datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
Minecraft-Skins-20M
Dataset Card for Minecraft Skins
Dataset Summary
This dataset contains 19,973,928 unique Minecraft player skins collected from various sources. Each skin is stored as a base64-encoded image with a unique identifier.
Dataset Structure
Data Fields
This dataset includes the following fields:
id: A randomly generated UUID for each skin entry. These UUIDs are not linked to any external APIs or services (such as Mojang's player UUIDs) and serve solely as… See the full description on the dataset page: https://huggingface.co/datasets/nyuuzyou/Minecraft-Skins-20M.mini-reachy-animation
Reachy Mini Animation Dataset
Multi-view renders of 85 emotional animations performed by the Reachy Mini
robot, paired with the full robot joint state for every single frame.
Source of the animations. The emotional animations rendered here come from the
official pollen-robotics/reachy-mini-emotions-library
dataset by Pollen Robotics. This dataset re-renders those emotions from 12 camera
angles (with 3 background variants) and pairs every frame with the robot's joint state.… See the full description on the dataset page: https://huggingface.co/datasets/BastienATOS/mini-reachy-animation.Minecraft-Skins-Captioned-1M
Dataset Card for Minecraft Skins
Dataset Summary
This dataset contains 981,079 unique Minecraft player skins collected from various sources. Each skin is stored as a base64-encoded image with a unique identifier.
Dataset Structure
Data Fields
This dataset includes the following fields:
hash: A data dependent hash. These hashes are generated from raw bytes and will be same if the skin is identical.
image: The skin image encoded in base64 format.… See the full description on the dataset page: https://huggingface.co/datasets/neurlang/Minecraft-Skins-Captioned-1M.gelsight-mini-pretrain
GelSight Mini Pretrain
~853K GelSight Mini tactile RGB frames, 12 public sources, one parquet schema. Built for self-supervised representation learning (VAE / MAE / SimCLR / DINO) — every frame contact-filtered, channel-normalized, and re-encoded as JPEG q92.
Frames
Sources
Real
536K
FoTA (labeled+unlabeled), 3DCal, FEATS, GelSLAM, TactileTracking, RTM, FeelAnyForce, UniT, TacQuad
Sim
317K
sim_tactile_mnist, sim_starstruck (Taxim-rendered, Mini-calibrated)
NC… See the full description on the dataset page: https://huggingface.co/datasets/yxma/gelsight-mini-pretrain.UrbanPersona-120K-Interpretive
UrbanPersona-120K-Interpretive
Annotation corpora and analysis outputs for "Persona Prompting in Multimodal Urban Perception: Descriptive Convergence and Interpretive Variation" (EMNLP 2026 Workshop Pandora). Two
multimodal LLMs, Qwen3-VL-8B and Gemma4 E4B, annotate the same 50 PerceptSent urban scenes as the
same 1,200 demographic personas at T = 0.1, 60,000 persona × image attempts per model and 120,000
in all, next to their no-persona ablations, a greedy T = 0 decoding… See the full description on the dataset page: https://huggingface.co/datasets/MInDS-lab-UTFPR/UrbanPersona-120K-Interpretive.CraftSight-Minecraft
CraftSight
Frame-level multi-label visual annotations for Minecraft agents.
Dataset creation toolkit: CraftSight is built and maintained with CraftSight Labeler, an open-source, browser-based annotation tool and reproducible release pipeline for multi-label Minecraft vision datasets. It supports manual and model-assisted labeling, structured game-state annotations, and trajectory-safe train/validation/test splits.
CraftSight provides Minecraft gameplay frames annotated… See the full description on the dataset page: https://huggingface.co/datasets/Krows7/CraftSight-Minecraft.UrbanPersona-60K
UrbanPersona-60K: persona-conditioned urban sentiment annotations
Every annotation produced for "Stable Behavior, Limited Variation: Persona Validity in LLM
Agents for Urban Sentiment Perception" (arXiv:2604.28048): 60,000 attempts in
which 1,200 demographically distinct LLM personas each judged the same 50 urban scenes, plus the
two no-persona ablations the paper measures them against, the seed profiles that produced the
personas, and the full analysis outputs.
Project page:… See the full description on the dataset page: https://huggingface.co/datasets/MInDS-lab-UTFPR/UrbanPersona-60K.forestllava-dataset
Forest-LLaVA Multimodal Tree-Species Dataset
Forest-LLaVA is a multimodal remote-sensing dataset for tree-species
recognition and structured vision-language research. Each record is indexed by
a numeric sample_id from the US subset of GlobalGeoTree and is linked to a
60 m × 60 m patch from NAIP Optical, Sentinel-2 MSI and Sentinel-1 SAR data,
four-level taxonomic labels and geographic/environmental records.
