datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
SyntheticFacesHQ
Part 1 consists of 89,785 HQ 1024x1024 curated face images. It uses "inspiration" images from Artstation-Artistic-face-HQ dataset (AAHQ), Close-Up Humans dataset and UIBVFED dataset.
Part 2 consists of 91,361 HQ 1024x1024 curated face images. It uses "inspiration" images from Face Synthetics dataset and by sampling from the Stable Diffusion v1.4 text to image generator using varied face portrait prompts.
Part 3 consists of 118,358 HQ 1024x1024 curated face images. It uses "inspiration" images… See the full description on the dataset page: https://huggingface.co/datasets/bitmind/SyntheticFacesHQ.SyntheticFacesHighQuality-T2I122K curated 1024x1024 face images from text2image models (Flux1, SDXL, Dalle3)
This dataset consists of 122,726 high quality 1024x1024 curated face images, and was created by creating random prompt strings that were sent to multiple "text to image" models (Flux1.pro, Flux1.dev, Flux1.schnell, SDXL, DALL-E 3) and dropping bad generations using a semi manual curation process.
The prompts used to generate the dataset are of various faces with different attributes and various conditions to make… See the full description on the dataset page: https://huggingface.co/datasets/bitmind/SyntheticFacesHighQuality-T2I.synthetic-watch-faces-dataset
Synthetic Watch Faces Dataset
A synthetic dataset of analog watch faces displaying various times for training vision models in time recognition tasks.
Dataset Description
This dataset consists of randomly generated analog watch faces showing different times. Each image contains a watch with hour and minute hands positioned to display a specific time. The dataset is designed to help train and evaluate computer vision models and Vision-Language Models (VLMs) for time… See the full description on the dataset page: https://huggingface.co/datasets/elischwartz/synthetic-watch-faces-dataset.children-faces-synthetic-deepfake-detection
Dataset of synthetic kids faces
Children are missing in most deepfake prevention and age detection datasets. This underrepresentation leads to biased models, weaker age-restricted access controls, and increased vulnerability to deepfake attacks. The Synthetic Children Faces Dataset fills that gap: it expands demographic coverage while avoiding any real-person data
Dataset Features
Dataset Size: Thousands of high-quality images across 5–9, 9–12, 12–16 age groups… See the full description on the dataset page: https://huggingface.co/datasets/AxonData/children-faces-synthetic-deepfake-detection.LookismBench-SyntheticFaces
Summary
LookismBench-Faces is a large-scale synthetic dataset designed to study algorithmic lookism in text-to-image (T2I) generative models and downstream computer vision systems.
The dataset consists of 26,400 synthetic human faces generated using Stable Diffusion 2.1 and 3.5 Medium under controlled prompt conditions. It enables systematic analysis of how generative AI models encode associations between facial attractiveness, semantic attributes, and gender classification… See the full description on the dataset page: https://huggingface.co/datasets/adi-gulati/LookismBench-SyntheticFaces.Unsupervised_Synthetic_Facessynthetic-face-sdxl-instantid-bench
Synthetic Face Detection Benchmark — SDXL+InstantID
Version: v1.0.0 · Build date: 2026-05-16 · Rows: 26492
Evaluation benchmark for synthetic-face detection under platform-realistic
conditions. Sampled to satisfy the ISO/IEC 19795 floor of 300 samples per
demographic subgroup across a 6×2 (skin tone × gender) cell grid. Not
training data; not licensed for commercial use.
See release.json for build provenance, manifest.csv for per-row
license attestation, LICENSES.csv for the… See the full description on the dataset page: https://huggingface.co/datasets/danb21/synthetic-face-sdxl-instantid-bench.