Pen/Pencil
  55: Bus
  56: Wild Bird
  57: High Heels
  58: Motorcycle
  59: Guitar
  60: Carpet
  61: Cell Phone
  62: Bread
  63: Camera
  64: Canned
  65: Truck
  66: Traffic cone
  67: Cymbal
  68: Lifesaver
  69: Towel
  70: Stuffed Toy
  71: Candle
  72: Sailboat
  73: Laptop
  74: Awning
  75: Bed
  76: Faucet
  77: Tent
  78: Horse
  79: Mirror
  80: Power outlet
  81: Sink
  82: Apple
  83: Air Conditioner
  84: Knife
  85: Hockey Stick
  86: Paddle
  87: Pickup Truck
  88: Fork
  89: Traffic Sign
  90: Balloon
  91: Tripod
  92: Dog
  93: Spoon
  94: Clock
  95: Pot
  96: Cow
  97: Cake
  98: Dinning Table
  99: Sheep
  100: Hanger
  101: Blackboard/Whiteboard
  102: Napkin
  103: Other Fish
  104: Orange/Tangerine
  105: Toiletry
  106: Keyboard
  107: Tomato
  108: Lantern
  109: Machinery Vehicle
  110: Fan
  111: Green Vegetables
  112: Banana
  113: Baseball Glove
  114: Airplane
  115: Mouse
  116: Train
  117: Pumpkin
  118: Soccer
  119: Skiboard
  120: Luggage
  121: Nightstand
  122: Tea pot
  123: Telephone
  124: Trolley
  125: Head Phone
  126: Sports Car
  127: Stop Sign
  128: Dessert
  129: Scooter
  130: Stroller
  131: Crane
  132: Remote
  133: Refrigerator
  134: Oven
  135: Lemon
  136: Duck
  137: Baseball Bat
  138: Surveillance Camera
  139: Cat
  140: Jug
  141: Broccoli
  142: Piano
  143: Pizza
  144: Elephant
  145: Skateboard
  146: Surfboard
  147: Gun
  148: Skating and Skiing shoes
  149: Gas stove
  150: Donut
  151: Bow Tie
  152: Carrot
  153: Toilet
  154: Kite
  155: Strawberry
  156: Other Balls
  157: Shovel
  158: Pepper
  159: Computer Box
  160: Toilet Paper
  161: Cleaning Products
  162: Chopsticks
  163: Microwave
  164: Pigeon
  165: Baseball
  166: Cutting/chopping Board
  167: Coffee Table
  168: Side Table
  169: Scissors
  170: Marker
  171: Pie
  172: Ladder
  173: Snowboard
  174: Cookies
  175: Radiator
  176: Fire Hydrant
  177: Basketball
  178: Zebra
  179: Grape
  180: Giraffe
  181: Potato
  182: Sausage
  183: Tricycle
  184: Violin
  185: Egg
  186: Fire Extinguisher
  187: Candy
  188: Fire Truck
  189: Billiards
  190: Converter
  191: Bathtub
  192: Wheelchair
  193: Golf Club
  194: Briefcase
  195: Cucumber
  196: Cigar/Cigarette
  197: Paint Brush
  198: Pear
  199: Heavy Truck
  200: Hamburger
  201: Extractor
  202: Extension Cord
  203: Tong
  204: Tennis Racket
  205: Folder
  206: American Football
  207: earphone
  208: Mask
  209: Kettle
  210: Tennis
  211: Ship
  212: Swing
  213: Coffee Machine
  214: Slide
  215: Carriage
  216: Onion
  217: Green beans
  218: Projector
  219: Frisbee
  220: Washing Machine/Drying Machine
  221: Chicken
  222: Printer
  223: Watermelon
  224: Saxophone
  225: Tissue
  226: Toothbrush
  227: Ice cream
  228: Hot-air balloon
  229: Cello
  230: French Fries
  231: Scale
  232: Trophy
  233: Cabbage
  234: Hot dog
  235: Blender
  236: Peach
  237: Rice
  238: Wallet/Purse
  239: Volleyball
  240: Deer
  241: Goose
  242: Tape
  243: Tablet
  244: Cosmetics
  245: Trumpet
  246: Pineapple
  247: Golf Ball
  248: Ambulance
  249: Parking meter
  250: Mango
  251: Key
  252: Hurdle
  253: Fishing Rod
  254: Medal
  255: Flute
  256: Brush
  257: Penguin
  258: Megaphone
  259: Corn
  260: Lettuce
  261: Garlic
  262: Swan
  263: Helicopter
  264: Green Onion
  265: Sandwich
  266: Nuts
  267: Speed Limit Sign
  268: Induction Cooker
  269: Broom
  270: Trombone
  271: Plum
  272: Rickshaw
  273: Goldfish
  274: Kiwi fruit
  275: Router/modem
  276: Poker Card
  277: Toaster
  278: Shrimp
  279: Sushi
  280: Cheese
  281: Notepaper
  282: Cherry
  283: Pliers
  284: CD
  285: Pasta
  286: Hammer
  287: Cue
  288: Avocado
  289: Hamimelon
  290: Flask
  291: Mushroom
  292: Screwdriver
  293: Soap
  294: Recorder
  295: Bear
  296: Eggplant
  297: Board Eraser
  298: Coconut
  299: Tape Measure/Ruler
  300: Pig
  301: Showerhead
  302: Globe
  303: Chips
  304: Steak
  305: Crosswalk Sign
  306: Stapler
  307: Camel
  308: Formula 1
  309: Pomegranate
  310: Dishwasher
  311: Crab
  312: Hoverboard
  313: Meat ball
  314: Rice Cooker
  315: Tuba
  316: Calculator
  317: Papaya
  318: Antelope
  319: Parrot
  320: Seal
  321: Butterfly
  322: Dumbbell
  323: Donkey
  324: Lion
  325: Urinal
  326: Dolphin
  327: Electric Drill
  328: Hair Dryer
  329: Egg tart
  330: Jellyfish
  331: Treadmill
  332: Lighter
  333: Grapefruit
  334: Game board
  335: Mop
  336: Radish
  337: Baozi
  338: Target
  339: French
  340: Spring Rolls
  341: Monkey
  342: Rabbit
  343: Pencil Case
  344: Yak
  345: Red Cabbage
  346: Binoculars
  347: Asparagus
  348: Barbell
  349: Scallop
  350: Noddles
  351: Comb
  352: Dumpling
  353: Oyster
  354: Table Tennis paddle
  355: Cosmetics Brush/Eyeliner Pencil
  356: Chainsaw
  357: Eraser
  358: Lobster
  359: Durian
  360: Okra
  361: Lipstick
  362: Cosmetics Mirror
  363: Curling
  364: Table Tennis

