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/python3.14/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
for split_generator in builder._split_generators(
~~~~~~~~~~~~~~~~~~~~~~~~~^
StreamingDownloadManager(base_path=builder.base_path, download_config=download_config)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 80, in _split_generators
raise ValueError(
...<2 lines>...
)
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 68, in compute_split_names_from_streaming_response
for split in get_dataset_split_names(
~~~~~~~~~~~~~~~~~~~~~~~^
path=dataset,
^^^^^^^^^^^^^
config_name=config,
^^^^^^^^^^^^^^^^^^^
token=hf_token,
^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
info = get_dataset_config_info(
path,
...<6 lines>...
**config_kwargs,
)
File "/usr/local/lib/python3.14/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.
CS 498 homework data
hw3_data.zip is the frozen, required HW3 Car-detection package (SHA-256
d70aa8544b52d56ca0569110434562ff74296ee80f63c8cd8b1c47364fc79523).
It contains train.npz, val.npz, and test.npz over 5,977/730/774
drive-disjoint KITTI frames. The processed targets use grid-aligned-v2.
hw3_multiclass_data.zip is a small optional bonus-only companion
(SHA-256 58811f16591d5ae9de97da8fa3bf871bfb65d101f8b6b5ac87666a5aa56e489c).
Its three aligned label archives add Pedestrian and Cyclist boxes/class IDs;
its Car boxes exactly match the core package. Use the handout's
data/download_multiclass_data.py after data/download_data.py. The companion
does not replace core BEVs, split membership, or the required result protocol.
Both packages derive from the
KITTI 3D object-detection training set.
Use is educational in CS 498; cite KITTI. Validation controls all model and
threshold decisions, and the final test split must remain held out until
those choices are frozen. File checksums are in SHA256SUMS.
HW4: compact NeRF Lego data
hw4_data.zip contains the 200 x 200 Blender-format Lego subset used by the
CS 498 HW4 handout: 50 training, 5 validation, and 12 held-out test poses.
Its SHA-256 is 502c9216170111bd9a6b7f7204442b23b69020924a11229ec0a820f825b3fbad. The handout downloader verifies every
member against hw4_SHA256SUMS before installation. The images are derived
from the NeRF synthetic Lego example data and retain the original camera
transforms; use is limited to the course and other permitted educational use.
Final Project - Track 1: Multi-View 3D Semantic Occupancy Prediction
Release: Wednesday, November 4, 2026. Due: December 9, 2026, 23:59 America/Chicago. Use the current Track 1 student package from the course.
final-track1/data/manifest.json indexes 220 per-house archives: 200 Train,
10 Val and 10 Test houses. Each independent sample has three overlapping RGB
images from one camera center, optional depth, intrinsics and poses. Train/Val
include prepared 25 cm targets; Test includes inputs only. Test ground truth
is not public. No simulator installation or data generation is needed.
From the released project root, use python run.py download --split train
(or --split val / --split test). The downloader pins revision
192c75d954d62969db82e2e3a018de2f71d4a48b, verifies SHA-256 checksums and safely
extracts the requested split. The canonical prediction grid is (40,14,80) in
(z,y,x) order. Ranking is completion mIoU macro-averaged across all ten Test
houses. See the course handout for the target policy and allowed inputs.
The supported execution paths are Ubuntu with an NVIDIA GPU and Google Colab.
Colab is the fallback and is recommended for Mac and Windows users. Follow
SETUP.md and notebooks/track1_colab.ipynb in the student package; run
python run.py doctor before training. Public downloads need no HF account/token.
Data are rendered using ProcTHOR and AI2-THOR; cite their papers. Educational redistribution is authorized for this course. Other homework packages above are unchanged.
Final Project - Track 2: Complete 3D Scene Reconstruction
30 Dev / 10 Val / 20 Test posed-RGB scenes. Dev/Val include evaluation GT; Test includes RGB and calibration only. No scene generation is required. See the Track 2 data guide for the pinned downloader, permitted use, local Val CSV rehearsal and lightweight Kaggle workflow. The student package is distributed by the course. Test targets remain private.
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