README.md

---
license: cc-by-4.0
pretty_name: "COCO-5k"
task_categories:
  - image-segmentation
  - object-detection
tags:
  - computer-vision
  - person
  - sign
  - child
  - bus
  - train
  - crowd
  - pictograph
annotations_creators:
  - expert-generated
size_categories:
  - 1K<n<10K
source_datasets:
  - extended
paperswithcode_id: coco
configs:
  - config_name: default
    data_files:
      - split: train
        path: "train/*"
---

[![COCO-5k - annotated computer-vision dataset on Pictograph](https://storage.googleapis.com/pictograph-assets/hf/heroes/coco-5k.png?v=6)](https://pictograph.io/datasets/pictograph-research/coco-5k)

[![Open in](https://img.shields.io/badge/Open_in-Pictograph-2c5aa0)](https://pictograph.io/datasets/pictograph-research/coco-5k) ![task](https://img.shields.io/badge/task-image_segmentation-1a4480) ![images](https://img.shields.io/badge/images-5,000-555) [![license](https://img.shields.io/badge/license-cc--by--4.0-3cb44b)](https://creativecommons.org/licenses/by/4.0/)

**[View on Pictograph](https://pictograph.io/datasets/pictograph-research/coco-5k)** · [Pictograph Research](https://pictograph.io/org/pictograph-research) · [Creative Commons Attribution 4.0](https://creativecommons.org/licenses/by/4.0/)

## About

**COCO-5k** is a computer-vision dataset curated and annotated on [Pictograph](https://pictograph.io/datasets/pictograph-research/coco-5k). The most common detected objects are person, sign, child, bus, train, crowd. On Pictograph you can browse every annotated image, fork it into your own workspace in one click, export it in a dozen formats, or train a model on it directly.

## At a glance

| Metric | Value |
| :--- | ---: |
| Images | 5,000 |
| Annotations | 36,335 |
| Classes | 80 |
| Annotation types | polygon, bounding box |
| Splits | train |

## Quick start

Load it in one line with the `datasets` library, then read each record's boxes and class names:

```python
from datasets import load_dataset
from PIL import ImageDraw

ds = load_dataset("pictograph/coco-5k", split="train")
example = ds[0]
image = example["image"]            # a PIL image
objects = example["objects"]        # {bbox, categories, category_names}

# draw every bounding box with its class name
draw = ImageDraw.Draw(image)
for (x, y, w, h), name in zip(objects["bbox"], objects["category_names"]):
    draw.rectangle([x, y, x + w, y + h], outline="red", width=3)
    draw.text((x, y - 12), name, fill="red")

# polygon masks live under example["segmentation"]:
#   [{"label": name, "category": idx, "points": [[x, y], ...]}, ...]
image.show()
```

Prefer a full annotation editor, one-click fork, multi-format export, and one-click training? [Open this dataset on Pictograph](https://pictograph.io/datasets/pictograph-research/coco-5k).

## Dataset structure

This dataset uses the Hugging Face `imagefolder` layout: each split directory holds the images plus a `metadata.jsonl` that links every image to its annotations by `file_name`.

| Field | Description |
| :--- | :--- |
| `file_name` | Path to the image within the split directory. |
| `objects.bbox` | Bounding boxes as `[x, y, width, height]` (pixels). |
| `objects.categories` | Integer class index per box (matches the class list below). |
| `objects.category_names` | Human class name per box. |
| `segmentation` | List of `{label, category, points}`; `points` is a polygon ring `[[x, y], ...]`. |

## Data instance

One record (bounding boxes are `[x, y, width, height]` in pixels; the class index maps into the class list below):

```python
{
  "image": <PIL.Image (RGB)>,
  "objects": {
    "bbox": [[172.0, 192.0, 249.4, 152.7]],
    "categories": [0],
    "category_names": ["person"]
  },
  "segmentation": [
    {"label": "person", "category": 0, "points": [[176.9, 207.2], [259.0, 274.1], ...]}
  ]
}
```

