---
title: Objects365
canonical_url: "https://www.modelscope.cn/datasets/OpenDataLab/Objects365"
md_url: "https://www.modelscope.cn/datasets/OpenDataLab/Objects365.md"
repository: OpenDataLab/Objects365
last_updated: 2025-02-27
license: "[CC BY 4.0]"
storage_size: "382 GB"
domain:
  - publishDate
  - labelTypes
  - publishUrl
  - mediaTypes
  - publisher
  - taskTypes
  - displayName
tasks:
  - 2019-05-10
  - Box2D
  - "https://www.objects365.org/overview.html"
  - Image
  - "Megvii Technology"
  - "Object Detection"
  - "Objects365 v2"
downloads: 27359
stars: 4
---

# Objects365

> Objects365 - OpenDataLab 在 ModelScope 开源的数据集。displayName: Objects365 v2 labelTypes: Box2D license: CC BY 4.0 mediaTypes: Image paperUrl: "" publishDate: "2019-05-10" publishUrl: https://www.objects365.org/overview.html publisher: Megvii Technology tags: []…

OpenDataLab/Objects365 是 ModelScope 魔搭社区上的2019-05-10、Box2D、https://www.objects365.org/overview.html数据集，涉及 publishDate、labelTypes、publishUrl 领域，存储大小 382 GB，采用 [CC BY 4.0] 许可。

- **Repository**: OpenDataLab/Objects365
- **License**: [CC BY 4.0]
- **Tasks**: 2019-05-10, Box2D, https://www.objects365.org/overview.html, Image, Megvii Technology, Object Detection, Objects365 v2
- **Domain**: publishDate, labelTypes, publishUrl, mediaTypes, publisher, taskTypes, displayName
- **Storage size**: 382 GB
- **Downloads**: 27359
- **Stars**: 4
- **Last updated**: 2025-02-27

Source: https://www.modelscope.cn/datasets/OpenDataLab/Objects365

---

displayName: Objects365 v2
labelTypes:
- Box2D
license:
- CC BY 4.0
mediaTypes:
- Image
paperUrl: ""
publishDate: "2019-05-10"
publishUrl: https://www.objects365.org/overview.html
publisher:
- Megvii Technology
tags: []
taskTypes:
- Object Detection

