| --- |
| 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 |
|
|
|
|
