| ## Easy Use | |
| VE-Bench can be installed with a single ``pip`` command. Since the model employs normalization during training, its output does not represent absolute scores. We **recommend performing comparisons between video pairs**, as demonstrated below: | |
| ``` | |
| pip install vebench | |
| ``` | |
| When comparing videos: | |
| ``` | |
| from vebench import VEBenchModel | |
| evaluator = VEBenchModel() | |
| score1 = evaluator.evaluate('A black-haired boy is turning his head', 'assets/src.mp4', 'assets/dst.mp4') | |
| score2 = evaluator.evaluate('A black-haired boy is turning his head', 'assets/src.mp4', 'assets/dst2.mp4') | |
| print(score1, score2) # Score1: 1.3563, Score2: 0.66194 | |
| ``` |