---
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