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.
AET-Bench: A Comprehensive Benchmark for Atomic Electron Tomography
A NeurIPS 2026 Datasets & Benchmarks submission: 755 samples, 6 difficulty tiers, 14 primary reconstruction methods, and 4 evaluation tasks spanning static 3D, atom-level localization, element classification, and 4D dynamic tracking of nanoparticles at the atomic scale.
AET-Bench introduces atom identity persistence and trajectory RMSD — two atom-centric metrics that expose a striking failure mode: state-of-the-art 4D Gaussian-splatting reconstructions reach SSIM 0.94 on AET data yet track only 15 % of individual atoms across time. The paper argues that pixel-level metrics systematically mislead atomic-scale evaluation because the HAADF-STEM $Z^\gamma$ forward model decouples density from atom count.
Highlights
| Samples | 755 across 6 tiers (analytical, binary alloys, 4D dynamics, multislice, real, Brownian Pt) |
| Methods | 14 primary + supplementary baselines from 5 algorithmic families |
| Tasks | T1 static 3D · T2 atom localization · T3 element classification · T7 4D dynamics |
| Results | 14,000+ rows in data/aet_bench/results.jsonl |
| Findings | 7 findings including 4D paradox, dose sensitivity, sim-to-real gap |
Quickstart
# Install
conda env create -f environment.yml
conda activate aet-bench
# Run one method on one sample
python scripts/run_benchmark.py \
--subset mono --sample Cu-0165-00 \
--method wbp --task T1
# Regenerate paper figures & tables
python scripts/generate_paper_assets.py
# Audit the paper result log and record its SHA256/provenance
python scripts/validate_results.py data/aet_bench/results.jsonl
python scripts/generate_paper_assets.py --results data/aet_bench/results.jsonl
# Fill GPU-heavy missing baselines; defaults to one worker per visible GPU
# Uses the existing aet4dgs conda env unless PYTHON_BIN is set.
bash scripts/start_gpu_extras.sh
# 4D-paradox mechanism analysis (reproduces Appendix D)
python scripts/analysis/fourd_mechanism.py
Repository Layout
data/aet_bench/ # benchmark data + results.jsonl (git-ignored)
scripts/
run_benchmark.py # unified entry point: --subset --method --task
generate_paper_assets.py # regenerates every paper figure + LaTeX table
bench_bootstrap.py # 95 % bootstrap CI utility
analysis/ # paradox / dose / ranking analyses
metrics/ # FSC, Hungarian atom matching, trajectory metrics
baselines/ # thin wrappers around each method
paper/aet_bench.tex # NeurIPS submission
paper/croissant.json # Croissant 1.0 dataset card
Adding a New Method
Methods are registered in scripts/run_benchmark.py under METHOD_REGISTRY.
Each entry maps a method name to a callable f(data, sample_id) -> volume.
See CONTRIBUTING.md for the detailed contract and examples.
Data
Simulated tiers are reproducible from scratch via
scripts/generate_haadf_data.py (analytical $Z^\gamma$),
scripts/generate_abtem_multislice.py (dynamical scattering), and
scripts/generate_dose_tier.py (dose-controlled Poisson noise).
Real experimental tier sources 26 nanoparticles from published Zenodo
records (DOIs listed in paper/croissant.json). The NeurIPS review
release bundle is hosted at
https://huggingface.co/datasets/pone7/4D-ATE.
Citation
Submitted to NeurIPS 2026 Datasets & Benchmarks. BibTeX will be added after the review window.
License
CC-BY-4.0 for the benchmark data and paper; MIT for the reference reconstruction and evaluation code.
Hugging Face Large-File Parts
Some tier archives are split into parts to keep individual Hub files below the recommended size limit. Concatenate parts in lexical order before extracting the reconstructed tar archive:
cat aet-bench-bi.tar.part-* > aet-bench-bi.tar
sha256sum aet-bench-bi.tar
Expected SHA256 for aet-bench-bi.tar: 6661b6b85c267e28010d479e854a95a84cf5bab8dde45cc0ed6a247b7a85b3e2.
cat aet-bench-mono.tar.part-* > aet-bench-mono.tar
sha256sum aet-bench-mono.tar
Expected SHA256 for aet-bench-mono.tar: 926adcfb2fc73c1d62b8c44befc9963540899f55a07669022da99c0376674602.
cat aet-bench-real.tar.part-* > aet-bench-real.tar
sha256sum aet-bench-real.tar
Expected SHA256 for aet-bench-real.tar: 7e433949b0c53d739b594bf724d4505e7e348b7ca1113e0838da630ae242e43d.
Uploaded-file checksums are in SHA256SUMS; reconstructed archive checksums are in ORIGINAL_ARCHIVE_SHA256SUMS.
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