First-person (egocentric) video dataset; multi-faceted non-scripted recordings in the wearers' homes, capturing all daily activities in the kitchen over multiple days. Annotations are collected using a novel live audio commentary approach.

Motivation

The actual download link is very slow, including the academic torrent. Therefore, to spare fellow community members from this misery, I am uploading the dataset here.

  Source

You can fnd the original source to download the dataset: https://github.com/epic-kitchens/epic-kitchens-download-scripts

  Citation

@INPROCEEDINGS{Damen2018EPICKITCHENS, title={Scaling Egocentric Vision: The EPIC-KITCHENS Dataset}, author={Damen, Dima and Doughty, Hazel and… See the full description on the dataset page: https://huggingface.co/datasets/awsaf49/epic_kitchens_100.

EPIC-KITCHENS-100 is a large-scale dataset in first-person (egocentric) vision; multi-faceted, audio-visual, non-scripted recordings in native environments - i.e. the wearers' homes, capturing all daily activities in the kitchen over multiple days. Annotations are collected using a novel 'Pause-and-Talk' narration interface.

EPIC-KITCHENS-100 is an extension of the EPIC-KITCHENS dataset released in 2018, to 100 hours of footage.

EPIC-KITCHENS-100: Extended Footage for EPIC-KITCHENS dataset, to 100 hours of footage. 10.5523/bris.2g1n6qdydwa9u22shpxqzp0t8m 2020-09-10 N.b. please also see ERRATUM published at 2021-05-25 This supersedes the original torrent, which had a small change made.

EPIC-KITCHENS-100 Automatic Annotations/hand-objects/P04

We introduce VISOR, a new dataset of pixel annotations and a benchmark suite for segmenting hands and active objects in egocentric video. VISOR annotates videos from EPIC-KITCHENS, which comes with a new set of challenges not encountered in current video segmentation datasets. Specifically, we need to ensure both short- and long-term consistency of pixel-level annotations as objects undergo transformative interactions, e.g. an onion is peeled, diced and cooked - where we aim to obtain accurate pixel-level annotations of the peel, onion pieces, chopping board, knife, pan, as well as the acting hands. VISOR introduces an annotation pipeline, AI-powered in parts, for scalability and quality. Data published under the Creative Commons Attribution-NonCommerial 4.0 International License.

Epic Kitchens dataset is a dataset for egocentric vision and action recognition.

The largest dataset in egocentric vision to-date. Full details on: http://epic-kitchens.github.io

EPIC-KITCHENS 2018/videos

The authors used the HACS and Epic-Kitchens-100 datasets for action localization tasks.

The largest dataset in egocentric vision to-date. Full details on: http://epic-kitchens.github.io

EPIC-KITCHENS 2018/videos/train

EPIC-KITCHENS-100 Multi-Instance Retrieval Challenge

The Epic-Kitchens-100 dataset contains 97 verb and 300 noun classes with actions defined by the combination of nouns and verbs.

A subset of the footage from EPIC-KITCHENS extracted in a special format for Neural-Diff analysis

Dataset Card for 2HANDS

2HANDS is the 2-Handed Affordance + Narration DataSet, consisting of a large number of unimanual and bimanual object affordance segmentation masks and task narrations as affordance class-labels.

Project Site https://sites.google.com/view/2handedafforder (ICCV 2025) Paper: https://arxiv.org/abs/2503.09320 Repository: Coming soon

Egocentric images and narrations/verb classes are derived from the EPIC-KITCHENS dataset and EPIC-VISOR annotations [1, 2].[1]… See the full description on the dataset page: https://huggingface.co/datasets/sjauhri/2HANDS.