datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
nanopath-fairness-tiles
nanopath-fairness-tiles
Pre-tiled histopathology patches from CPTAC whole-slide images, used as the
external out-of-distribution validation set for a study on pretraining-time
vs. post-hoc fairness in histopathology foundation models.
Contents
Per-cohort folders, each slides_full/<slide_id>.parquet (one row per tile:
case_id, slide_id, tile_idx, image) + labels.tsv:
cohort
organ / task
slides
cptac_lung
NSCLC — LUAD vs LSCC subtype
604
cptac_gbm
GBM —… See the full description on the dataset page: https://huggingface.co/datasets/ryankim17920/nanopath-fairness-tiles.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.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.