Dataset Ninja LogoDataset Ninja:

COCO 2017 Dataset

163957803817
Taggeneral, benchmark
Taskinstance segmentation
Release YearMade in 2017
LicenseCC BY 4.0
Download25 GB

Introduction #

Tsung-Yi, Genevieve Patterson, Matteo R. Ronchiet al.

The COCO 2017 dataset is a component of the extensive Microsoft COCO dataset. To learn more about this dataset, you can visit its homepage. The creators of this dataset, in their pursuit of advancing object recognition, have placed their focus on the broader concept of scene comprehension. This vision is realized through the compilation of images depicting intricate everyday scenes where common objects naturally exist. Within the Microsoft COCO dataset, you will find photographs encompassing 91 different types of objects, all of which are easily identifiable by a four-year-old. This comprehensive dataset includes a grand total of 2.5 million labeled instances distributed across 328,000 images. It’s worth noting that in COCO 2017, specifically, you’ll encounter 1.8 million labeled instances within 164,000 images, and the number of classes differ.

The dataset addresses three core research problems in scene understanding: detecting non-iconic views (or non-canonical perspectives) of objects, contextual reasoning between objects and the precise 2D localization of objects he selection of object categories is a non-trivial exercise. The categories must form a representative set of all categories, be relevant to practical applications and occur with high enough frequency to enable the collection of a large dataset.

Other important decisions are whether to include both “thing” and “stuff” categories and whether fine-grained and object-part categories should be included. “Thing” categories include objects for which individual instances may be easily labeled (person, chair, car) whereas “stuff” categories include materials and objects with no clear boundaries (sky, street, grass). Authors decided to only include “thing” categories and not “stuff” because they are primarily interested in the precise localization of object instances. Check out the COCO-Stuff 164k for details

The specificity of object categories can vary significantly. For instance, a dog could be a member of the “mammal”, “dog”, or “German shepherd” categories. To enable the practical collection of a significant number of instances per category, authors chose to limit our dataset to entry-level categories, i.e. category labels that are commonly used by humans when describing objects (dog, chair, person). It is also possible that some object categories may be parts of other object categories. For instance, a face may be part of a person. Authors anticipate the inclusion of object-part categories (face, hands, wheels) would be beneficial for many real-world applications.

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Dataset LinkHomepageDataset LinkResearch Paper

Summary #

COCO 2017: Common Objects in Context 2017 is a dataset for instance segmentation, semantic segmentation, and object detection tasks. It is applicable or relevant across various domains.

The dataset consists of 163957 images with 2099063 labeled objects belonging to 80 different classes including person, chair, car, and other: dining table, cup, bottle, bowl, handbag, truck, bench, backpack, book, cell phone, sink, clock, tv, potted plant, couch, dog, knife, sports ball, traffic light, cat, umbrella, bus, tie, bed, train, and 52 more.

Images in the COCO 2017 dataset have pixel-level instance segmentation and bounding box annotations. Due to the nature of the instance segmentation task, it can be automatically transformed into a semantic segmentation task (only one mask for every class). There are 41739 (25% of the total) unlabeled images (i.e. without annotations). There are 3 splits in the dataset: train2017 (118287 images), test2017 (40670 images), and val2017 (5000 images). The dataset was released in 2017 by the COCO Consortium.

Here is a visualized example for randomly selected sample classes:

Explore #

COCO 2017 dataset has 163957 images. Click on one of the examples below or open "Explore" tool anytime you need to view dataset images with annotations. This tool has extended visualization capabilities like zoom, translation, objects table, custom filters and more. Hover the mouse over the images to hide or show annotations.