## Classes

Class index matches `objects.categories` in `metadata.jsonl`.

| # | Class | Annotations |
| ---: | :--- | ---: |
| 0 | airplane | 143 |
| 1 | apple | 236 |
| 2 | backpack | 371 |
| 3 | banana | 370 |
| 4 | baseball bat | 145 |
| 5 | baseball glove | 148 |
| 6 | bear | 71 |
| 7 | bed | 163 |
| 8 | bench | 411 |
| 9 | bicycle | 314 |
| 10 | bird | 427 |
| 11 | boat | 424 |
| 12 | book | 1,129 |
| 13 | bottle | 1,013 |
| 14 | bowl | 623 |
| 15 | broccoli | 312 |
| 16 | bus | 283 |
| 17 | cake | 310 |
| 18 | car | 1,918 |
| 19 | carrot | 365 |
| 20 | cat | 202 |
| 21 | cell phone | 262 |
| 22 | chair | 1,771 |
| 23 | clock | 267 |
| 24 | couch | 261 |
| 25 | cow | 372 |
| 26 | cup | 895 |
| 27 | dining table | 695 |
| 28 | dog | 218 |
| 29 | donut | 328 |
| 30 | elephant | 252 |
| 31 | fire hydrant | 101 |
| 32 | fork | 215 |
| 33 | frisbee | 115 |
| 34 | giraffe | 232 |
| 35 | hair drier | 11 |
| 36 | handbag | 540 |
| 37 | horse | 272 |
| 38 | hot dog | 125 |
| 39 | keyboard | 153 |
| 40 | kite | 327 |
| 41 | knife | 325 |
| 42 | laptop | 231 |
| 43 | microwave | 55 |
| 44 | motorcycle | 367 |
| 45 | mouse | 106 |
| 46 | orange | 285 |
| 47 | oven | 143 |
| 48 | parking meter | 60 |
| 49 | person | 10,777 |
| 50 | pizza | 284 |
| 51 | potted plant | 342 |
| 52 | refrigerator | 126 |
| 53 | remote | 283 |
| 54 | sandwich | 177 |
| 55 | scissors | 36 |
| 56 | sheep | 354 |
| 57 | sink | 225 |
| 58 | skateboard | 179 |
| 59 | skis | 241 |
| 60 | snowboard | 69 |
| 61 | spoon | 253 |
| 62 | sports ball | 260 |
| 63 | stop sign | 75 |
| 64 | suitcase | 299 |
| 65 | surfboard | 267 |
| 66 | teddy bear | 190 |
| 67 | tennis racket | 225 |
| 68 | tie | 252 |
| 69 | toaster | 9 |
| 70 | toilet | 179 |
| 71 | toothbrush | 57 |
| 72 | traffic light | 634 |
| 73 | train | 190 |
| 74 | truck | 414 |
| 75 | tv | 288 |
| 76 | umbrella | 407 |
| 77 | vase | 274 |
| 78 | wine glass | 341 |
| 79 | zebra | 266 |

## License

Released under [Creative Commons Attribution 4.0](https://creativecommons.org/licenses/by/4.0/). When you use this data, please credit **COCO Consortium (Lin et al., ECCV 2014)**.

## Source and attribution

This dataset is derived from [COCO 2017](https://cocodataset.org), created by COCO Consortium (Lin et al., ECCV 2014), originally licensed cc-by-4.0. We are grateful to the original authors. If you use this data, please cite the original source above.

## Citation

```bibtex
@inproceedings{lin2014microsoft,
  title={Microsoft COCO: Common Objects in Context},
  author={Lin, Tsung-Yi and Maire, Michael and Belongie, Serge and Hays, James and Perona, Pietro and Ramanan, Deva and Dollar, Piotr and Zitnick, C Lawrence},
  booktitle={ECCV},
  year={2014}
}
```

---

*Published from [Pictograph](https://pictograph.io/datasets/pictograph-research/coco-5k) - annotate, train, and deploy from one API.*