datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
power-plant-olmoearth-segmentation
OlmoEarth v1.2-ready power energy dataset
Derived cook 20260830T191500Z from pinned source 35da3d0549b16e15114ded1eb7df3f4a378e9b4f.
The branch uses no train/validation/test split. Every accepted row is tagged all.
Accepted imagery rows: 12453
Quality-unavailable rows: 1
Release mode: partial with documented quality-unavailable exclusions
Clearest-available quality fallbacks: 10
Sentinel-2 L2A: uint16 [12,1,128,128]
Band order: B02,B03,B04,B08,B05,B06,B07,B8A,B11,B12,B01,B09… See the full description on the dataset page: https://huggingface.co/datasets/sarkarghya/power-plant-olmoearth-segmentation.droid_dataset_segmentation_mask
DROID SAM 3.1 Segmentation Masks
This dataset is a mask-only sidecar generated from the original
droid_101/0.0.1 RLDS release. It does not redistribute DROID images or
actions. Its episode_index follows the RLDS episode order.
The same episodes appear in lerobot/droid_1.0.1, but LeRobot stores them in a
different episode order. Therefore, mask episode_index and LeRobot
episode_index must not be joined directly. Use the mapping file described
below to associate these masks with… See the full description on the dataset page: https://huggingface.co/datasets/EpicPinkPenguin/droid_dataset_segmentation_mask.table_spill_cleanup_bimanual_rgbd_segmentation_poses
Exylos Bimanual Table Spill Cleanup Rich-Modality Sample
A compact, rich-modality bimanual robot manipulation dataset for tabletop spill cleanup.
Each episode combines synchronized dual-arm Panda state/action trajectories, 7 RGB camera streams, per-frame depth maps, per-frame segmentation masks, object pose streams, phase annotations, and an objective cleanup success metric based on the remaining spill fraction.
This dataset is a rich-modality inspection sample for the Exylos… See the full description on the dataset page: https://huggingface.co/datasets/ExylosAi/table_spill_cleanup_bimanual_rgbd_segmentation_poses.im3-datacenter-olmoearth-segmentation
OlmoEarth v1.2-ready datacenter energy dataset
Derived cook 20260830T191500Z from pinned source 1f3ee5f334dd53940d088a89db57aed62cc925d5.
The branch uses no train/validation/test split. Every accepted row is tagged all.
Accepted imagery rows: 1417
Quality-unavailable rows: 0
Release mode: partial with documented quality-unavailable exclusions
Clearest-available quality fallbacks: 1
Sentinel-2 L2A: uint16 [12,1,128,128]
Band order: B02,B03,B04,B08,B05,B06,B07,B8A,B11,B12,B01,B09… See the full description on the dataset page: https://huggingface.co/datasets/sarkarghya/im3-datacenter-olmoearth-segmentation.recitation-segmentation
Recitation Segmentation Dataset for Holy Quran Pronunciation Error Detection
This dataset is used for building models that segment Holy Quran recitations based on pause points (waqf) with high accuracy. The segments are crucial for tasks like Automatic Pronunciation Error Detection and Correction, leveraging the rigorous recitation rules (tajweed) of the Holy Quran.
The dataset was presented in the paper Automatic Pronunciation Error Detection and Correction of the Holy Quran's… See the full description on the dataset page: https://huggingface.co/datasets/nour-world/recitation-segmentation.wind-and-solar-candidate-olmoearth-segmentation
OlmoEarth v1.2-ready renewable energy dataset
Derived cook 20260830T191500Z from pinned source 2c551f58998cd25554ea679148b21a9c701b51db.
The branch uses no train/validation/test split. Every accepted row is tagged all.
Accepted imagery rows: 12649
Quality-unavailable rows: 4
Release mode: partial with documented quality-unavailable exclusions
Clearest-available quality fallbacks: 4
Sentinel-2 L2A: uint16 [12,1,128,128]
Band order: B02,B03,B04,B08,B05,B06,B07,B8A,B11,B12,B01,B09… See the full description on the dataset page: https://huggingface.co/datasets/sarkarghya/wind-and-solar-candidate-olmoearth-segmentation.recitation-segmentation
Recitation Segmentation Dataset for Holy Quran Pronunciation Error Detection
This dataset is used for building models that segment Holy Quran recitations based on pause points (waqf) with high accuracy. The segments are crucial for tasks like Automatic Pronunciation Error Detection and Correction, leveraging the rigorous recitation rules (tajweed) of the Holy Quran.
The dataset was presented in the paper Automatic Pronunciation Error Detection and Correction of the Holy Quran's… See the full description on the dataset page: https://huggingface.co/datasets/obadx/recitation-segmentation.africa-synth-retail-and-ecommerce-customer-segmentation-data-nigeria
Customer Segmentation Data | Africa (Electric Sheep Africa metadata inventory)
Size category: 100K<n<1M - Formats: parquet - Sector: culture_language - Engineered by Electric Sheep Africa
TL;DR
This dataset is part of the Electric Sheep Africa catalog on Hugging Face. It is indexed for African data discovery with standardized metadata, loading guidance, provenance notes, and analyst-oriented context.
What This Dataset Covers
Public datasets help… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/africa-synth-retail-and-ecommerce-customer-segmentation-data-nigeria.image-segmentation-checkpoint-downloadsImage-to-Segmentation
Segmentation Data Subset
image_id: Refer to image_id on image_data subset
segmentation_id: Segmentation identifier
segmentation_information: COCO Format annotations: [[x1, y1, x2, y2, x3, y3, x4, y4]]
cmu_panoptic_dataset_instance_segmentation_mask
CMU Panoptic person instance masks (SAM 3.1 pseudo-labels)
TODO before making this repo public: confirm permission from the CMU Panoptic
Studio organisers. Until then the repo must stay gated and private. See
LICENSE_NOTICE.md.
Per-person instance segmentation masks for the CMU Panoptic Studio dataset, generated
with SAM 3.1 (facebook/sam3.1 @ daa63191845a41281374e725f4c9e51c7a824460) and aligned to the
dataset's own 3D skeletons: masks and pose share the same person id across… See the full description on the dataset page: https://huggingface.co/datasets/EpicPinkPenguin/cmu_panoptic_dataset_instance_segmentation_mask.Segmentation-drivableNALA-segmentation-IDcar-segmentation-assignmentThe paquet file is organized as a table. The columns read from left to right: Row number, frame index, timestamp, car component, confidenct score, and the two x-axis
and two y-axis where one of the corner of the boxes might reside. The paquet file used a Yolo8 object detector to find car components in various frames
in the given car video.
Segmentation_de_la_feuille_de_papier_qui_sert_de_zone_20260924_110415This dataset was created using LeRobot.
Dataset Structure
meta/info.json:
{
"codebase_version": "v3.0",
"fps": 30,
"features": {
"action": {
"dtype": "float32",
"names": [
"shoulder_pan.pos",
"shoulder_lift.pos",
"elbow_flex.pos",
"wrist_flex.pos",
"wrist_roll.pos",
"gripper.pos"
],
"shape": [
6… See the full description on the dataset page: https://huggingface.co/datasets/Tridex/Segmentation_de_la_feuille_de_papier_qui_sert_de_zone_20260924_110415.