# Download script/URL (optional) ---------------------------------------------------------------------------------------
download: |
  from tqdm import tqdm

  from ultralytics.utils.checks import check_requirements
  from ultralytics.utils.downloads import download
  from ultralytics.utils.ops import xyxy2xywhn

  import numpy as np
  from pathlib import Path

  check_requirements(('pycocotools>=2.0',))
  from pycocotools.coco import COCO

  # Make Directories
  dir = Path(yaml['path'])  # dataset root dir
  for p in 'images', 'labels':
      (dir / p).mkdir(parents=True, exist_ok=True)
      for q in 'train', 'val':
          (dir / p / q).mkdir(parents=True, exist_ok=True)

  # Train, Val Splits
  for split, patches in [('train', 50 + 1), ('val', 43 + 1)]:
      print(f"Processing {split} in {patches} patches ...")
      images, labels = dir / 'images' / split, dir / 'labels' / split

      # Download
      url = f"https://dorc.ks3-cn-beijing.ksyun.com/data-set/2020Objects365%E6%95%B0%E6%8D%AE%E9%9B%86/{split}/"
      if split == 'train':
          download([f'{url}zhiyuan_objv2_{split}.tar.gz'], dir=dir)  # annotations json
          download([f'{url}patch{i}.tar.gz' for i in range(patches)], dir=images, curl=True, threads=8)
      elif split == 'val':
          download([f'{url}zhiyuan_objv2_{split}.json'], dir=dir)  # annotations json
          download([f'{url}images/v1/patch{i}.tar.gz' for i in range(15 + 1)], dir=images, curl=True, threads=8)
          download([f'{url}images/v2/patch{i}.tar.gz' for i in range(16, patches)], dir=images, curl=True, threads=8)

      # Move
      for f in tqdm(images.rglob('*.jpg'), desc=f'Moving {split} images'):
          f.rename(images / f.name)  # move to /images/{split}

