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quchenyuan/UCF101-ZIP

The UCF‑101 dataset is a widely used benchmark for video action recognition. It contains 13,320 videos across 101 action categories, totaling about 7.2 GB. Videos have a resolution of 320×240 pixels and durations ranging from 1 to 30 seconds. Originally collected from YouTube and manually annotated, a ZIP version is provided to replace the original RAR distribution for easier access. The dataset is suitable for research on video‑based action recognition, such as training and evaluating deep‑learning models.

Updated 4/26/2023
hugging_face

Description

Dataset Overview

Dataset Name

UCF‑101

Dataset Content

  • Number of Videos: 13,320
  • Number of Categories: 101
  • Video Resolution: 320×240 pixels
  • Video Duration: 1 to 30 seconds
  • Dataset Size: Approximately 7.2 GB

Dataset Characteristics

  • Contains 101 action categories, with 24 to 953 videos per category.
  • Videos sourced from diverse platforms, including YouTube.
  • Actions include a wide range of human activities such as basketball, cycling, cooking, etc.

Dataset Format

  • Original format: RAR; a ZIP version is now provided for better accessibility.

Dataset Usage

  • Suitable for video action‑recognition research projects, such as training and evaluating deep‑learning models.

Dataset Organization

  • Organized by action category; each category contains a folder of video files.
  • Filenames embed the action label and video ID; label information is provided separately.

Contact

Citation

  • When using this dataset, cite the following technical report: Khurram Soomro, Amir Roshan Zamir and Mubarak Shah, UCF101: A Dataset of 101 Human Action Classes From Videos in The Wild, CRCV‑TR‑12‑01, November 2012.

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Topics

Video Action Recognition
Deep Learning

Source

Organization: hugging_face

Created: Unknown

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