Link to this sectionObjects365 数据集#
Objects365 数据集是一个大规模目标检测基准，包含 1,742,289 张训练图像和 80,000 张验证图像，涵盖 365 个目标类别——从人、汽车和椅子到瓶子、狗和路灯。它由 Megvii 研究人员创建并在 ICCV 2019 上发表，侧重于现实世界中的多样化目标，被广泛用于预训练计算机视觉模型，这些模型比使用 ImageNet 预训练的模型具有更好的泛化能力。
Watch: How to Train Ultralytics YOLO on the Objects365 Dataset
Link to this section主要特性#
- Objects365 定义了 365 个目标类别，上游版本报告总共约有 200 万张图像和 3000 万个边界框。
- 该数据集包含各种现实场景中的多样化目标，为目标检测任务提供了一个丰富且具有挑战性的基准。
- 标注内容包括对象的边界框，使其适用于训练和评估目标检测模型。
- 根据 ICCV 2019 论文，在 COCO 基准测试中，Objects365 预训练比 ImageNet 预训练的效果高出 5.6 个点（42.0 对比 36.4 mAP）。
Link to this section数据集结构#
Ultralytics 的 Objects365.yaml 配置文件定义了两个拆分集：
| 拆分 | 图像 | 描述 |
|---|---|---|
| 训练 | 1,742,289 | 用于模型训练的标注图像 |
| 验证 | 80,000 | 用于评估和基准测试的预留图像 |
下载过程会获取训练集和验证集（总共 1,822,289 张图像），配置中的 test: 键保持为空。
Link to this section应用#
Objects365 数据集支持多种目标检测领域的深度学习应用：
- 预训练检测骨干网络：凭借 365 个类别和密集的框标注，Objects365 预训练能够改善在 COCO 和 VOC 等较小数据集上的下游微调效果。
- 零售与库存识别：数百种日常类别（如瓶子、杯子、运动鞋、手提包）支持货架监控和自动结账系统。
- 机器人与智能环境：广泛的家庭和街道目标覆盖范围，有助于机器人和智能摄像头识别非结构化场景中的物体。
- 检测器基准测试：庞大的类别列表和真实场景图像，使其成为评估检测模型泛化能力的一项严苛基准。
若要在浏览器中标注你自己的图像、训练并管理大规模数据集，请使用 Ultralytics Platform 运行完整的工作流程。
Link to this section数据集 YAML#
Objects365.yaml 文件定义了数据集配置，包括数据集路径、类别名称和其他元数据。它托管在 Ultralytics 仓库中，地址为 https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/Objects365.yaml。
ultralytics/cfg/datasets/Objects365.yaml
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/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: 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: Dining 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: Hami melon
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: Meatball
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 concurrent.futures import ThreadPoolExecutor
from pathlib import Path
import numpy as np
from ultralytics.utils import TQDM
from ultralytics.utils.checks import check_requirements
from ultralytics.utils.downloads import download
from ultralytics.utils.ops import xyxy2xywhn
check_requirements("faster-coco-eval")
from faster_coco_eval import COCO
# Train, Val Splits
dir = Path(yaml["path"])
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
images.mkdir(parents=True, exist_ok=True)
labels.mkdir(parents=True, exist_ok=True)
# 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, threads=17) # 51 patches / 17 threads = 3
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, threads=16)
download([f"{url}images/v2/patch{i}.tar.gz" for i in range(16, patches)], dir=images, threads=16)
# Move
files = list(images.rglob("*.jpg"))
with ThreadPoolExecutor(max_workers=16) as executor:
list(TQDM(executor.map(lambda f: f.rename(images / f.name), files), total=len(files), desc=f"Moving {split} images"))
# 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)
def process_annotation(im):
"""Process and write annotations for a single image."""
