|
|
|
|
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
|
|
|
|
# Objects365 dataset https://www.objects365.org/ by Megvii
|
|
|
|
|
# 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.yolo.utils.checks import check_requirements
|
|
|
|
|
from ultralytics.yolo.utils.downloads import download
|
|
|
|
|
from ultralytics.yolo.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)
|
