README.md

metadata

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/*

View on Pictograph · Pictograph Research · Creative Commons Attribution 4.0

About

COCO-5k is a computer-vision dataset curated and annotated on Pictograph. 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:

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.

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):

{
  "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. When you use this data, please credit COCO Consortium (Lin et al., ECCV 2014).

Source and attribution

This dataset is derived from COCO 2017, 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

@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 - annotate, train, and deploy from one API.