─░├žeri─če ge├ž

Objects365 Veri K├╝mesi

Objects365 veri k├╝mesi, vah┼či do─čadaki ├že┼čitli nesnelere odaklanarak nesne alg─▒lama ara┼čt─▒rmalar─▒n─▒ te┼čvik etmek i├žin tasarlanm─▒┼č b├╝y├╝k ├Âl├žekli, y├╝ksek kaliteli bir veri k├╝mesidir. Megvii ara┼čt─▒rmac─▒lar─▒ndan olu┼čan bir ekip taraf─▒ndan olu┼čturulan veri k├╝mesi, 365 nesne kategorisini kapsayan kapsaml─▒ bir a├ž─▒klamal─▒ s─▒n─▒rlay─▒c─▒ kutu seti ile ├žok ├že┼čitli y├╝ksek ├ž├Âz├╝n├╝rl├╝kl├╝ g├Âr├╝nt├╝ler sunar.

Temel ├ľzellikler

  • Objects365, 2 milyon g├Âr├╝nt├╝ ve 30 milyondan fazla s─▒n─▒rlay─▒c─▒ kutu ile 365 nesne kategorisi i├žerir.
  • Veri k├╝mesi, ├že┼čitli senaryolardaki farkl─▒ nesneleri i├žeriyor ve nesne alg─▒lama g├Ârevleri i├žin zengin ve zorlu bir ├Âl├ž├╝t sa─čl─▒yor.
  • Ek a├ž─▒klamalar nesneler i├žin s─▒n─▒rlay─▒c─▒ kutular i├žerir, bu da onu nesne alg─▒lama modellerini e─čitmek ve de─čerlendirmek i├žin uygun hale getirir.
  • Objects365 ├Ânceden e─čitilmi┼č modelleri, ImageNet ├Ânceden e─čitilmi┼č modellerinden ├Ânemli ├Âl├ž├╝de daha iyi performans g├Âstererek ├že┼čitli g├Ârevlerde daha iyi genelleme sa─člar.

Veri K├╝mesi Yap─▒s─▒

Objects365 veri k├╝mesi, ilgili ek a├ž─▒klamalarla birlikte tek bir g├Âr├╝nt├╝ k├╝mesi halinde d├╝zenlenmi┼čtir:

  • G├Âr├╝nt├╝ler: Veri k├╝mesi, her biri 365 kategoride ├že┼čitli nesneler i├žeren 2 milyon y├╝ksek ├ž├Âz├╝n├╝rl├╝kl├╝ g├Âr├╝nt├╝ i├žermektedir.
  • Ek a├ž─▒klamalar: G├Âr├╝nt├╝lere 30 milyondan fazla s─▒n─▒rlay─▒c─▒ kutu eklenmi┼čtir ve nesne alg─▒lama g├Ârevleri i├žin kapsaml─▒ temel ger├žek bilgileri sa─člar.

Uygulamalar

Objects365 veri k├╝mesi, nesne alg─▒lama g├Ârevlerinde derin ├Â─črenme modellerini e─čitmek ve de─čerlendirmek i├žin yayg─▒n olarak kullan─▒lmaktad─▒r. Veri setinin ├žok ├že┼čitli nesne kategorileri ve y├╝ksek kaliteli ek a├ž─▒klamalar─▒, onu bilgisayarla g├Ârme alan─▒ndaki ara┼čt─▒rmac─▒lar ve uygulay─▒c─▒lar i├žin de─čerli bir kaynak haline getirmektedir.

Veri K├╝mesi YAML

Veri k├╝mesi yap─▒land─▒rmas─▒n─▒ tan─▒mlamak i├žin bir YAML (Yet Another Markup Language) dosyas─▒ kullan─▒l─▒r. Veri k├╝mesinin yollar─▒, s─▒n─▒flar─▒ ve di─čer ilgili bilgiler hakk─▒nda bilgi i├žerir. Objects365 Veri K├╝mesi i├žin Objects365.yaml dosyas─▒ ┼ču adreste tutulur https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/Objects365.yaml.

ultralytics/cfg/datasets/Objects365.yaml

# Ultralytics YOLO ­čÜÇ, 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
# ÔöťÔöÇÔöÇ ultralytics
# ÔööÔöÇÔöÇ datasets
#     ÔööÔöÇÔöÇ Objects365  ÔćÉ 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)

Kullan─▒m

Bir YOLOv8n modelini Objects365 veri k├╝mesinde 640 g├Âr├╝nt├╝ boyutuyla 100 epok i├žin e─čitmek i├žin a┼ča─č─▒daki kod par├žac─▒klar─▒n─▒ kullanabilirsiniz. Kullan─▒labilir ba─č─▒ms─▒z de─či┼čkenlerin kapsaml─▒ bir listesi i├žin Model E─čitimi sayfas─▒na bak─▒n.

Tren ├ľrne─či

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

├ľrnek Veriler ve A├ž─▒klamalar

Objects365 veri k├╝mesi, nesne alg─▒lama g├Ârevleri i├žin zengin bir ba─člam sa─člayan 365 kategoriden nesneler i├žeren ├že┼čitli y├╝ksek ├ž├Âz├╝n├╝rl├╝kl├╝ g├Âr├╝nt├╝ler i├žerir. ─░┼čte veri k├╝mesindeki g├Âr├╝nt├╝lerden baz─▒ ├Ârnekler:

Veri k├╝mesi ├Ârnek g├Âr├╝nt├╝s├╝

  • Objects365: Bu g├Âr├╝nt├╝, nesnelerin s─▒n─▒rlay─▒c─▒ kutularla a├ž─▒kland─▒─č─▒ bir nesne alg─▒lama ├Ârne─čini g├Âstermektedir. Veri k├╝mesi, bu g├Ârev i├žin modellerin geli┼čtirilmesini kolayla┼čt─▒rmak ├╝zere geni┼č bir g├Âr├╝nt├╝ yelpazesi sunmaktad─▒r.

├ľrnek, Objects365 veri k├╝mesindeki verilerin ├že┼čitlili─čini ve karma┼č─▒kl─▒─č─▒n─▒ sergilemekte ve bilgisayarla g├Ârme uygulamalar─▒ i├žin do─čru nesne alg─▒laman─▒n ├Ânemini vurgulamaktad─▒r.

At─▒flar ve Te┼čekk├╝r

Objects365 veri setini ara┼čt─▒rma veya geli┼čtirme ├žal─▒┼čmalar─▒n─▒zda kullan─▒rsan─▒z, l├╝tfen a┼ča─č─▒daki makaleye at─▒fta bulunun:

@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}
}

Objects365 veri setini bilgisayarla g├Ârme ara┼čt─▒rma toplulu─ču i├žin de─čerli bir kaynak olarak yaratan ve s├╝rd├╝ren ara┼čt─▒rmac─▒ ekibine te┼čekk├╝r ederiz. Objects365 veri seti ve yarat─▒c─▒lar─▒ hakk─▒nda daha fazla bilgi i├žin Objects365 veri seti web sitesini ziyaret edin.



Created 2023-11-12, Updated 2024-06-02
Authors: glenn-jocher (5), Laughing-q (1)

Yorumlar