errors
Datasets
All datasets matching “errors”Errors_Additive_Manufacturing_Plattform_Cam
Errors_Additive_Manufacturing_Plattform_Cam
3D Printing Nozzle Camera – YOLO Object Detection Dataset
This Repository is part of the Project: Künstliche Intelligenz zur Automatiserten Fehlerkorrektur in der Additiven Fertigung(Förderkennzeichen: 16IS23050B).
This dataset contains images captured from a camera positioned to capture the whole plattform of a 3D printer.
The task is object detection of both regular print elements and typical printing defects.
The dataset is… See the full description on the dataset page: https://huggingface.co/datasets/DasKunststoffZentrumSKZ/Errors_Additive_Manufacturing_Plattform_Cam.Errors_Additive_Manufacturing_Nozzle_Cam
Errors_Additive_Manufacturing_Nozzle_Cam
3D Printing Nozzle Camera – YOLO Object Detection Dataset
This Repository is part of the Project: Künstliche Intelligenz zur Automatiserten Fehlerkorrektur in der Additiven Fertigung(Förderkennzeichen: 16IS23050B).
This dataset contains images captured from a camera positioned directly next to the nozzle of a 3D printer.
The task is object detection of both regular print elements and typical printing defects.
The dataset is… See the full description on the dataset page: https://huggingface.co/datasets/DasKunststoffZentrumSKZ/Errors_Additive_Manufacturing_Nozzle_Cam.Spotlight-VideoGen-Errors
Spotlight Dataset
Spotlight: Identifying and Localizing Video Generation Errors Using VLMs
Aditya Chinchure, Sahithya Ravi, Pushkar Shukla, Vered Shwartz, Leonid Sigal
🎉 Accepted to ECCV 2026
🌐 Project Page
Summary
Spotlight is a benchmark for evaluating whether Vision Language Models (VLMs) can precisely
localize and explain errors in AI-generated videos. It contains 600 videos generated by
three state-of-the-art Text-to-Video (T2V) models —… See the full description on the dataset page: https://huggingface.co/datasets/UBC-ViL/Spotlight-VideoGen-Errors.quran-recitation-errors
Examples
Loading dataset:
from datasets import load_dataset
ds = load_dataset('sobolev210/quran-recitation-errors',)
print(ds["train"][0])
errorstabular-errors-v1
TabFix multilingual table error pairs — version 2.0
This release keeps 18 business error categories and separates executable deterministic detection from two residual neural categories: text.encoding and text.spelling. The same repository and family-disjoint splits are retained.
Split
Records
Open-vocabulary views
train
27948
3260
validation
17127
1844
test
32776
3540
The seven string columns remain id, split, family_id, clean_xml, corrupt_xml, errors… See the full description on the dataset page: https://huggingface.co/datasets/Antix5/tabular-errors-v1.