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.
Because Canon does not host this file on their public support pages, the internet is flooded with third-party download links. Many of these links contain malware, adware, or ransomware disguised as the printer utility. Safe Downloading Practices
: The software allows technicians to change the internal region code of the printer, which can be necessary for compatibility with local ink cartridges. How to Use the Service Tool
The is a professional, Windows-based utility used by technicians to maintain and calibrate Canon inkjet printers. It is primarily sought by users to resolve the 5B00 error , which indicates that the printer's waste ink absorber (the "pampers") is full and must be reset to resume operation. Core Functions and Capabilities
To use the software, the printer must first be placed in . This usually involves a physical button sequence (e.g., holding Power and pressing Stop 5–6 times). Service Tool Canon - Canon, Inc. Software Informer. Software Canon Service Tool V.4906 Download
A small window saying "A function was finished" will pop up. Click .
If your Canon printer suddenly stops working and displays an error code like "5B00" or "1700," your device isn't necessarily broken. These codes usually mean the waste ink pad counter is full. Canon design incorporates these counters to prevent ink from overflowing inside the printer.
: G1000, G2000, G3000, G4000, G1400, G2400, G3400 Because Canon does not host this file on
Ultimate Guide to Canon Service Tool V.4906: Safe Download and Usage
Why do technicians still warn against casual use? Here are real-world failures:
Instead of paying an authorized service center (often $100+) to replace the pad and run the official Service Tool, users seek the V.4906 download to perform the reset themselves. This tool forces the printer’s EEPROM to reset the counter to zero. How to Use the Service Tool The is
While still holding the Power button, press the button 5 times (or 6 times for some G-series models). The green power LED will flash with each press.
Keep holding the button, but release the Stop/Reset button.
: Triggers deep cleaning sequences more powerful than standard driver utilities.
– I cannot provide a direct download link because:
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.