datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
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.