# COCO-CLS-5k

COCO-CLS-5k: 5,000 images with segmentation polygons, bounding boxes and image-level tags for 135 classes, including airplane, apple, backpack and banana. Based on COCO 2017. Import it into a Pictograph workspace to export COCO, YOLO or Pascal VOC, or train a model on it. Published on Pictograph by Pictograph Research.

## Overview

- Type: public computer vision dataset
- Images: 5,000
- Classes: 135
- Annotation types: polygon, bbox, tag
- Tasks: Object detection (https://pictograph.io/explore/task/object-detection), Segmentation (https://pictograph.io/explore/task/segmentation)
- Owner: Pictograph Research
- Imported from: Pictograph Research / COCO-5k (https://pictograph.io/datasets/pictograph-research/coco-5k)
- License: Custom License
- Source: COCO 2017 (https://cocodataset.org), created by COCO Consortium (Lin et al., ECCV 2014). Credit the original authors when you use this data.
- Stars: 0
- Page: https://pictograph.io/datasets/pictograph-research/coco-cls-5k
- Hugging Face: https://huggingface.co/datasets/pictograph/coco-cls-5k
- Export formats: Pictograph JSON, Darwin V7, COCO, YOLO, YOLO-OBB, YOLO-Pose, DOTA, Pascal VOC, CVAT, Datumaro, LabelMe, CSV

## License terms

Non-commercial use only. Annotations: CC BY 4.0, the COCO Consortium. Images: the COCO Consortium does not own their copyright; each image comes from Flickr under its own license, recorded per image in COCO's annotation files, and most of them (68.9% of train2017) are Creative Commons NonCommercial. Use of the images must abide by the Flickr Terms of Use and each image's license. Cite Lin et al., Microsoft COCO: Common Objects in Context (ECCV 2014).

## Classes

- airplane
- apple
- backpack
- banana
- baseball bat
- baseball glove
- bear
- bed
- bench
- bicycle
- bird
- boat
- book
- bottle
- bowl
- broccoli
- bus
- cake
- car
- carrot
- cat
- cell phone
- chair
- clock
- couch
- cow
- cup
- dining table
- dog
- donut
- elephant
- fire hydrant
- fork
- frisbee
- giraffe
- hair drier
- handbag
- horse
- hot dog
- keyboard
- kite
- knife
- laptop
- microwave
- motorcycle
- mouse
- orange
- oven
- parking meter
- person
- pizza
- potted plant
- refrigerator
- remote
- sandwich
- scissors
- sheep
- sink
- skateboard
- skis
- snowboard
- spoon
- sports ball
- stop sign
- suitcase
- surfboard
- teddy bear
- tennis racket
- tie
- toaster
- toilet
- toothbrush
- traffic light
- train
- truck
- tv
- umbrella
- vase
- wine glass
- zebra
- skateboarding
- skiing
- bathroom
- bird
- frisbee
- giraffe
- soccer
- teddy bear
- elephant
- vintage photo
- bear
- zebra
- tennis
- train
- baseball
- cat
- dog
- airplane
- kitchen
- kitesurfing
- kite
- boat
- beach
- surfing
- horse
- vase
- livestock
- bedroom
- fruit
- living room
- market
- motorcycle
- portrait
- clock
- street
- fire hydrant
- bus
- luggage
- bench
- pizza
- office
- electronics
- child
- video games
- dining
- formal wear
- donut
- dessert
- meal
- vegetables
- cake
- hot dog
- street sign
- stop sign
- other

## Models trained on this dataset

- PG-COCO-ViT-b16: https://pictograph.io/models/pictograph-research/pg-coco-vit-b16
- PG-COCO-ResNet50: https://pictograph.io/models/pictograph-research/pg-coco-resnet50

## About

# COCO-CLS-5k

> A public computer-vision dataset of 5,000 images with 36,335 annotations across 135 classes, annotated as polygon, bounding box, and tag.