OpenSample annotation mask from COCO 2017Sample image from COCO 2017
OpenSample annotation mask from COCO 2017Sample image from COCO 2017
OpenSample annotation mask from COCO 2017Sample image from COCO 2017
OpenSample annotation mask from COCO 2017Sample image from COCO 2017
OpenSample annotation mask from COCO 2017Sample image from COCO 2017
OpenSample annotation mask from COCO 2017Sample image from COCO 2017
OpenSample annotation mask from COCO 2017Sample image from COCO 2017
OpenSample annotation mask from COCO 2017Sample image from COCO 2017
OpenSample annotation mask from COCO 2017Sample image from COCO 2017
OpenSample annotation mask from COCO 2017Sample image from COCO 2017
OpenSample annotation mask from COCO 2017Sample image from COCO 2017
OpenSample annotation mask from COCO 2017Sample image from COCO 2017
OpenSample annotation mask from COCO 2017Sample image from COCO 2017
OpenSample annotation mask from COCO 2017Sample image from COCO 2017
OpenSample annotation mask from COCO 2017Sample image from COCO 2017
OpenSample annotation mask from COCO 2017Sample image from COCO 2017
OpenSample annotation mask from COCO 2017Sample image from COCO 2017
OpenSample annotation mask from COCO 2017Sample image from COCO 2017
OpenSample annotation mask from COCO 2017Sample image from COCO 2017
OpenSample annotation mask from COCO 2017Sample image from COCO 2017
OpenSample annotation mask from COCO 2017Sample image from COCO 2017
OpenSample annotation mask from COCO 2017Sample image from COCO 2017
OpenSample annotation mask from COCO 2017Sample image from COCO 2017
OpenSample annotation mask from COCO 2017Sample image from COCO 2017
OpenSample annotation mask from COCO 2017Sample image from COCO 2017
OpenSample annotation mask from COCO 2017Sample image from COCO 2017
OpenSample annotation mask from COCO 2017Sample image from COCO 2017
OpenSample annotation mask from COCO 2017Sample image from COCO 2017
OpenSample annotation mask from COCO 2017Sample image from COCO 2017
OpenSample annotation mask from COCO 2017Sample image from COCO 2017
👀
Have a look at 163957 images
View images along with annotations and tags, search and filter by various parameters

Class balance #

There are 80 annotation classes in the dataset. Find the general statistics and balances for every class in the table below. Click any row to preview images that have labels of the selected class. Sort by column to find the most rare or prevalent classes.

Search
Rows 1-10 of 80
Class
Images
Objects
Count on image
average
Area on image
average
person
any
66808
649193
9.72
29.33%
chair
any
13354
98848
7.4
11.35%
car
any
12786
110156
8.62
8.71%
dining table
any
12338
44412
3.6
43.66%
cup
any
9579
44616
4.66
4.98%
bottle
any
8880
55003
6.19
4.23%
bowl
any
7425
31719
4.27
15.96%
handbag
any
7133
28871
4.05
3.54%
truck
any
6377
23846
3.74
16.8%
bench
any
5805
28267
4.87
12.96%

Images #

Explore every single image in the dataset with respect to the number of annotations of each class it has. Click a row to preview selected image. Sort by any column to find anomalies and edge cases. Use horizontal scroll if the table has many columns for a large number of classes in the dataset.

Class sizes #

The table below gives various size properties of objects for every class. Click a row to see the image with annotations of the selected class. Sort columns to find classes with the smallest or largest objects or understand the size differences between classes.

