datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
notch-beam-2d-impact
NotchBeam2D-Impact — StructBench canonical dataset
Download
One case, one file — fetch exactly what you need (pip install huggingface_hub):
from huggingface_hub import hf_hub_download, snapshot_download
# one case
path = hf_hub_download("StructBench/notch-beam-2d-impact",
filename="<case_id>.h5", repo_type="dataset")
# the full archive (resumable; cached under HF_HOME)
root = snapshot_download("StructBench/notch-beam-2d-impact"… See the full description on the dataset page: https://huggingface.co/datasets/StructBench/notch-beam-2d-impact.taylor-impact-2d
Taylor2D-Impact — StructBench canonical dataset
Download
One case, one file — fetch exactly what you need (pip install huggingface_hub):
from huggingface_hub import hf_hub_download, snapshot_download
# one case
path = hf_hub_download("StructBench/taylor-impact-2d",
filename="<case_id>.h5", repo_type="dataset")
# the full archive (resumable; cached under HF_HOME)
root = snapshot_download("StructBench/taylor-impact-2d", repo_type="dataset")… See the full description on the dataset page: https://huggingface.co/datasets/StructBench/taylor-impact-2d.sam3-low-dice-2d-nnunet
SAM3 low-Dice 2D datasets for nnU-Net
Private research export of two small 2D datasets on which the balanced-finish
SAM3 LoRA validation Dice was below 0.5. The purpose is to test whether a
dataset-specific nnU-Net can fit these data and to distinguish data/training
limitations from inference bugs.
Dataset
SAM3 Dice
SAM3 IoU
Evaluated validation images
Actual SAM3 training images
DRIVE
0.212233
0.118717
2
14
RAVIR
0.224709
0.128455
2
16
The two-image validation… See the full description on the dataset page: https://huggingface.co/datasets/MedicalSAM3/sam3-low-dice-2d-nnunet.cadquery-creating-basic-2d-and-3d-forms2d-graphene-conductivity-coherence-loss-v0.1Goal
Predict irreversible conductivity dropin graphene devices.
Core idea
Failure does not arrive as one threshold.
It arrives when critical relationships collapse:
Raman D/G ratiomobilitythermal conductivitysheet resistance
stop telling one coherent story.
Inputs
operational hours
Raman D/G ratio
carrier mobility
thermal conductivity
sheet resistance
defect density
humidity exposure
Required outputs
conductivity_coherence_score
decoupling_flag
decoupling_type
irreversible_drop_probability… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/2d-graphene-conductivity-coherence-loss-v0.1.2d-tmd-photoluminescence-quenching-v0.1Goal
Forecast optoelectronic collapsein monolayer TMD devices.
Core idea
Performance fails when coupling collapses:
defect densityPL intensityquantum yieldstrain and peak shift
stop tracking as one system.
Inputs
operational hours
defect density
normalized PL intensity
quantum yield
peak shift
strain
temperature cycles
humidity exposure
Required outputs
pl_coherence_score
quench_flag
quench_type
performance_collapse_risk
quench_horizon_hr
stabilization_actions
Quench types… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/2d-tmd-photoluminescence-quenching-v0.1.2d-latticesthai-myanmar-2dlmmma_2datasetbtc_2d_packreddit_analysis_2Dproduct_profile_2d_data_for_eval