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 "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 299, in get_dataset_config_info
for split_generator in builder._split_generators(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 83, 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 "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 353, in get_dataset_split_names
info = get_dataset_config_info(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 304, 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.
MeshFleet: Filtered and Annotated 3D Vehicle Dataset for Domain Specific Generative Modeling
This is a processed version of the MeshFleet Dataset using the dataset pipeline from TRELLIS. It contains all the 3D models from the original dataset, but is already preprocessed and ready to use with the TRELLIS training pipeline. For fast loading and processing the dataset is chunked and compressed to webdataset files. All files for each object are stored in a separate file. You can either load the whole dataset (about 190 GB) or just a subset of it. The dataset is stored in the train and test folders.
The reconstruct the folder structure expected by the TRELLIS training pipeline, you can use the 'reconstruct_data.py' script:
python reconstruct_data.py --shard_dir ./data/meshfleet_trellis/train --output_dir ./data/meshfleet_trellis/train_reconstructed
Information From The Base Dataset
This is a curated collection of 3D car models derived from Objaverse-XL described in MeshFleet: Filtered and Annotated 3D Vehicle Dataset for Domain Specific Generative Modeling. The MeshFleet dataset provides metadata for 3D car models, including their SHA256 from Objaverse-XL, vehicle category, and size. The core dataset is available as a CSV file: meshfleet_with_vehicle_categories_df.csv. You can easily load it using pandas:
import pandas as pd
meshfleet_df = pd.read_csv('./data/meshfleet_with_vehicle_categories_df.csv')
print(meshfleet_df.head())
The actual 3D models can be downloaded from Objaverse-XL using their corresponding SHA256 hashes. Pre-rendered images of the MeshFleet models are also available within the Hugging Face repository in the renders directory, organized as renders/{sha256}/00X.png. The code used to generate this dataset can be found at https://github.com/FeMa42/MeshFleet.git.
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