COCO-2017

COCO is a large-scale object detection, segmentation, and captioning dataset.

This version contains images, bounding boxes, and segmentations for the 2017 version of the dataset.

Note

With support from the COCO team, FiftyOne is a recommended tool for downloading, visualizing, and evaluating on the COCO dataset!

Check out this guide for more details on using FiftyOne to work with COCO.

Notes

• COCO defines 91 classes but the data only uses 80 classes

• Some images from the train and validation sets don’t have annotations

• The test set does not have annotations

• COCO 2014 and 2017 use the same images, but the splits are different

Details

• Dataset name: coco-2017

• Dataset source: http://cocodataset.org/#home

• Dataset license: CC-BY-4.0

• Dataset size: 25.20 GB

• Tags: image, detection, segmentation

• Supported splits: train, validation, test

• ZooDataset class: COCO2017Dataset

Full split stats

• Train split: 118,287 images

• Test split: 40,670 images

• Validation split: 5,000 images

Partial downloads

FiftyOne provides parameters that can be used to efficiently download specific subsets of the COCO dataset to suit your needs. When new subsets are specified, FiftyOne will use existing downloaded data first if possible before resorting to downloading additional data from the web.

The following parameters are available to configure a partial download of COCO-2017 by passing them to load_zoo_dataset():

• split (None) and splits (None): a string or list of strings, respectively, specifying the splits to load. Supported values are ("train", "test", "validation"). If neither is provided, all available splits are loaded

• label_types (None): a label type or list of label types to load. Supported values are ("detections", "segmentations"). By default, only detections are loaded

• classes (None): a string or list of strings specifying required classes to load. If provided, only samples containing at least one instance of a specified class will be loaded

• image_ids (None): a list of specific image IDs to load. The IDs can be specified either as <split>/<image-id> strings or <image-id> ints or strings. Alternatively, you can provide the path to a TXT (newline-separated), JSON, or CSV file containing the list of image IDs to load in either of the first two formats

• include_id (False): whether to include the COCO ID of each sample in the loaded labels

• include_license (False): whether to include the COCO license of each sample in the loaded labels, if available. The supported values are:

  • "False" (default): don’t load the license

  • True/"name": store the string license name

  • "id": store the integer license ID

  • "url": store the license URL

• only_matching (False): whether to only load labels that match the classes or attrs requirements that you provide (True), or to load all labels for samples that match the requirements (False)

• num_workers (None): the number of processes to use when downloading individual images. By default, multiprocessing.cpu_count() is used

• shuffle (False): whether to randomly shuffle the order in which samples are chosen for partial downloads

• seed (None): a random seed to use when shuffling

• max_samples (None): a maximum number of samples to load per split. If label_types and/or classes are also specified, first priority will be given to samples that contain all of the specified label types and/or classes, followed by samples that contain at least one of the specified labels types or classes. The actual number of samples loaded may be less than this maximum value if the dataset does not contain sufficient samples matching your requirements

Note

See COCO2017Dataset and COCODetectionDatasetImporter for complete descriptions of the optional keyword arguments that you can pass to load_zoo_dataset().

Example usage

PythonCLI

import fiftyone as fo
import fiftyone.zoo as foz

#
# Load 50 random samples from the validation split
#
# Only the required images will be downloaded (if necessary).
# By default, only detections are loaded
#

dataset = foz.load_zoo_dataset(
    "coco-2017",
    split="validation",
    max_samples=50,
    shuffle=True,
)

session = fo.launch_app(dataset)

#
# Load segmentations for 25 samples from the validation split that
# contain cats and dogs
#
# Images that contain all `classes` will be prioritized first, followed
# by images that contain at least one of the required `classes`. If
# there are not enough images matching `classes` in the split to meet
# `max_samples`, only the available images will be loaded.
#
# Images will only be downloaded if necessary
#

dataset = foz.load_zoo_dataset(
    "coco-2017",
    split="validation",
    label_types=["segmentations"],
    classes=["cat", "dog"],
    max_samples=25,
)

session.dataset = dataset

#
# Download the entire validation split and load both detections and
# segmentations
#
# Subsequent partial loads of the validation split will never require
# downloading any images
#

dataset = foz.load_zoo_dataset(
    "coco-2017",
    split="validation",
    label_types=["detections", "segmentations"],
)

session.dataset = dataset