ActivityNet 200

ActivityNet is a large-scale video dataset for human activity understanding supporting the tasks of global video classification, trimmed activity classification, and temporal activity detection.

This version contains videos and temporal activity detections for the 200 class version of the dataset.

Note

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

Notes

• ActivityNet 200 is a superset of ActivityNet 100

• ActivityNet 100 and 200 differ in the number of activity classes and videos per split

• Partial downloads will download videos (if still available) from YouTube

• Full splits can be loaded by first downloading the official source files from the ActivityNet maintainers

• The test set does not have annotations

Details

• Dataset name: activitynet-200

• Dataset source: http://activity-net.org/index.html

• Dataset license: CC-BY-4.0

• Dataset size: 500 GB

• Tags: video, classification, action-recognition, temporal-detection

• Supported splits: train, validation, test

• ZooDataset class: ActivityNet200Dataset

Full split stats

• Train split: 10,024 videos (15,410 instances)

• Test split: 5,044 videos (labels withheld)

• Validation split: 4,926 videos (7,654 instances)

Partial downloads

FiftyOne provides parameters that can be used to efficiently download specific subsets of the ActivityNet 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 YouTube.

The following parameters are available to configure a partial download of ActivityNet 200 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 none are provided, all available splits are loaded

• source_dir (None): the directory containing the manually downloaded ActivityNet files used to avoid downloading videos from YouTube

• 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

• max_duration (None): only videos with a duration in seconds that is less than or equal to the max_duration will be downloaded. By default, all videos are downloaded

• copy_files (True): whether to move (False) or create copies (True) of the source files when populating dataset_dir. This is only relevant when a source_dir is provided

• num_workers (None): the number of processes to use when downloading individual videos. 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 classes are also specified, only up to the number of samples that contain at least one specified class will be loaded. By default, all matching samples are loaded

Note

See ActivityNet200Dataset and ActivityNetDatasetImporter for complete descriptions of the optional keyword arguments that you can pass to load_zoo_dataset().

Full split downloads

Many videos have been removed from YouTube since the creation of ActivityNet. As a result, if you do not specify any partial download parameters defined in the previous section, you must first download the official source files from the ActivityNet maintainers in order to load a full split into FiftyOne.

To download the source files, you must fill out this form.

Refer to this page to see how to load full splits by passing the source_dir parameter to load_zoo_dataset().

Example usage

PythonCLI

import fiftyone as fo
import fiftyone.zoo as foz

#
# Load 10 random samples from the validation split
#
# Only the required videos will be downloaded (if necessary)
#

dataset = foz.load_zoo_dataset(
    "activitynet-200",
    split="validation",
    max_samples=10,
    shuffle=True,
)

session = fo.launch_app(dataset)

#
# Load 10 samples from the validation split that
# contain the actions "Bathing dog" and "Walking the dog"
#
# Videos that contain all `classes` will be prioritized first, followed
# by videos that contain at least one of the required `classes`. If
# there are not enough videos matching `classes` in the split to meet
# `max_samples`, only the available videos will be loaded.
#
# Videos will only be downloaded if necessary
#
# Subsequent partial loads of the validation split will never require
# downloading any videos
#

dataset = foz.load_zoo_dataset(
    "activitynet-200",
    split="validation",
    classes=["Bathing dog", "Walking the dog"],
    max_samples=10,
)

session.dataset = dataset

Note

In order to work with video datasets, you’ll need to have ffmpeg installed.