The goal of the Kinetics dataset is to help the computer vision and machine learning communities advance models for video understanding. Given this large human action classification dataset, it may be possible to learn powerful video representations that transfer to different video tasks.
The Kinetics-700-2020 dataset will be used for this challenge. Kinetics-700-2020 is a large-scale, high-quality dataset of YouTube video URLs which include a diverse range of human focused actions. The aim of the Kinetics dataset is to help the machine learning community create more advanced models for video understanding. It is an approximate super-set of both Kinetics-400, released in 2017, Kinetics-600, released in 2018 and Kinetics-700, released in 2019.
The dataset consists of approximately 650,000 video clips, and covers 700 human action classes with at least 700 video clips for each action class. Each clip lasts around 10 seconds and is labeled with a single class. All of the clips have been through multiple rounds of human annotation, and each is taken from a unique YouTube video. The actions cover a broad range of classes including human-object interactions such as playing instruments, as well as human-human interactions such as shaking hands and hugging.
More information about how to download the Kinetics dataset is available here.
If you're looking for free or alternative solutions, consider:
If you are running Windows 10 or Windows 11, Stardock actively supports Start11 . This tool is fully compatible with modern Windows architecture, receives regular security patches, and allows you to customize your Start menu to look like Windows 7, 10, or a modernized layout safely. Open-Source Alternatives If you're looking for free or alternative solutions,
: Cracked software is frequently unstable, prone to crashes, and cannot receive official security updates, leaving your system permanently vulnerable. This is the official, actively supported successor to Start8
This is the official, actively supported successor to Start8. It is fully optimized for Windows 10 and Windows 11, receiving regular security and feature updates directly from Stardock. It is actively maintained by the community on
: A highly popular, free, and open-source alternative. It is actively maintained by the community on GitHub, safe from malware, and allows you to restore classic Windows 7-style Start Menus on modern Windows versions without spending money.
Search terms that combine software names with keywords like "crack," "pre-activated," "torrentz," or specific years (like 2021) are frequently used as bait by cybercriminals. Downloading files from these unverified sources exposes your system to numerous hazards: 1. Malware and Trojan Bundles
1. Possible to use ImageNet checkpoints?
We allow finetuning from public ImageNet checkpoints for the supervised track -- but a link to the specific checkpoint should be provided with each submission.
2. Possible to use optical flow?
Flow can be used as long as not trained on external datasets, except if they are synthetic.
3. Can we train on test data without labels (e.g. transductive)?
No.
4. Can we use semantic class label information?
Yes, for the supervised track.
5. Will there be special tracks for methods using fewer FLOPs / small models or just RGB vs RGB+Audio in the self-supervised track?
We will ask participants to provide the total number of model parameters and the modalities used and plan to create special mentions for those doing well in each setting, but not specific tracks.