try:
width, height = im["width"], im["height"]
path = Path(im["file_name"])
with open(labels / path.with_suffix(".txt").name, "a", encoding="utf-8") 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)
images_list = coco.loadImgs(imgIds)
with ThreadPoolExecutor(max_workers=16) as executor:
list(TQDM(executor.map(process_annotation, images_list), total=len(images_list), desc=f"Class {cid + 1}/{len(names)} {cat}"))Link to this section用法#
712 GB 下载量
Objects365 会在首次使用时自动下载，需要约 712 GB 的可用磁盘空间——包括 345 GB 的下载压缩包以及 367 GB 用于解压后的数据集。下载脚本会安装 faster-coco-eval 包并将标注转换为 YOLO 格式，这可能需要很长时间，具体取决于你的网络连接和硬件性能。
要使用 640 的图像尺寸在 Objects365 数据集上训练 YOLO26n 模型 100 个 epochs，你可以使用以下代码片段。有关可用参数的完整列表，请参考模型 Training 页面。
训练示例
from ultralytics import YOLO
# Load a model
model = YOLO("yolo26n.pt") # load a pretrained model (recommended for training)
# Train the model
results = model.train(data="Objects365.yaml", epochs=100, imgsz=640)Link to this section样本图像和标注#
Objects365 数据集包含多样化的高分辨率图像，其 365 个类别中具有密集的边界框标注。下面的示例展示了该数据集典型的真实场景和多目标标注：
Link to this section引用与致谢#
如果你在研究或开发工作中使用 Objects365 数据集，请引用以下论文：
引用
@InProceedings{Shao_2019_ICCV,
author = {Shao, Shuai and Li, Zeming and Zhang, Tianyuan and Peng, Chao and Yu, Gang and Zhang, Xiangyu and Li, Jing and Sun, Jian},
title = {Objects365: A Large-Scale, High-Quality Dataset for Object Detection},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2019}
}我们衷心感谢创建并维护 Objects365 数据集的研究团队，他们为计算机视觉研究社区贡献了宝贵的资源。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。
Link to this section常见问题解答#
Link to this sectionObjects365 数据集有什么用途？#
Objects365 数据集用于在机器学习和计算机视觉中训练和评估目标检测模型。它提供了 1,742,289 张训练图像和 80,000 张验证图像，涵盖 365 个目标类别，特别适用于预训练检测器，随后可在较小的、特定任务的数据集上进行微调。
Link to this sectionObjects365 数据集中有多少图像和类别？#
Ultralytics 的 Objects365.yaml 配置文件涵盖 365 个目标类别，拆分为 1,742,289 张训练图像和 80,000 张验证图像（总计 1,822,289 张），没有测试拆分集。上游版本报告总共约有 200 万张图像和 3000 万个边界框。
Link to this sectionObjects365 数据集下载有多大？#
Objects365 需要大约 712 GB 的磁盘空间——其中约 345 GB 是当你首次使用 data="Objects365.yaml" 进行训练时自动下载的 zip 压缩包，另需 367 GB 空间用于解压数据集。下载脚本会安装 faster-coco-eval 包并将标注转换为 YOLO 格式。你可以在检测数据集概览中浏览较小的替代方案。
Link to this section如何使用 Objects365 数据集训练 YOLO26 模型？#
要使用 Objects365 数据集训练 YOLO26n 模型 100 个 epoch（图像尺寸为 640），请按照以下说明进行操作：
训练示例
from ultralytics import YOLO
# Load a model
model = YOLO("yolo26n.pt") # load a pretrained model (recommended for training)
# Train the model
results = model.train(data="Objects365.yaml", epochs=100, imgsz=640)请参阅 Training 页面获取可用参数的完整列表。
Link to this section为什么我应该在目标检测项目中使用 Objects365 数据集？#
Objects365 的 365 类词汇表和密集标注使其成为最强大的目标检测预训练数据集之一——ICCV 2019 论文报告称，在 COCO 上使用它进行预训练比使用 ImageNet 预训练有 5.6 个点的提升（42.0 对比 36.4 mAP）。其图像涵盖了多样化的现实场景，这有助于模型在下游检测任务中展现出良好的泛化能力。
Link to this section在哪里可以找到 Objects365 数据集的 YAML 配置文件？#
Objects365 数据集的 YAML 配置文件可在 Objects365.yaml 找到。此文件包含数据集路径和类标签等基本信息，这对于设置训练环境至关重要。