datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
OpenVid-60k-split
Combination of part_id's from bigdata-pw/OpenVid-1M and video data from nkp37/OpenVid-1M.
This is a 60k video split of the original dataset for faster iteration during testing. The split was obtained by filtering on aesthetic and motion scores by iteratively increasing their values until there were at most 1000 videos. Only videos containing between 80 and 240 frames were considered.
from datasets import load_dataset, disable_caching, DownloadMode
from torchcodec.decoders import… See the full description on the dataset page: https://huggingface.co/datasets/finetrainers/OpenVid-60k-split.OpenVid-10k-split
Combination of part_id's from bigdata-pw/OpenVid-1M and video data from nkp37/OpenVid-1M.
This is a 10k video split of the original dataset for faster iteration during testing. The split was obtained by filtering on aesthetic and motion scores by iteratively increasing their values until there were at most 1000 videos. Only videos containing between 80 and 240 frames were considered.
from datasets import load_dataset, disable_caching, DownloadMode
from torchcodec.decoders import… See the full description on the dataset page: https://huggingface.co/datasets/finetrainers/OpenVid-10k-split.OpenVid-1k-split
Combination of part_id's from bigdata-pw/OpenVid-1M and video data from nkp37/OpenVid-1M.
This is a 1k video split of the original dataset for faster iteration during testing. The split was obtained by filtering on aesthetic and motion scores by iteratively increasing their values until there were at most 1000 videos. Only videos containing between 80 and 240 frames were considered.
Loading the data:
from datasets import load_dataset, disable_caching, DownloadMode
from… See the full description on the dataset page: https://huggingface.co/datasets/finetrainers/OpenVid-1k-split.