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+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
+# Objects365 dataset https://www.objects365.org/ by Megvii
+# Example usage: python train.py --data Objects365.yaml
+# parent
+# ├── yolov5
+# └── 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:
+: Person
+: Sneakers
+: Chair
+: Other Shoes
+: Hat
+: Car
+: Lamp
+: Glasses
+: Bottle
+: Desk
+: Cup
+: Street Lights
+: Cabinet/shelf
+: Handbag/Satchel
+: Bracelet
+: Plate
+: Picture/Frame
+: Helmet
+: Book
+: Gloves
+: Storage box
+: Boat
+: Leather Shoes
+: Flower
+: Bench
+: Potted Plant
+: Bowl/Basin
+: Flag
+: Pillow
+: Boots
+: Vase
+: Microphone
+: Necklace
+: Ring
+: SUV
+: Wine Glass
+: Belt
+: Monitor/TV
+: Backpack
+: Umbrella
+: Traffic Light
+: Speaker
+: Watch
+: Tie
+: Trash bin Can
+: Slippers
+: Bicycle
+: Stool
+: Barrel/bucket
+: Van
+: Couch
+: Sandals
+: Basket
+: Drum
+: Pen/Pencil
+: Bus
+: Wild Bird
+: High Heels
+: Motorcycle
+: Guitar
+: Carpet
+: Cell Phone
+: Bread
+: Camera
+: Canned
+: Truck
+: Traffic cone
+: Cymbal
+: Lifesaver
+: Towel
+: Stuffed Toy
+: Candle
+: Sailboat
+: Laptop
+: Awning
+: Bed
+: Faucet
+: Tent
+: Horse
+: Mirror
+: Power outlet
+: Sink
+: Apple
+: Air Conditioner
+: Knife
+: Hockey Stick
+: Paddle
+: Pickup Truck
+: Fork
+: Traffic Sign
+: Balloon
+: Tripod
+: Dog
+: Spoon
+: Clock
+: Pot
+: Cow
+: Cake
+: Dinning Table
+: Sheep
+: Hanger
+: Blackboard/Whiteboard
+: Napkin
+: Other Fish
+: Orange/Tangerine
+: Toiletry
+: Keyboard
+: Tomato
+: Lantern
+: Machinery Vehicle
+: Fan
+: Green Vegetables
+: Banana
+: Baseball Glove
+: Airplane
+: Mouse
+: Train
+: Pumpkin
+: Soccer
+: Skiboard
+: Luggage
+: Nightstand
+: Tea pot
+: Telephone
+: Trolley
+: Head Phone
+: Sports Car
+: Stop Sign
+: Dessert
+: Scooter
+: Stroller
+: Crane
+: Remote
+: Refrigerator
+: Oven
+: Lemon
+: Duck
+: Baseball Bat
+: Surveillance Camera
+: Cat
+: Jug
+: Broccoli
+: Piano
+: Pizza
+: Elephant
+: Skateboard
+: Surfboard
+: Gun
+: Skating and Skiing shoes
+: Gas stove
+: Donut
+: Bow Tie
+: Carrot
+: Toilet
+: Kite
+: Strawberry
+: Other Balls
+: Shovel
+: Pepper
+: Computer Box
+: Toilet Paper
+: Cleaning Products
+: Chopsticks
+: Microwave
+: Pigeon
+: Baseball
+: Cutting/chopping Board
+: Coffee Table
+: Side Table
+: Scissors
+: Marker
+: Pie
+: Ladder
+: Snowboard
+: Cookies
+: Radiator
+: Fire Hydrant
+: Basketball
+: Zebra
+: Grape
+: Giraffe
+: Potato
+: Sausage
+: Tricycle
+: Violin
+: Egg
+: Fire Extinguisher
+: Candy
+: Fire Truck
+: Billiards
+: Converter
+: Bathtub
+: Wheelchair
+: Golf Club
+: Briefcase
+: Cucumber
+: Cigar/Cigarette
+: Paint Brush
+: Pear
+: Heavy Truck
+: Hamburger
+: Extractor
+: Extension Cord
+: Tong
+: Tennis Racket
+: Folder
+: American Football
+: earphone
+: Mask
+: Kettle
+: Tennis
