datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
3D-DefectBench
3D-DefectBench
A controlled benchmark for evaluating vision-language models (VLMs) as fine-grained judges of
defects in text-to-3D generation.
3D-DefectBench is a VLM-as-a-judge benchmark for detecting fine-grained defects in textured 3D
meshes. It lets you measure how well any VLM judge aligns with human judgment: run your judge over the
assets and score its predictions against the human defect labels provided here.
Each example pairs a text prompt with a generated, textured 3D… See the full description on the dataset page: https://huggingface.co/datasets/zzhao0500/3D-DefectBench.humancentric-scenes-ai
HumanCentric-Scenes-AI
A multimodal benchmark of 296 AI-generated human-centric scenes across four domains:
CCTV / surveillance imagery (Set 2, 85 images). Midjourney-generated stills that mimic low-resolution security-camera footage — parking lots, building interiors, outdoor public spaces — designed to test whether detection cues survive heavy compression and low-light noise.
Occupation × gender portraits (Set 3, 128 images). A balanced 64-occupation × 2-gender paired design… See the full description on the dataset page: https://huggingface.co/datasets/rjmaftv33/humancentric-scenes-ai.Recruitment-Task-3
DeepWeeds - AI-MED AGH convenience mirror
This is a convenience mirror of the official DeepWeeds image archive and the
upstream annotations pinned to a specific commit. original/images.zip is
preserved unchanged; images are not extracted or duplicated here. models.zip
from the source authors is deliberately not mirrored.
Dataset facts
17,509 in-situ images from Queensland, Australia.
Nine classes: eight weed species plus Negative.
The authors publish five folds… See the full description on the dataset page: https://huggingface.co/datasets/AI-MED-AGH/Recruitment-Task-3.