datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
safemaize-screening3-v3
SafeMaize screening3_v3
Public maize images for screening experiments on northern/turcicum leaf blight-like
symptoms and fall armyworm, with healthy maize as the comparison. Images keep their
original bytes and are stored as Parquet shards, so no loose image files are published.
Class
Core images
healthy
29,448
nlb_tlb_like
23,889
faw_feeding_injury
10,459
Partition
Images
Shards
train
43,945
74
val
5,669
13
calibration
5,669
13
test
8,513
11… See the full description on the dataset page: https://huggingface.co/datasets/saiteja33/safemaize-screening3-v3.tool-safety-dataset
Tool Safety Dataset
Dataset Description
The Tool Safety Dataset is a specialized collection of tool images with detailed safety and usage information. It combines visual data with comprehensive metadata about various hand tools, making it valuable for both computer vision tasks and safety training applications.
Dataset Summary
Type: Image dataset with bounding boxes and detailed tool information
Size: Multiple splits (train/test/validation)
Format: Images with… See the full description on the dataset page: https://huggingface.co/datasets/akameswa/tool-safety-dataset.kamari-safe-open-v0
Kámárí-Safe Open v0 (benchmark)
A frozen, leakage-free benchmark for African-tailored age verification. It holds manifests and
split tables, not raw images (paths, hashes, labels, skin band, quality). Use it to measure age
accuracy and, more importantly, child-safety.
Headline metric
Minor-Pass-Through Rate (MPTR) is the headline: the fraction of true minors a model passes as
adults, reported overall, at 21, and for dark + brown skin. Report MPTR alongside MAE; a… See the full description on the dataset page: https://huggingface.co/datasets/Shinzmann/kamari-safe-open-v0.SafeEditBench
SafeEditBench
WARNING: This repository contains content that might be disturbing!
A benchmark for evaluating content safety detection in image editing. SafeEditBench tests whether vision-language models can correctly identify policy violations in edited images across diverse content safety policies.
Dataset
SafeEditBench contains image pairs (original + edited) annotated with safety labels and policy violation types.
Split
Images
Directory
train
901… See the full description on the dataset page: https://huggingface.co/datasets/tyodd/SafeEditBench.