+: Ship
+: Swing
+: Coffee Machine
+: Slide
+: Carriage
+: Onion
+: Green beans
+: Projector
+: Frisbee
+: Washing Machine/Drying Machine
+: Chicken
+: Printer
+: Watermelon
+: Saxophone
+: Tissue
+: Toothbrush
+: Ice cream
+: Hot-air balloon
+: Cello
+: French Fries
+: Scale
+: Trophy
+: Cabbage
+: Hot dog
+: Blender
+: Peach
+: Rice
+: Wallet/Purse
+: Volleyball
+: Deer
+: Goose
+: Tape
+: Tablet
+: Cosmetics
+: Trumpet
+: Pineapple
+: Golf Ball
+: Ambulance
+: Parking meter
+: Mango
+: Key
+: Hurdle
+: Fishing Rod
+: Medal
+: Flute
+: Brush
+: Penguin
+: Megaphone
+: Corn
+: Lettuce
+: Garlic
+: Swan
+: Helicopter
+: Green Onion
+: Sandwich
+: Nuts
+: Speed Limit Sign
+: Induction Cooker
+: Broom
+: Trombone
+: Plum
+: Rickshaw
+: Goldfish
+: Kiwi fruit
+: Router/modem
+: Poker Card
+: Toaster
+: Shrimp
+: Sushi
+: Cheese
+: Notepaper
+: Cherry
+: Pliers
+: CD
+: Pasta
+: Hammer
+: Cue
+: Avocado
+: Hamimelon
+: Flask
+: Mushroom
+: Screwdriver
+: Soap
+: Recorder
+: Bear
+: Eggplant
+: Board Eraser
+: Coconut
+: Tape Measure/Ruler
+: Pig
+: Showerhead
+: Globe
+: Chips
+: Steak
+: Crosswalk Sign
+: Stapler
+: Camel
+: Formula 1
+: Pomegranate
+: Dishwasher
+: Crab
+: Hoverboard
+: Meat ball
+: Rice Cooker
+: Tuba
+: Calculator
+: Papaya
+: Antelope
+: Parrot
+: Seal
+: Butterfly
+: Dumbbell
+: Donkey
+: Lion
+: Urinal
+: Dolphin
+: Electric Drill
+: Hair Dryer
+: Egg tart
+: Jellyfish
+: Treadmill
+: Lighter
+: Grapefruit
+: Game board
+: Mop
+: Radish
+: Baozi
+: Target
+: French
+: Spring Rolls
+: Monkey
+: Rabbit
+: Pencil Case
+: Yak
+: Red Cabbage
+: Binoculars
+: Asparagus
+: Barbell
+: Scallop
+: Noddles
+: Comb
+: Dumpling
+: Oyster
+: Table Tennis paddle
+: Cosmetics Brush/Eyeliner Pencil
+: Chainsaw
+: Eraser
+: Lobster
+: Durian
+: Okra
+: Lipstick
+: Cosmetics Mirror
+: Curling
+: Table Tennis
+# Download script/URL (optional) ---------------------------------------------------------------------------------------
+download: |
+  from tqdm import tqdm
+  from utils.general import Path, check_requirements, download, np, xyxy2xywhn
+  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, delete=False)  # annotations json
+          download([f'{url}patch{i}.tar.gz' for i in range(patches)], dir=images, curl=True, delete=False, threads=8)
+      elif split == 'val':
+          download([f'{url}zhiyuan_objv2_{split}.json'], dir=dir, delete=False)  # annotations json
+          download([f'{url}images/v1/patch{i}.tar.gz' for i in range(15 + 1)], dir=images, curl=True, delete=False, threads=8)
+          download([f'{url}images/v2/patch{i}.tar.gz' for i in range(16, patches)], dir=images, curl=True, delete=False, 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=False)
+                      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)