---
# 数据集介绍
  ## 简介
  Objects365 是一个全新的数据集，旨在促进目标检测研究，重点关注野外的各种物体。
365个类别
200万张图片
3000万个边界框
Objects365 Consortium 不拥有图像的版权。 图片的使用必须遵守 Flickr 使用条款。 图像的用户对数据集的使用承担全部责任，包括但不限于使用他们可能从数据集创建的受版权保护的图像的任何副本。 您不会分发上述图片。 如果侵犯图片版权，我们将立即删除图片。
  ## 类定义
  ```
1:Person
2:Sneakers
3:Chair
4:Other Shoes
5:Hat
6:Car
7:Lamp
8:Glasses
9:Bottle
10:Desk
11:Cup
12:Street Lights
13:Cabinet/shelf
14:Handbag/Satchel
15:Bracelet
16:Plate
17:Picture/Frame
18:Helmet
19:Book
20:Gloves
21:Storage box
22:Boat
23:Leather Shoes
24:Flower
25:Bench
26:Potted Plant
27:Bowl/Basin
28:Flag
29:Pillow
30:Boots
31:Vase
32:Microphone
33:Necklace
34:Ring
35:SUV
36:Wine Glass
37:Belt
38:Moniter/TV
39:Backpack
40:Umbrella
41:Traffic Light
42:Speaker
43:Watch
44:Tie
45:Trash bin Can
46:Slippers
47:Bicycle
48:Stool
49:Barrel/bucket
50:Van
51:Couch
52:Sandals
53:Bakset
54:Drum
55:Pen/Pencil
56:Bus
57:Wild Bird
58:High Heels
59:Motorcycle
60:Guitar
61:Carpet
62:Cell Phone
63:Bread
64:Camera
65:Canned
66:Truck
67:Traffic cone
68:Cymbal
69:Lifesaver
70:Towel
71:Stuffed Toy
72:Candle
73:Sailboat
74:Laptop
75:Awning
76:Bed
77:Faucet
78:Tent
79:Horse
80:Mirror
81:Power outlet
82:Sink
83:Apple
84:Air Conditioner
85:Knife
86:Hockey Stick
87:Paddle
88:Pickup Truck
89:Fork
90:Traffic Sign
91:Ballon
92:Tripod
93:Dog
94:Spoon
95:Clock
96:Pot
97:Cow
98:Cake
99:Dinning Table
100:Sheep
101:Hanger
102:Blackboard/Whiteboard
103:Napkin
104:Other Fish
105:Orange/Tangerine
106:Toiletry
107:Keyboard
108:Tomato
109:Lantern
110:Machinery Vehicle
111:Fan
112:Green Vegetables
113:Banana
114:Baseball Glove
115:Airplane
116:Mouse
117:Train
118:Pumpkin
119:Soccer
120:Skiboard
121:Luggage
122:Nightstand
123:Tea pot
124:Telephone
125:Trolley
126:Head Phone
127:Sports Car
128:Stop Sign
129:Dessert
130:Scooter
131:Stroller
132:Crane
133:Remote
134:Refrigerator
135:Oven
136:Lemon
137:Duck
138:Baseball Bat
139:Surveillance Camera
140:Cat
141:Jug
142:Broccoli
143:Piano
144:Pizza
145:Elephant
146:Skateboard
147:Surfboard
148:Gun
149:Skating and Skiing shoes
150:Gas stove
151:Donut
152:Bow Tie
153:Carrot
154:Toilet
155:Kite
156:Strawberry
157:Other Balls
158:Shovel
159:Pepper
160:Computer Box
161:Toilet Paper
162:Cleaning Products
163:Chopsticks
164:Microwave
165:Pigeon
166:Baseball
167:Cutting/chopping Board
168:Coffee Table
169:Side Table
170:Scissors
171:Marker
172:Pie
173:Ladder
174:Snowboard
175:Cookies
176:Radiator
177:Fire Hydrant
178:Basketball
179:Zebra
180:Grape
181:Giraffe
182:Potato
183:Sausage
184:Tricycle
185:Violin
186:Egg
187:Fire Extinguisher
188:Candy
189:Fire Truck
190:Billards
191:Converter
192:Bathtub
193:Wheelchair
194:Golf Club
195:Briefcase
196:Cucumber
197:Cigar/Cigarette
198:Paint Brush
199:Pear
200:Heavy Truck
201:Hamburger
202:Extractor
203:Extention Cord
204:Tong
205:Tennis Racket
206:Folder
207:American Football
208:earphone
209:Mask
210:Kettle
211:Tennis
212:Ship
213:Swing
214:Coffee Machine
215:Slide
216:Carriage
217:Onion
218:Green beans
219:Projector
220:Frisbee
221:Washing Machine/Drying Machine
222:Chicken
223:Printer
224:Watermelon
225:Saxophone
226:Tissue
227:Toothbrush
228:Ice cream
229:Hotair ballon
230:Cello
231:French Fries
232:Scale
233:Trophy
234:Cabbage
235:Hot dog
236:Blender
237:Peach
238:Rice
239:Wallet/Purse
240:Volleyball
241:Deer
242:Goose
243:Tape
244:Tablet
245:Cosmetics
246:Trumpet
247:Pineapple
248:Golf Ball
249:Ambulance
250:Parking meter
251:Mango
252:Key
253:Hurdle
254:Fishing Rod
255:Medal
256:Flute
257:Brush
258:Penguin
259:Megaphone
260:Corn
261:Lettuce
262:Garlic
263:Swan
264:Helicopter
265:Green Onion
266:Sandwich
267:Nuts
268:Speed Limit Sign
269:Induction Cooker
270:Broom
271:Trombone
272:Plum
273:Rickshaw
274:Goldfish
275:Kiwi fruit
276:Router/modem
277:Poker Card
278:Toaster
279:Shrimp
280:Sushi
281:Cheese
282:Notepaper
283:Cherry
284:Pliers
285:CD
286:Pasta
287:Hammer
288:Cue
289:Avocado
290:Hamimelon
291:Flask
292:Mushroon
293:Screwdriver
294:Soap
295:Recorder
296:Bear
297:Eggplant
298:Board Eraser
299:Coconut
300:Tape Measur/ Ruler
301:Pig
302:Showerhead
303:Globe
304:Chips
305:Steak
306:Crosswalk Sign
307:Stapler
308:Campel
309:Formula 1
310:Pomegranate
311:Dishwasher
312:Crab
313:Hoverboard
314:Meat ball
315:Rice Cooker
316:Tuba
317:Calculator
318:Papaya
319:Antelope
320:Parrot
321:Seal
322:Buttefly
323:Dumbbell
324:Donkey
325:Lion
326:Urinal
327:Dolphin
328:Electric Drill
329:Hair Dryer
330:Egg tart
331:Jellyfish
332:Treadmill
333:Lighter
334:Grapefruit
335:Game board
336:Mop
337:Radish
338:Baozi
339:Target
340:French
341:Spring Rolls
342:Monkey
343:Rabbit
344:Pencil Case
345:Yak
346:Red Cabbage
347:Binoculars
348:Asparagus
349:Barbell
350:Scallop
351:Noddles
352:Comb
353:Dumpling
354:Oyster
355:Table Teniis paddle
356:Cosmetics Brush/Eyeliner Pencil
357:Chainsaw
358:Eraser
359:Lobster
360:Durian
361:Okra
362:Lipstick
363:Cosmetics Mirror
364:Curling
365:Table Tennis
```
  ## 引文
  ```
@INPROCEEDINGS{9009553,
  author={Shao, Shuai and Li, Zeming and Zhang, Tianyuan and Peng, Chao and Yu, Gang and Zhang, Xiangyu and Li, Jing and Sun, Jian},
  booktitle={2019 IEEE/CVF International Conference on Computer Vision (ICCV)}, 
  title={Objects365: A Large-Scale, High-Quality Dataset for Object Detection}, 
  year={2019},
  volume={},
  number={},
  pages={8429-8438},
  doi={10.1109/ICCV.2019.00852}
}
```
  
## Download dataset
:modelscope-code[]{type="git"}