**[Pictograph Research](/org/pictograph-research)** · a custom license · Public · Updated 2026-11-03

## Overview

This dataset holds **5,000 images** annotated as polygon, bounding box, and tag across 135 classes. The most common auto-detected tags are playing, eating, person, eye-level view, top-down view, zoo. It is ready to import into a workspace, export in a dozen formats, or train a model on.

## At a glance

| Metric | Value |
| :--- | ---: |
| Images | 5,000 |
| Annotations | 36,335 |
| Classes | 135 |
| Annotation types | polygon, bounding box, tag |
| Folders | 1 |
| Storage | Standard |
| Visibility | Public |

## What's in these images

Tags auto-detected across the dataset, most common first.

| Tag | Category | Images |
| :--- | :--- | ---: |
| playing | Attributes | 696 |
| eating | Attributes | 563 |
| person | Objects | 471 |
| eye-level view | Attributes | 414 |
| top-down view | Attributes | 340 |
| zoo | Scenes | 248 |
| sign | Objects | 235 |
| walking | Attributes | 219 |
| driving | Attributes | 212 |
| child | Objects | 211 |
| bus | Objects | 203 |
| cooking | Attributes | 197 |
| jumping | Attributes | 197 |
| bathroom | Scenes | 195 |
| sitting | Attributes | 194 |

## Image tags

Every image carries exactly one image-level tag: 55 tags across all 5,000 images, so the dataset trains an image classifier as it is. The tags were assigned from the dataset's SigLIP embedding map and reviewed by eye; `other` holds the 313 images no other tag fits. The COCO polygons are kept alongside.

| Tag | Images |
| :--- | ---: |
| street | 228 |
| meal | 176 |
| bathroom | 175 |
| cat | 167 |
| tennis | 164 |
| skiing | 160 |
| kitchen | 149 |
| train | 143 |
| baseball | 137 |
| bus | 137 |
| skateboarding | 133 |
| livestock | 131 |
| pizza | 130 |
| surfing | 128 |
| clock | 127 |
| dining | 126 |
| office | 110 |
| horse | 108 |
| living room | 106 |
| motorcycle | 104 |
| giraffe | 102 |
| dog | 95 |
| airplane | 90 |
| video games | 89 |
| elephant | 84 |
| formal wear | 84 |
| kite | 81 |
| fruit | 78 |
| portrait | 76 |
| zebra | 76 |
| street sign | 74 |
| boat | 71 |
| teddy bear | 71 |
| frisbee | 65 |
| fire hydrant | 57 |
| bird | 53 |
| bedroom | 52 |
| bear | 48 |
| vase | 43 |
| bench | 42 |
| cake | 40 |
| child | 40 |
| donut | 40 |
| luggage | 39 |
| electronics | 38 |
| market | 33 |
| stop sign | 31 |
| vegetables | 29 |
| hot dog | 27 |
| dessert | 24 |
| beach | 23 |
| kitesurfing | 22 |
| soccer | 18 |
| vintage photo | 13 |
| other | 313 |

## Use this dataset

```python
from pictograph import Client, Dataset

client = Client()  # reads PICTOGRAPH_API_KEY
dataset: Dataset = client.datasets.get("COCO-CLS-5k")
print(dataset.image_count, dataset.annotation_count)
```

- Import it into your own workspace with one click from its page.
- Pull it with the [Pictograph SDK](https://pictograph.io/docs/api-reference/datasets) (shown above) or the CLI.
- [Export](https://pictograph.io/docs/export-conversion) to COCO, YOLO, Darwin V7, Pascal VOC, and more.
- [Train a model](https://pictograph.io/docs/guides/train) directly from an export.

## License

Released under a custom license.

## Source

The images and annotations come from [COCO 2017](https://cocodataset.org), created by COCO Consortium (Lin et al., ECCV 2014) and released under [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/). Please credit the original authors when you use this data.


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Free to view. Sign in at https://app.pictograph.io to import this dataset, re-annotate it with SAM3, or export it.