datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
tv-task2-clean-aug1_illumination
tv-task2-clean-aug1_illumination
LeRobot v3 dataset for banana pick-and-place with a SO-101 robot arm.Built for Vision-Language-Action (VLA / SmolVLA) training.
Dataset summary
Property
Value
Episodes
1 184
Frames
139 368
FPS
10
Task prompts
33
Robot
SO-101 (so_follower)
Camera
Front (480 × 640, AV1)
Action space
6-DoF joint positions
Format
LeRobot v3.0
Construction
Base: ETHrobotlearning/tv-task2-clean-aug1 (592 episodes… See the full description on the dataset page: https://huggingface.co/datasets/ETHrobotlearning/tv-task2-clean-aug1_illumination.euv-illumination-pupil-coherence-baseline-mapping-v0.1
What this dataset is
This dataset benchmarks baseline coherence between:
illumination mirror settingsmeasured pupil shapeprinted wafer outcomes
It captures when the illumination system stays inside a stable, predictable coupling regime.
Task
Given illumination and pupil metrics, predict:
coherence_score
A scalar in 0..1 describing how strongly pupil shape and mirror settings predict wafer CD uniformity and sidewall angle.
Inputs
illum_mirror_setting_vector… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/euv-illumination-pupil-coherence-baseline-mapping-v0.1.euv-illumination-pupil-coherence-drift-detection-v0.1
Purpose
Detect when illumination-pupil coupling begins to drift toward loss of critical dimension control.
This dataset maps coherence decay between:
pupil geometrydose distributionprinted wafer metrics
The goal is to identify drift before feature collapse.
Task
Input system metrics.
Predict:
drift_scoredrift_flag
Format:
float,int
Example:
0.42,1
Why this matters
Lithography systems rarely fail instantly.They drift.
Early detection prevents yield loss and… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/euv-illumination-pupil-coherence-drift-detection-v0.1.tv-task2-clean-aug1_illumination-fixed
tv-task2-clean-aug1_illumination
LeRobot v3 dataset for banana pick-and-place with a SO-101 robot arm.Built for Vision-Language-Action (VLA / SmolVLA) training.
Dataset summary
Property
Value
Episodes
1 184
Frames
139 368
FPS
10
Task prompts
33
Robot
SO-101 (so_follower)
Camera
Front (480 × 640, AV1)
Action space
6-DoF joint positions
Format
LeRobot v3.0
Construction
Base: ETHrobotlearning/tv-task2-clean-aug1 (592 episodes… See the full description on the dataset page: https://huggingface.co/datasets/ETHrobotlearning/tv-task2-clean-aug1_illumination-fixed.test_illumination_augment_config3-green-blue-redeuv-illumination-cd-failure-horizon-and-correction-routing-v0.1
Purpose
Predict how close an EUV tool is to losing critical dimension controland determine the minimal corrective action.
The dataset links illumination coherence driftto downstream CD collapse risk.
Task
Given system state, predict:
failure_horizon_waferscorrection_route
Format:
float,string
Example:
45,illumination tuning
Why this matters
Yield loss begins before visible CD failure.
Early routing decisions prevent tool downtime and scrap.
This dataset… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/euv-illumination-cd-failure-horizon-and-correction-routing-v0.1.BSICLE-medieval-illumination-folio-bin-class-dataset
BSICLE Medieval Folio Illumination Dataset
This dataset contains 1,484 medieval and early modern folio images, dating approximately from the 7th to the mid-17th century. annotated for binary image classification: whether a folio contains illumination or not.
The dataset is intended to train and evaluate
lightweight computer vision models for the fast
detection of illuminated folios in medieval manuscript
corpora, especially in IIIF-based heritage and
research workflows.
Check… See the full description on the dataset page: https://huggingface.co/datasets/ENC-PSL/BSICLE-medieval-illumination-folio-bin-class-dataset.illuminationdb