Datasets:
image imagewidth (px) 200 640 | objects dict | segmentation listlengths 1 62 ⌀ |
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[
217.35,
283.46,
134.94000000000003,
115.01000000000005
],
[
0.91,
218.89,
155.76,
178.40000000000003
],
[
403.91,
351.38,
147.60999999999996,... | [
{
"label": "umbrella",
"category": 76,
"points": [
[
355.48,
272.32
],
[
362.79,
275.98
],
[
372.84,
273.24
],
[
399.34,
268.67
],
[
440.47,
255.87
],
[
... | |
{
"bbox": [
[
16.36,
130.43,
28.97,
73.19
],
[
62.4,
130.74,
71.50999999999999,
100.44
],
[
329.55,
104.4,
106.40999999999997,
122.66999999999999
],
[
518.82,
87.71,
81.20999999999992,
129.8
... | [
{
"label": "person",
"category": 49,
"points": [
[
26.02,
203.62
],
[
21.95,
181.77
],
[
19.92,
168.55
],
[
16.36,
154.83
],
[
17.89,
145.17
],
[
22.4... | |
{
"bbox": [
[
0,
169.34,
620.7,
348.5899999999999
]
],
"categories": [
20
],
"category_names": [
"cat"
]
} | [
{
"label": "cat",
"category": 20,
"points": [
[
330.59,
476.32
],
[
425.05,
478.57
],
[
453.16,
506.68
],
[
462.15,
517.93
],
[
620.7,
513.43
],
[
617... | |
{
"bbox": [
[
73.71,
41.97,
339.77000000000004,
351.46000000000004
],
[
459.97,
248.03,
29.029999999999973,
26.590000000000003
],
[
0,
253.89,
75.39,
66.49000000000001
]
],
"categories": [
49,
62,
67
],
... | [
{
"label": "person",
"category": 49,
"points": [
[
73.71,
249.61
],
[
94.38,
243.32
],
[
137.53,
236.13
],
[
146.52,
225.34
],
[
140.22,
209.16
],
[
1... | |
{
"bbox": [
[
55.78,
96.24,
93.30000000000001,
108.69000000000001
],
[
268.99,
192.47,
12.629999999999995,
13.580000000000013
],
[
268.24,
194.52,
11.349999999999966,
10.189999999999998
],
[
15.4,
194.19,
... | [
{
"label": "refrigerator",
"category": 52,
"points": [
[
55.78,
96.24
],
[
69.25,
98.17
],
[
143.31,
105.86
],
[
149.08,
195.31
],
[
110.61,
195.31
],
[
... | |
{
"bbox": [
[
519.37,
192.93,
50.639999999999986,
23.98999999999998
],
[
504.82,
188.33,
17.21999999999997,
22.149999999999977
],
[
365.94,
199.5,
34.94,
22.939999999999998
],
[
370.97,
166.6,
71.63,
... | [
{
"label": "car",
"category": 18,
"points": [
[
519.87,
213.75
],
[
519.37,
209.25
],
[
519.7,
205.09
],
[
519.7,
202.76
],
[
525.53,
200.09
],
[
532.... | |
{
"bbox": [
[
144.54,
116.4,
460.44000000000005,
219.9
],
[
149.64,
156.64,
490.36,
199.17000000000002
]
],
"categories": [
20,
42
],
"category_names": [
"cat",
"laptop"
]
} | [
{
"label": "cat",
"category": 20,
"points": [
[
144.54,
140.15
],
[
153.83,
140.15
],
[
166.22,
146.34
],
[
179.64,
146.34
],
[
195.12,
146.34
],
[
19... | |
{
"bbox": [
[
219.79,
203.38,
76.33000000000001,
72.62
],
[
1.77,
405.47,
97.06,
96.64999999999998
],
[
352.29,
413.4,
74.70999999999998,
79.75
],
[
56.22,
430.99,
270.99,
198.91999999999996
... | [
{
"label": "sports ball",
"category": 62,
"points": [
[
289.76,
217.69
],
[
283.4,
211.86
],
[
273.33,
207.09
],
[
260.08,
203.38
],
[
247.35,
204.44
],
[
... | |
{
"bbox": [
[
81.98,
27.33,
280.45,
556.5799999999999
],
[
2.88,
142.38,
477.12,
488.99
]
],
"categories": [
21,
49
],
"category_names": [
"cell phone",
"person"
]
} | [
{
"label": "cell phone",
"category": 21,
"points": [
[
335.1,
562.34
],
[
362.43,
27.33
],
[
81.98,
28.76
],
[
90.61,
562.34
],
[
339.42,
583.91
]
]
},
{
... | |
{
"bbox": [
[
0,
196.61,
209.4,
102.01999999999998
],
[
296.82,
281.51,
28.069999999999993,
25.019999999999982
],
[
478.2,
210.62,
66.95999999999998,
36.400000000000006
],
[
300.14,
293.17,
45.8500000... | [
{
"label": "bus",
"category": 16,
"points": [
[
3.22,
200.91
],
[
85.91,
199.84
],
[
113.83,
197.69
],
[
123.49,
196.61
],
[
124.56,
196.61
],
[
139.6... | |
{
"bbox": [
[
106.17,
22.69,
66.60000000000001,
59.379999999999995
],
[
284.5,
212.22,
36.20999999999998,
23.569999999999993
],
[
283.46,
152.82,
145.74,
138.86
],
[
79.83,
68.64,
148.47000000000003,
... | [
{
"label": "baseball bat",
"category": 4,
"points": [
[
113.06,
22.69
],
[
106.17,
28.92
],
[
107.48,
30.89
],
[
169.16,
82.07
],
[
172.77,
79.45
]
]
},
{
... | |
{
"bbox": [
[
425.22,
76.05,
24.739999999999952,
27.25
],
[
1,
164.46,
348.21,
269.4
],
[
0,
145.67,
209.53,
34.920000000000016
],
[
334.25,
37.91,
199.54999999999995,
307.31000000000006
],
... | [
{
"label": "cell phone",
"category": 21,
"points": [
[
426.69,
88.42
],
[
437,
80.17
],
[
442.16,
76.05
],
[
445.4,
76.64
],
[
448.49,
80.76
],
[
449.... | |
{
"bbox": [
[
16.4,
280.72,
65.74000000000001,
115.94999999999999
],
[
533.62,
184.35,
106.38,
158.00000000000003
],
[
105.46,
91.06,
354.63,
296.35
],
[
90.62,
231.02,
18.69999999999999,
25.289... | [
{
"label": "bicycle",
"category": 9,
"points": [
[
35.57,
312.67
],
[
23.7,
317.24
],
[
17.31,
324.54
],
[
17.31,
331.85
],
[
20.96,
339.15
],
[
19.14... |
COCO-5k
5,000 images and 36,335 annotations across 80 classes, annotated as polygon and bounding box.
View on Pictograph · Pictograph Research · Creative Commons Attribution 4.0
About
COCO-5k is a computer-vision dataset curated and annotated on Pictograph. 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 |
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 |
Polygon rings as [[x, y], ...] with class label. |
Use it
from datasets import load_dataset
ds = load_dataset("pictograph/coco-5k")
print(ds)
Prefer a full annotation editor, one-click fork, multi-format export, and one-click training? Open this dataset on Pictograph.
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 Creative Commons Attribution 4.0. We are grateful to the original authors. If you use this data, please cite the original source above.
Published from Pictograph - annotate, train, and deploy from one API.
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