Search
Rows 1-10 of 80
Class
Object count
Avg area
Max area
Min area
Min height
Min height
Max height
Max height
Avg height
Avg height
Min width
Min width
Max width
Max width
person
any
649193
5.07%
100%
0%
1px
0.21%
640px
100%
117px
24.68%
1px
0.16%
640px
100%
car
any
110156
1.78%
100%
0%
2px
0.31%
640px
100%
44px
9.42%
2px
0.31%
640px
100%
chair
any
98848
2.46%
100%
0%
2px
0.31%
640px
100%
75px
16.03%
1px
0.16%
640px
100%
book
any
56131
1.53%
100%
0%
1px
0.21%
633px
100%
51px
10.91%
2px
0.31%
640px
100%
bottle
any
55003
1.17%
99.38%
0%
2px
0.31%
640px
100%
67px
13.85%
2px
0.31%
640px
100%
cup
any
44616
1.87%
100%
0%
2px
0.31%
633px
100%
65px
13.45%
2px
0.31%
640px
100%
dining table
any
44412
21.17%
100%
0%
1px
0.24%
640px
100%
169px
34.81%
2px
0.31%
640px
100%
bowl
any
31719
6.67%
100%
0%
2px
0.42%
640px
100%
88px
18.22%
2px
0.42%
640px
100%
skis
any
29926
0.63%
74.11%
0%
1px
0.16%
629px
100%
34px
7.03%
1px
0.16%
634px
99.79%
handbag
any
28871
1.36%
99.38%
0%
2px
0.42%
582px
100%
60px
12.61%
1px
0.2%
640px
100%

Spatial Heatmap #

The heatmaps below give the spatial distributions of all objects for every class. These visualizations provide insights into the most probable and rare object locations on the image. It helps analyze objects' placements in a dataset.

Spatial Heatmap

Objects #

Table contains all 2099063 objects. Click a row to preview an image with annotations, and use search or pagination to navigate. Sort columns to find outliers in the dataset.

Search
Rows 1-10 of 102042
Object ID
Class
Image name
click row to open
Image size
height x width
Height
Height
Width
Width
Area
1
tv
any
000000301421.jpg
427 x 640
128px
29.98%
152px
23.75%
5.91%
2
tv
any
000000301421.jpg
427 x 640
128px
29.98%
152px
23.75%
7.12%
3
chair
any
000000301421.jpg
427 x 640
135px
31.62%
178px
27.81%
4.83%
4
chair
any
000000301421.jpg
427 x 640
135px
31.62%
178px
27.81%
8.79%
5
cell phone
any
000000301421.jpg
427 x 640
59px
13.82%
35px
5.47%
0.47%
6
cell phone
any
000000301421.jpg
427 x 640
59px
13.82%
35px
5.47%
0.76%
7
laptop
any
000000301421.jpg
427 x 640
136px
31.85%
255px
39.84%
6.07%
8
laptop
any
000000301421.jpg
427 x 640
136px
31.85%
255px
39.84%
12.69%
9
keyboard
any
000000301421.jpg
427 x 640
65px
15.22%
121px
18.91%
1.34%
10
keyboard
any
000000301421.jpg
427 x 640
65px
15.22%
121px
18.91%
2.88%

License #

COCO 2017: Common Objects in Context 2017 is under CC BY 4.0 license.

Source

Citation #

If you make use of the COCO 2017 data, please cite the following reference:

@dataset{COCO 2017,
  author={Tsung-Yi and Genevieve Patterson and Matteo R. Ronchi and Yin Cui and Michael Maire and Serge Belongie and Lubomir Bourdev and Ross Girshick and James Hays Georgia and Pietro Perona and Deva Ramanan and Larry Zitnick and Piotr Dollár},
  title={COCO 2017: Common Objects in Context 2017},
  year={2017},
  url={https://cocodataset.org/#home}
}

Source

If you are happy with Dataset Ninja and use provided visualizations and tools in your work, please cite us:

@misc{ visualization-tools-for-coco-dataset,
  title = { Visualization Tools for COCO 2017 Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/coco-2017 } },
  url = { https://datasetninja.com/coco-2017 },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { may },
  note = { visited on 2024-05-26 },
}

Download #

Dataset COCO 2017 can be downloaded in Supervisely format:

As an alternative, it can be downloaded with dataset-tools package:

pip install --upgrade dataset-tools

… using following python code:

import dataset_tools as dtools

dtools.download(dataset='COCO 2017', dst_dir='~/dataset-ninja/')

Make sure not to overlook the python code example available on the Supervisely Developer Portal. It will give you a clear idea of how to effortlessly work with the downloaded dataset.

The data in original format can be downloaded here:

. . .

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