The dataset viewer is not available for this subset.
Exception: SplitsNotFoundError
Message: The split names could not be parsed from the dataset config.
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
for split_generator in builder._split_generators(
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 82, in _split_generators
raise ValueError(
ValueError: The TAR archives of the dataset should be in WebDataset format, but the files in the archive don't share the same prefix or the same types.
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 65, in compute_split_names_from_streaming_response
for split in get_dataset_split_names(
^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
info = get_dataset_config_info(
^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 291, in get_dataset_config_info
raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
YAML Metadata Warning: The task_categories "time-series-classification" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other
gem-analysis-humanml3d
HumanML3D 数据集，包含 3D 人体运动数据和文本描述。
下载方式
方式 1: 下载压缩版 (推荐，避免 rate limit)
数据集包含 16 万+ 小文件，直接下载可能触发 rate limit。推荐下载压缩版：
# 使用 huggingface-cli
huggingface-cli download --repo-type dataset yonful/gem-analysis-humanml3d humanml3d.tar.gz --local-dir .
# 解压
tar -xzf humanml3d.tar.gz
或使用 Python:
from huggingface_hub import hf_hub_download
# 下载压缩文件
hf_hub_download(
repo_id="yonful/gem-analysis-humanml3d",
filename="humanml3d.tar.gz",
repo_type="dataset",
local_dir="."
)
# 解压
import tarfile
with tarfile.open("humanml3d.tar.gz", "r:gz") as tar:
tar.extractall()
方式 2: 直接下载所有文件
如果网络稳定，可以直接下载：
from huggingface_hub import snapshot_download
snapshot_download(
repo_id="yonful/gem-analysis-humanml3d",
repo_type="dataset",
local_dir="./HumanML3D"
)
数据集结构
HumanML3D/
├── new_joints/ # 3D 关节位置数据
├── new_joint_vecs/ # 旋转不变特征
├── texts/ # 文本描述
├── Mean.npy # 均值
├── Std.npy # 标准差
├── all.txt # 所有样本列表
├── train.txt # 训练集
├── val.txt # 验证集
├── test.txt # 测试集
└── train_val.txt # 训练+验证集
数据集信息
- 文件数量: ~160,000
- 原始大小: ~1.6 GB
- 压缩后大小: ~1.3 GB
许可证
请参考原始数据源的许可证要求。
引用
@inproceedings{guo2022generating,
title={Generating diverse and natural 3d human motions from text},
author={Guo, Chuan and Zou, Shihao and Zuo, Xinxin and Wang, Sen and Ji, Wei and Li, Xingyu and Cheng, Li},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages={5152--5161},
year={2022}
}
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