Datasets:
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language:
- en
license: "agpl-3.0"
task_categories:
- object-detection
tags:
- yolo
- coco
- object-detection
- computer-vision
pretty_name: COCO8
size_categories:
- "n<1K"
dataset_info:
config_name: coco8
features:
- name: image
dtype: image
- name: objects
dtype:
sequence:
- name: bbox
dtype:
sequence: float32
- name: categories
dtype:
class_label:
names:
0: person
1: bicycle
2: car
3: motorcycle
4: airplane
5: bus
6: train
7: truck
8: boat
9: traffic light
10: fire hydrant
11: stop sign
12: parking meter
13: bench
14: bird
15: cat
16: dog
17: horse
18: sheep
19: cow
20: elephant
21: bear
22: zebra
23: giraffe
24: backpack
25: umbrella
26: handbag
27: tie
28: suitcase
29: frisbee
30: skis
31: snowboard
32: sports ball
33: kite
34: baseball bat
35: baseball glove
36: skateboard
37: surfboard
38: tennis racket
39: bottle
40: wine glass
41: cup
42: fork
43: knife
44: spoon
45: bowl
46: banana
47: apple
48: sandwich
49: orange
50: broccoli
51: carrot
52: hot dog
53: pizza
54: donut
55: cake
56: chair
57: couch
58: potted plant
59: bed
60: dining table
61: toilet
62: tv
63: laptop
64: mouse
65: remote
66: keyboard
67: cell phone
68: microwave
69: oven
70: toaster
71: sink
72: refrigerator
73: book
74: clock
75: vase
76: scissors
77: teddy bear
78: hair drier
79: toothbrush
configs:
- config_name: coco8
data_files:
- split: train
path: images/train/*
- split: validation
path: images/val/*
default: true
---
# Ultralytics COCO8 数据集
Ultralytics COCO8 是一个规模虽小但用途广泛的目标检测数据集，由 COCO train2017
的前 8 张图像组成，其中 4 张用于训练，4 张用于验证。该数据集非常适合用于测试和调试目标检测模型，
或用于尝试新的检测方法。虽然只有8张图像，规模小巧便于管理，但
其多样性足以用于检测训练流程中的错误，并在训练更大规模的数据集之前作为合理性检查。
该数据集旨在与Ultralytics YOLOv8配合使用。
## 仓库结构
```text
images/train/ # 4 张训练图片 + metadata.jsonl
images/val/ # 4 张验证图片 + metadata.jsonl
labels/train|val/ # 原始 YOLO 格式 .txt 标注（归一化 cx cy w h）
data.yaml # Ultralytics 数据配置
```
每个 split 目录下的 `metadata.jsonl` 提供目标检测标注（像素坐标 `[x, y, width, height]`，
左上角原点），可直接被 Hugging Face Dataset Viewer 与 `datasets` 库识别
（目录名 `train`/`val` 会被自动映射为 train/validation 两个 split）：
```jsonl
{"file_name": "000000000009.jpg", "objects": {"bbox": [[1.08, 187.69, 611.59, 285.84], ...], "categories": [45, ...]}}
```
## 使用方法
使用 🤗 Datasets 加载：
```python
from datasets import load_dataset
ds = load_dataset("cc92yy3344/coco8")
print(ds["train"][0]["objects"])
# {'bbox': [[1.08, 187.69, 611.59, 285.84], ...], 'categories': [45, ...]}
```
文档: https://docs.ultralytics.com
社区: https://community.ultralytics.com
GitHub: https://github.com/ultralytics/ultralytics
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