      # Labels
      coco = COCO(dir / f'zhiyuan_objv2_{split}.json')
      names = [x["name"] for x in coco.loadCats(coco.getCatIds())]
      for cid, cat in enumerate(names):
          catIds = coco.getCatIds(catNms=[cat])
          imgIds = coco.getImgIds(catIds=catIds)
          for im in tqdm(coco.loadImgs(imgIds), desc=f'Class {cid + 1}/{len(names)} {cat}'):
              width, height = im["width"], im["height"]
              path = Path(im["file_name"])  # image filename
              try:
                  with open(labels / path.with_suffix('.txt').name, 'a') as file:
                      annIds = coco.getAnnIds(imgIds=im["id"], catIds=catIds, iscrowd=None)
                      for a in coco.loadAnns(annIds):
                          x, y, w, h = a['bbox']  # bounding box in xywh (xy top-left corner)
                          xyxy = np.array([x, y, x + w, y + h])[None]  # pixels(1,4)
                          x, y, w, h = xyxy2xywhn(xyxy, w=width, h=height, clip=True)[0]  # normalized and clipped
                          file.write(f"{cid} {x:.5f} {y:.5f} {w:.5f} {h:.5f}\n")
              except Exception as e:
                  print(e)

使用方法

要在图像大小为 640 的 Objects365 数据集上训练YOLOv8n 模型 100 次,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。

列车示例

from ultralytics import YOLO

# Load a model
model = YOLO("yolov8n.pt")  # load a pretrained model (recommended for training)

# Train the model
results = model.train(data="Objects365.yaml", epochs=100, imgsz=640)
# Start training from a pretrained *.pt model
yolo detect train data=Objects365.yaml model=yolov8n.pt epochs=100 imgsz=640

样本数据和注释

Objects365 数据集包含一组不同的高分辨率图像,其中有 365 个类别的物体,为物体检测任务提供了丰富的背景信息。以下是该数据集中的一些图像示例:

数据集样本图像

  • 物体 365:该图像展示了一个物体检测实例,其中的物体都标注了边界框。该数据集提供了大量图像,有助于为这项任务开发模型。

该示例展示了 Objects365 数据集中数据的多样性和复杂性,并强调了准确的物体检测对于计算机视觉应用的重要性。

引文和致谢

如果您在研究或开发工作中使用 Objects365 数据集,请引用以下论文:

@inproceedings{shao2019objects365,
  title={Objects365: A Large-scale, High-quality Dataset for Object Detection},
  author={Shao, Shuai and Li, Zeming and Zhang, Tianyuan and Peng, Chao and Yu, Gang and Li, Jing and Zhang, Xiangyu and Sun, Jian},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={8425--8434},
  year={2019}
}

我们在此向创建和维护 Objects365 数据集的研究团队表示感谢,该数据集是计算机视觉研究界的宝贵资源。有关 Objects365 数据集及其创建者的更多信息,请访问Objects365 数据集网站。

常见问题

Objects365 数据集的用途是什么?

Objects365 数据集专为机器学习和计算机视觉中的物体检测任务而设计。它提供了一个大规模、高质量的数据集,包含 200 万张注释图像和 3,000 万个边界框,涵盖 365 个类别。利用这样一个多样化的数据集,有助于提高物体检测模型的性能和通用性,使其成为该领域研究和开发的无价之宝。

如何在 Objects365 数据集上训练YOLOv8 模型?

要使用 Objects365 数据集训练YOLOv8n 模型 100 次,图像大小为 640,请按照以下说明操作:

列车示例

from ultralytics import YOLO

# Load a model
model = YOLO("yolov8n.pt")  # load a pretrained model (recommended for training)

# Train the model
results = model.train(data="Objects365.yaml", epochs=100, imgsz=640)
# Start training from a pretrained *.pt model
yolo detect train data=Objects365.yaml model=yolov8n.pt epochs=100 imgsz=640

有关可用参数的完整列表,请参阅 "培训"页面。

为什么要在对象检测项目中使用 Objects365 数据集?

Objects365 数据集为物体检测任务提供了多项优势: 1.多样性:该数据集包含 200 万张不同场景下的物体图像,涵盖 365 个类别。 2.高质量注释:3,000 多万个边界框提供全面的地面实况数据。 3.性能:在 Objects365 上预先训练的模型明显优于在 ImageNet 等数据集上训练的模型,从而获得更好的泛化效果。

在哪里可以找到 Objects365 数据集的 YAML 配置文件?

Objects365 数据集的 YAML 配置文件位于Objects365.yaml。该文件包含数据集路径和类标签等基本信息,对于设置训练环境至关重要。

Objects365 的数据集结构如何增强物体检测建模?

Objects365 数据集包含 200 万张高分辨率图像和超过 3000 万个边界框的全面注释。这种结构可确保为训练物体检测的深度学习模型提供一个强大的数据集,提供各种各样的物体和场景。这种多样性和数据量有助于开发出更准确、更能推广到现实世界应用中的模型。有关数据集结构的更多详情,请参阅数据集 YAML部分。



创建于 2023-11-12,更新于 2024-07-04
作者:glenn-jocher(6),Laughing-q(1)

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