datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
nanopath-evals
NanoPath evaluation data
This is the immutable data mirror used by NanoPath probe protocol v2. It contains only the exact development records consumed by medarc/nanopath: selected THUNDER training/validation images, prepared development-only slide caches, and the two PathoROB subsets. manifest.json records SHA-256 checksums and binds the snapshot to the checked-in benchmark manifests.
No official THUNDER, HEST, or CPTAC classification test record is included. HEST is absent.… See the full description on the dataset page: https://huggingface.co/datasets/medarc/nanopath-evals.midjourney-dalle-sd-nanobananapro-dataset
Dataset Card: Midjourney, DALL-E, Stable Diffusion & Nano Banana Pro vs Real Images
Description
Dataset de classification binaire pour détecter les images générées par IA (Midjourney, DALL-E, Stable Diffusion et Nano Banana Pro) vs images réelles.
Dataset Structure
Train set: 10,000 images
Real: 5000 images
Fake (AI-generated): 5000 images
Test set: 2,000 images
Real: 1000 images
Fake (AI-generated): 1000 images
Features
{
"image": Image… See the full description on the dataset page: https://huggingface.co/datasets/julienlucas/midjourney-dalle-sd-nanobananapro-dataset.nano-receipts
🧾 Nano Receipts Dataset
A diverse collection of 2428 hyper-realistic synthetic receipt images generated using state-of-the-art text-to-image AI models.
🚀 Quick Start
from datasets import load_dataset
# Load dataset (fast parquet format!)
dataset = load_dataset("34data/nano-receipts")
# Access images
image = dataset["train"][0]["image"] # PIL Image
filename = dataset["train"][0]["filename"]
📊 Dataset Details
Total Images: 2428 receipts
Format:… See the full description on the dataset page: https://huggingface.co/datasets/34data/nano-receipts.nano-receipts
🧾 Nano Receipts Dataset
A diverse collection of 2428 hyper-realistic synthetic receipt images generated using state-of-the-art text-to-image AI models.
🚀 Quick Start
from datasets import load_dataset
# Load dataset (fast parquet format!)
dataset = load_dataset("34data/nano-receipts")
# Access images
image = dataset["train"][0]["image"] # PIL Image
filename = dataset["train"][0]["filename"]
📊 Dataset Details
Total Images: 2428 receipts… See the full description on the dataset page: https://huggingface.co/datasets/samarth010/nano-receipts.nando_dt7