datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
PatchCamelyon
PatchCamelyon (PCam)
Description
The PatchCamelyon benchmark is a new and challenging image classification dataset. It consists of 327.680 color images (96 x 96px) extracted from histopathologic scans of lymph node sections. Each image is annoted with a binary label indicating presence of metastatic tissue. PCam provides a new benchmark for machine learning models: bigger than CIFAR10, smaller than imagenet, trainable on a single GPU
Why PCam
Fundamental… See the full description on the dataset page: https://huggingface.co/datasets/1aurent/PatchCamelyon.PatchCamelyon
PatchCamelyon (PCam)
This is a reupload of the PatchCamelyon (PCam) dataset to make it more readily usable instead of manipulating H5 files. The original can be found in the author's Github repo.
If you use this dataset, please cite the original publications:
@inproceedings{veeling2018rotation,
title={Rotation Equivariant CNNs for Digital Pathology},
author={Veeling, Bastiaan S and Linmans, Jasper and Winkens, Jim and Cohen, Taco and Welling, Max},
booktitle={Medical Image… See the full description on the dataset page: https://huggingface.co/datasets/zacharielegault/PatchCamelyon.PatchCamelyon
PatchCamelyon (PCam)
Description
The PatchCamelyon benchmark is a new and challenging image classification dataset. It consists of 327.680 color images (96 x 96px) extracted from histopathologic scans of lymph node sections. Each image is annoted with a binary label indicating presence of metastatic tissue. PCam provides a new benchmark for machine learning models: bigger than CIFAR10, smaller than imagenet, trainable on a single GPU
Why PCam
Fundamental… See the full description on the dataset page: https://huggingface.co/datasets/pavan316/PatchCamelyon.PatchCamelyon
PatchCamelyon (PCam)
Description
The PatchCamelyon benchmark is a new and challenging image classification dataset. It consists of 327.680 color images (96 x 96px) extracted from histopathologic scans of lymph node sections. Each image is annoted with a binary label indicating presence of metastatic tissue. PCam provides a new benchmark for machine learning models: bigger than CIFAR10, smaller than imagenet, trainable on a single GPU
Why PCam
Fundamental… See the full description on the dataset page: https://huggingface.co/datasets/KE9037/PatchCamelyon.vessel-detection-labeled-patches
Vessel Detection Labeled Patches
Validated/confirmed satellite image patches exported from the military-boat-detection review workflow.
Contents
images/: patch images.
metadata.csv: one row per patch, compatible with Hugging Face image-folder metadata.
metadata.jsonl: rich patch metadata with nested objects.
annotations.csv: one row per vessel annotation.
annotations.jsonl: JSONL version of the object annotations.
labels/: YOLO-format labels. Hard negatives have empty… See the full description on the dataset page: https://huggingface.co/datasets/DefendIntelligence/vessel-detection-labeled-patches.sentinel-lfm-mining-patches
sentinel-lfm — illegal-mining single-frame patches
128px RGB patches cropped from the Roboflow illegal-mining dataset, labelled
mine (1) / no-mine (0). Split by source image (no leakage) into
train/val/test. Provided as PNGs + vlm_sft-format JSONL (one image + prompt
-> JSON answer) so it drops straight into VLM fine-tuning.
split
pos
neg
total
train
1410
555
1965
val
303
66
369
test
303
116
419
RGB only (no multispectral). Each JSONL row is a single-turn VLM… See the full description on the dataset page: https://huggingface.co/datasets/ASTRALK/sentinel-lfm-mining-patches.ALL-IDB-Patches
ALL-IDB Patches
MATLAB source code for creating image patches and labels used in the paper “ALL-IDB Patches: Whole slide imaging for Acute Lymphoblastic Leukemia detection using Deep Learning”, presented at ICASSP Workshops 2023.
The repository converts annotated ALL-IDB1 whole-slide microscope images into fixed-size overlapping patches, preserving the position of white blood cell centroids and generating patch-level labels for probable lymphoblasts… See the full description on the dataset page: https://huggingface.co/datasets/AngeloUNIMI/ALL-IDB-Patches.deep_pavements_surface_patchesA data-only mirror of https://github.com/kauevestena/deep_pavements_dataset
coffee_rocole_original_patches
Dataset Card for coffee_rocole_original_patches
This is a FiftyOne dataset with 700 samples.
Installation
If you haven't already, install FiftyOne:
pip install -U fiftyone
Usage
import fiftyone as fo
from fiftyone.utils.huggingface import load_from_hub
# Load the dataset
# Note: other available arguments include 'max_samples', etc
dataset = load_from_hub("pjramg/coffee_rocole_original_patches")
# Launch the App
session = fo.launch_app(dataset)… See the full description on the dataset page: https://huggingface.co/datasets/pjramg/coffee_rocole_original_patches.Deepfakes-QA-Patch1
Deepfake Quality Assessment
Deepfake QA is a Deepfake Quality Assessment model designed to analyze the quality of deepfake images & videos. It evaluates whether a deepfake is of good or bad quality, where:
0 represents a bad-quality deepfake
1 represents a good-quality deepfake
This classification serves as the foundation for training models on deepfake quality assessment, helping improve deepfake detection and enhancement techniques.
Citation
If you use our… See the full description on the dataset page: https://huggingface.co/datasets/prithivMLmods/Deepfakes-QA-Patch1.Deepfakes-QA-Patch2
Deepfake Quality Assessment
Deepfake QA is a Deepfake Quality Assessment model designed to analyze the quality of deepfake images & videos. It evaluates whether a deepfake is of good or bad quality, where:
0 represents a bad-quality deepfake
1 represents a good-quality deepfake
This classification serves as the foundation for training models on deepfake quality assessment, helping improve deepfake detection and enhancement techniques.
Citation… See the full description on the dataset page: https://huggingface.co/datasets/prithivMLmods/Deepfakes-QA-Patch2.Deepfakes-QA-Patch1
Deepfake Quality Assessment
Deepfake QA is a Deepfake Quality Assessment model designed to analyze the quality of deepfake images & videos. It evaluates whether a deepfake is of good or bad quality, where:
0 represents a bad-quality deepfake
1 represents a good-quality deepfake
This classification serves as the foundation for training models on deepfake quality assessment, helping improve deepfake detection and enhancement techniques.
Citation… See the full description on the dataset page: https://huggingface.co/datasets/strangerguardhf/Deepfakes-QA-Patch1.Deepfakes-QA-Patch2
Deepfake Quality Assessment
Deepfake QA is a Deepfake Quality Assessment model designed to analyze the quality of deepfake images & videos. It evaluates whether a deepfake is of good or bad quality, where:
0 represents a bad-quality deepfake
1 represents a good-quality deepfake
This classification serves as the foundation for training models on deepfake quality assessment, helping improve deepfake detection and enhancement techniques.
Citation… See the full description on the dataset page: https://huggingface.co/datasets/strangerguardhf/Deepfakes-QA-Patch2.