datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
olmoearth-paper-embeddings
OlmoEarth — Foundation-Model Embeddings for Paper Table 2
This dataset contains pre-extracted embeddings from 26 Earth-observation
foundation models evaluated on the 24 downstream tasks that make up
Table 2 of the OlmoEarth paper:
OlmoEarth: Stable Latent Image Modeling for Multimodal Earth Observation
AI2, 2025. arXiv:2511.13655.
For every supported (model, task) pair we ran the model's encoder over the
task's train / validation / test splits with the paper-best… See the full description on the dataset page: https://huggingface.co/datasets/allenai/olmoearth-paper-embeddings.Danbooru-WD-EVA-EmbeddingsThis dataset includes WD EVA v2 large embeddings for danbooru images. Pixiv will be added later. Tensors under w are direct outputs and match indexes for WD EVA model. Tensors under e are from WD EVA as well however these strips the classifiaction head, they are smaller and suitable for deduplication computing for example.
The indexes of w, e and f (filename) match.
WD EVA Model: https://huggingface.co/SmilingWolf/wd-eva02-large-tagger-v3
relaion2b-natural-embeddings
LAION-Natural Embeddings: CLIP ViT-H/14 Features for ~500M Natural Photographs (CCN 2025, Roth & Hebart)
LAION-Natural Embeddings provides pre-computed CLIP ViT-H/14 embeddings for ~500 million natural photographs from ReLAION-2B, filtered using the LAION-Natural naturalness classifier (score > 0.7).
Also known as: LAION-Natural Embeddings · ReLAION-Natural Embeddings · LAION-2B-Natural Embeddings
Part of the LAION-Natural dataset family, introduced in: How to sample the… See the full description on the dataset page: https://huggingface.co/datasets/andropar/relaion2b-natural-embeddings.road-images-and-embeddings
Norwegian Road Images with Embeddings (Trondheim Area)
A dataset of 34,908 road images from the Trondheim region of Norway (~40km radius), captured by Statens vegvesen (Norwegian Public Roads Administration) in 2025. Each image is paired with rich geospatial metadata, nearest address information, and a 3072-dimensional image embedding from Google's gemini-embedding-2-preview model.
Dataset Structure
Each example contains:
Field
Type
Description
image
Image… See the full description on the dataset page: https://huggingface.co/datasets/thomasht86/road-images-and-embeddings.DinoBloom_hemato_embeddings
DinoBloom Hemato Patient Embeddings
Per-patient image embeddings of peripheral blood smears, extracted with the DinoBloom-B foundation model (code, paper).
Each .h5 file contains the stacked DinoBloom-B embeddings for all single-cell crops of one patient.
Contents
patient_embeddings/
├── caitomorph/ # 409 patients — caitomorph cohort (Dasdelen et al., 2026)
├── aml_hehr/ # 189 patients — AML genetic-subtype cohort (Hehr et al., 2023)
└── apl_aml/ # 106… See the full description on the dataset page: https://huggingface.co/datasets/MarrLab/DinoBloom_hemato_embeddings.local-embeddings-2022
Local Embeddings Dataset
Multi-temporal satellite imagery dataset for phenology embedding training.
Dataset Description
This dataset contains multi-spectral satellite tiles across 6 months (April-September 2022) with 16 bands per tile.
Dataset Structure
local_embeddings/
├── alphaearth_embeddings_tiles_202204/ (263 tiles)
├── alphaearth_embeddings_tiles_202205/ (263 tiles)
├── alphaearth_embeddings_tiles_202206/ (263 tiles)
├──… See the full description on the dataset page: https://huggingface.co/datasets/gabrielireland/local-embeddings-2022.spheer-fm-embeddings
Spheer FM Embeddings
Annual, 10 m, per-pixel embeddings pre-computed with Spheer FM Albatross, a self-supervised geospatial foundation model trained on Sentinel-2 time series.
Spheer FM is deliberately specialised: it is trained on European Sentinel-2 time series, with a focus on nature and biodiversity. Unlike foundation models built primarily around spatial image structure, Spheer FM places temporal land-surface dynamics at the centre of its representation. Its temporal… See the full description on the dataset page: https://huggingface.co/datasets/spheer/spheer-fm-embeddings.merged_remote_landscapes_v1
Dataset Card for Merged Remote Landscapes dataset
Dataset summary
This is a merged version of following datasets:
torchgeo/ucmerced
NWPU-RESISC45
from datasets import load_dataset
dataset = load_dataset('EmbeddingStudio/merged_remote_landscapes_v1')
Categories
This is a union of categories from original datasets:
agricultural, airplane, airport, baseball diamond, basketball court, beach, bridge, buildings, chaparral, church, circular farmland, cloud… See the full description on the dataset page: https://huggingface.co/datasets/EmbeddingStudio/merged_remote_landscapes_v1.imagenet-1k-224-clip-embeddings
ImageNet-1k-224 CLIP Embeddings
Pre-computed CLIP image embeddings for every image in
mlnomad/imagenet-1k-224.
Columns
Column
Type
Description
original_index
int
Row index in the source dataset for cross-referencing
label
int (0–999)
ImageNet class index
embedding
List[float]
L2-normalised CLIP image embedding (768D)
Stats
Source: mlnomad/imagenet-1k-224 (train split)
Total images: 1281167
Embedding dim: 768
CLIP model:… See the full description on the dataset page: https://huggingface.co/datasets/mlnomad/imagenet-1k-224-clip-embeddings.vlm-compositionality-embeddings
VLM Compositionality Embeddings
Pre-computed image and text embeddings for the thesis "From Euclidean to Hyperbolic Vision-Language Spaces: A Study of Attribute–Object Compositionality" by Meelad Dashti (Politecnico di Torino & University of Twente, 2026).
Code repository: github.com/MelDashti/hyperbolic-vlm-compositionality
Models
Model
Geometry
Architecture
Training Data
CLIP ViT-L/14
Spherical
ViT-L/14
WIT (400M+ pairs)
DINOv2 ViT-L/14
Spherical
ViT-L/14… See the full description on the dataset page: https://huggingface.co/datasets/Meldashti/vlm-compositionality-embeddings.testing_qwen3vl_embeddings
Dataset Card for random_short_videos
This is a FiftyOne dataset with 412 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("harpreetsahota/testing_qwen3vl_embeddings")
# Launch the App
session = fo.launch_app(dataset)… See the full description on the dataset page: https://huggingface.co/datasets/harpreetsahota/testing_qwen3vl_embeddings.flux-classification-embeddingsThe dataset contains over 200 embeddings and labels for FLUX classification. The dataset should be used in conjunction with the embedding model.
embeddings.npy - Contains the embeddings
labels.npy - contains the labels.
danbooru-convnext-embeddings2
Dataset Card for Danbooru ConvNeXt Embeddings 2
Danbooru ConvNeXt 向量数据集 2
Dataset Details / 数据集详情
Dataset Description / 数据集描述
English:
This dataset contains approximately 5,312,000 image embeddings (vectors). It was generated by extracting features from the massive Danbooru anime image dataset using the convnext_large.dinov3_lvd1689m computer vision model. These embeddings represent the visual features of the images in a high-dimensional space… See the full description on the dataset page: https://huggingface.co/datasets/telecomadm1145/danbooru-convnext-embeddings2.fashion_embeddings