What is EPIC-Kitchens?

The largest dataset in first-person (egocentric) vision; multi-faceted 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 `live' audio commentary approach.


  • 32 kitchens - 4 cities
  • Head-mounted camera
  • 55 hours of recording - Full HD, 60fps
  • 11.5M frames
  • Multi-language narrations
  • 39,594 action segments
  • 454,255 object bounding boxes
  • 125 verb classes, 331 noun classes


Stay tuned with updates on epic-kitchens, as well as EPIC workshop series by joining the epic-community mailing list send an email to: with the subject subscribe epic-community and a blank message body.

EPIC-Kitchens Stats

Some graphical representations of our dataset and annotations

Time Of Day


Wordle of annotations


Dataset publicly available for research purposes

Sequences and Annotations

Sequences: Available at Data.Bris servers (1TB zipped)

To download parts of the dataset, we provide three scripts for downloading the

Pretrained Models

We release several pretrained models for action recognition (PyTorch) as well as object detection faster RCNN model


Cite the following paper (available now on Arxiv and the CVF):

   title={Scaling Egocentric Vision: The EPIC-KITCHENS Dataset},
   author={Damen, Dima and Doughty, Hazel and Farinella, Giovanni Maria  and Fidler, Sanja and 
           Furnari, Antonino and Kazakos, Evangelos and Moltisanti, Davide and Munro, Jonathan 
           and Perrett, Toby and Price, Will and Wray, Michael},
   booktitle={European Conference on Computer Vision (ECCV)},

Copyright Creative Commons License

All datasets and benchmarks on this page are copyright by us and published under the Creative Commons Attribution-NonCommercial 4.0 International License. This means that you must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. You may not use the material for commercial purposes.

2020 Challenges

Challenge and Leaderboard Details

For 2019 Challenge Results and winners, go to: Challenge 2019 Details.

EPIC-Kitchens Challenges @CVPR2020, Seattle, Washington- Dates

Jan 1, 2020
EPIC-Kitchens Challenges 2020 Launched!
May 29, 2020
Server Submission Deadline at 23:59:59 GMT
Jun 5, 2020
Deadline for Submission of Technical Reports
June 14-19, 2020
Results announced at EPIC@CVPR2020 and ActivityNet@CVPR2020 Workshops

Challenges Guidelines

The three challenges below and their test sets and evaluation servers are available via CodaLab. The leaderboards will decide the winners for each individual challenge. When comparing to state of the art (i.e. in papers), results should be reported on both test sets (S1 and S2) via submitting your predictions to the evaluation server.

For each challenge, the CodaLab server page details submission format and evaluation metrics. To enter any of the three competitions, you need to register an account for that challenge using a valid institute (university/company) email address. A single registration per research team is allowed. For all challenges the maximum submissions per day is limited to 1, and the overall maximum number of submissions per team is limited to 50 overall, submitted once a day.

To submit your results, follow the JSON submission format, upload your results and give time for the evaluation to complete (in the order of several minutes). After the evaluation is complete, the results automatically appear on the public leaderboards but you are allowed to withdraw these at any point in time.

To participate in the challenge, you need to have your results on the public leaderboard, along with an informative team name (that represents your institute or the collection of institutes participating in the work), as well as brief information on your method. You are also required to submit a report on your method (in the form of 2-6 pages) to the EPIC@CVPR workshop by June 5th, detailing your entry's technical details.

To submit your technical report, use the CVPR 2020 camera ready author kit (no blind submission), and submit a report of 2-6 pages inclusive of any references to the EPIC@CVPR2020 CMT3 website (Link soon). Please select the track "EPIC-Kitchens 2020 Challenges - Technical Papers" when submitting your pdf. These technical reports will be combined into an overall report of the EPIC-Kitchens challenges.

Make the most of the starter packs available with the challenges, and should you have any questions, please use our info email

Frequently Asked Questions

  • Q. Who is allowed to participate?
    A. Any researcher, whether in academia or industry, is invited to participate in the EPIC-Challenges. We only request a valid official email address, associated with an institution, for registraton. This ensures we limit the number of submissions per team. Do not use your personal email for registration. You registration request will be declined without further explanation.
  • Q. Can I participate in more than one challenge? Do I need to register separately for each challenge?
    A. Yes, and yes. You can participate in all three challenges but you need to register separately for each. Winners for each challenge will be announced and there will be no 'overall' winner across challenges.
  • Q. Can I get additional submission limits to debug my file format?
    A. No. Please check your format in advance. We do not offer additional allowance for submission failures.
  • Q. Can I participate in the challenge but not submit a report describing my method?
    A. We do not allow that option. Entries to the challenge will only be considered if a technical report is submitted on time. This should not affect later publications of your method if you restrict your report to 4 pages including references.
  • Q. Should I disclose the team's name on the public leaderboard?
    A. Yes, you need to have a team name that represents your institution before the server's closing time.
  • Q. What happens after the server closes?
    A. We will close the test server after the deadline and until the workshops, but open it again later for normal submissions of future papers.
  • Q. Are there any prizes given?
    A. This is under discussion. Certificates will be awarded.
  • Q. Can I add other members to my team?
    A. Yes you can.
  • Q. What is the submission limit?
    A. Once a day, with a maximum of 50 submissions.
  • Q. Is the submission limit for the user or for the team?
    A. For the team.
  • Q. Can you give me access to features of pre-trained models of baselines?
    A. Yes, we hare released several action recognition models tuned for EPIC-Kitchens here

Action-Recognition Challenge

Given a trimmed action segment, the challenge is to classify the segment into its action class composed of the pair of verb and noun classes. To participate in this challenge, predictions for all segments in the seen (S1) and unseen (S2) test sets should be provided. For each test segment we require the confidence scores for each verb and noun class.

Get started with the starter pack

Submit your results on CodaLab website

Sample qualitative results from the challenge's baseline

Action-Anticipation Challenge

Given an anticipation time, set to 1s before the action starts, the challenge is to classify the future action into its action class composed of the pair of verb and noun classes. To participate in this challenge, predictions for all segments in the seen (S1) and unseen (S2) test sets should be provided. For each test segment we require the confidence scores for each verb and noun class.

Submit your results on CodaLab website

Sample qualitative results from the challenge's baseline

Object-Detection Challenge

This challenge focuses on object detection and localisation. Note that our annotations only capture the ‘active’ objects pre-, during- and post- interaction. To participate in the challenge bounding box predictions with confidence scores should be submitted for sampled frames from the seen (S1) and unseen (S2) test sets.

Submit your results on CodaLab website

Sample qualitative results from the challenge's baseline

The Team

We are a group of researchers working in computer vision from the University of Bristol, University of Toronto, and University of Catania.

Dima Damen

Principal Investigator
University of Bristol
United Kingom

Giovanni Maria Farinella

University of Catania

Sanja Fidler

Co-I (2017-2019)
University of Toronto, NVIDIA AI Research

Davide Moltisanti

(Apr 2017 - )
University of Bristol

Michael Wray

(Apr 2017 - )
University of Bristol

Hazel Doughty

(Apr 2017 - )
University of Bristol

Toby Perrett

(Apr 2017 - )
University of Bristol

Antonino Furnari

(Jul 2017 - )
University of Catania

Jonathan Munro

(Sep 2017 - )
University of Bristol

Evangelos Kazakos

(Sep 2017 - )
University of Bristol

Will Price

(Oct 2017 - )
University of Bristol

Jian Ma

(Sep 2019 - )
University of Bristol


The dataset is sponsored by: