# UCF101
UCF101 is an action recognition data set of realistic action videos,
collected from YouTube, having 101 action categories. This data set is an
extension of UCF50 data set which has 50 action categories.
With 13,320 videos from 101 action categories, UCF101 gives the largest
diversity in terms of actions and with the presence of large variations in
camera motion, object appearance and pose, object scale, viewpoint,
cluttered background, illumination conditions, etc, it is the most
challenging data set to date. As most of the available action recognition
data sets are not realistic and are staged by actors, UCF101 aims to
encourage further research into action recognition by learning and
exploring new realistic action categories.
The videos in 101 action categories are grouped into 25 groups, where each
group can consist of 4-7 videos of an action. The videos from the same
group may share some common features, such as similar background, similar
viewpoint, etc.
**Details**
- Dataset name: `ucf101`
- Dataset source: [https://www.crcv.ucf.edu/research/data-sets/ucf101](https://www.crcv.ucf.edu/research/data-sets/ucf101)
- Dataset license: CC0-1.0
- Dataset size: 6.48 GB
- Tags: `video, action-recognition`
- Supported splits: `train, test`
- ZooDataset class:
[`UCF101Dataset`](../../api/fiftyone.zoo.datasets.base.md#fiftyone.zoo.datasets.base.UCF101Dataset)
**Example usage**
Python
CLI
```python
import fiftyone as fo
import fiftyone.zoo as foz
import fiftyone.utils.video as fouv
dataset = foz.load_zoo_dataset("ucf101", split="test")
# Re-encode source videos as H.264 MP4s so they can be viewed in the App
fouv.reencode_videos(dataset)
session = fo.launch_app(dataset)
```
```shell
fiftyone zoo datasets load ucf101 --split test
# Re-encode source videos as H.264 MP4s so they can be viewed in the App
fiftyone utils transform-videos ucf101-test --reencode
fiftyone app launch ucf101-test
```
#### NOTE
In order to work with video datasets, you’ll need to have
[ffmpeg installed](../../installation/troubleshooting.md#troubleshooting-video).
Also, if you don’t already have a utility to uncompress `.rar` archives,
you may need to install one. For example, on macOS:
```shell
brew install rar
```