The repository contains the complete image archives for the… See the full description on the dataset page: https://huggingface.co/datasets/minute1028/forestllava-dataset.gelsight-mini-pretrain-nc
GelSight Mini Pretrain · Non-Commercial Extension
⚠️ Non-commercial use only. This repository is licensed
CC-BY-NC-4.0 because it includes upstream sources whose licenses
restrict commercial use. For commercial-friendly Mini tactile data,
see the main yxma/gelsight-mini-pretrain repo
(CC-BY-4.0).
This dataset is the CC-BY-NC extension of yxma/gelsight-mini-pretrain.
It contains only the GelSight Mini sources whose upstream licenses are
not compatible with CC-BY-4.0 aggregation.… See the full description on the dataset page: https://huggingface.co/datasets/yxma/gelsight-mini-pretrain-nc.rvl_cdip_mini
Dataset Card for RVL-CDIP-MINI
This dataset is a subset (1%) of the original aharley/rvl_cdip merged with the corresponding annotations from jordyvl/rvl_cdip_easyocr.
You can easily and quickly load it:
dataset = load_dataset("dvgodoy/rvl_cdip_mini")
DatasetDict({
train: Dataset({
features: ['image', 'width', 'height', 'category', 'ocr_words', 'word_boxes', 'ocr_paragraphs', 'paragraph_boxes', 'label'],
num_rows: 3200
})
validation: Dataset({… See the full description on the dataset page: https://huggingface.co/datasets/dvgodoy/rvl_cdip_mini.minecraft-skins-1.1m-deduped-64x64-2.0
Minecraft Skins 1.1M Deduped (64x64 Edition) 2.0!
Minecraft Skins 1.1M Deduped 1.5 but it's tagged.
Format is just a 76.5 MB JSONL file and a 6.1 MB zipped JSONL file (as a JSONZ file)
Tools used
PIL Image (Python) and Google Colab (T4 GPU tier, but it didn't use the GPU at all!)
How it was made
Loaded Minecraft Skins 1.1M Deduped 1.5,
Tagged using a simple system where it looks for colors and complexity,
Output is given in a 6.1 MB ZIP archive or a 76.5 MB… See the full description on the dataset page: https://huggingface.co/datasets/MihaiPopa-1/minecraft-skins-1.1m-deduped-64x64-2.0.TFQ-Data-Full
TFQ-Data: A Fine-Grained Dataset for Image Implication
TFQ-Data is a large-scale visual instruction tuning dataset specifically designed to train Multi-modal Large Language Models (MLLMs) on Image Implication and Metaphorical Reasoning.
Unlike standard VQA datasets that focus on literal description, TFQ-Data utilizes a True-False Question (TFQ) format. This format provides high knowledge density and verifiable reward signals, making it an ideal substrate for Visual Reinforcement… See the full description on the dataset page: https://huggingface.co/datasets/MING-ZCH/TFQ-Data-Full.gelsight-mini-pretrain-video
GelSight Mini Pretrain · Video / Sequence Subset
🎬 Companion to yxma/gelsight-mini-pretrain.
Where the main repo treats every kept frame as an independent image, this repo
preserves temporal sequences — one row per frame, ordered, with explicit
sequence-id + position metadata, for video tactile pretraining.
Why this repo
The main repo's pipeline applies perceptual-hash dedupe within each capture
to drop near-identical adjacent frames. That's great for image-level… See the full description on the dataset page: https://huggingface.co/datasets/yxma/gelsight-mini-pretrain-video.TFQ-Bench-Full
TFQ-Bench: A Benchmark for Evaluating Image Implication Understanding
TFQ-Bench is a rigorous evaluation benchmark designed to assess the capabilities of MLLMs in understanding visual metaphors, sarcasm, and implicit meanings via True-False Questions.
It serves as a complement to existing benchmarks like II-Bench (Multiple-Choice Question) and CII-Bench (Open-Style Question), offering a lower-bound difficulty check that tests a model's ability to verify specific propositions about… See the full description on the dataset page: https://huggingface.co/datasets/MING-ZCH/TFQ-Bench-Full.ring-drop-mini
Ring Drop Mini
A small binary image set for classifying blue ring drop vs green ring drop frames. Images live in media_blob; class ids live in bucket.
TFQ-Data-Lite
TFQ-Data: A Fine-Grained Dataset for Image Implication
TFQ-Data is a large-scale visual instruction tuning dataset specifically designed to train Multi-modal Large Language Models (MLLMs) on Image Implication and Metaphorical Reasoning.
Unlike standard VQA datasets that focus on literal description, TFQ-Data utilizes a True-False Question (TFQ) format. This format provides high knowledge density and verifiable reward signals, making it an ideal substrate for Visual Reinforcement… See the full description on the dataset page: https://huggingface.co/datasets/MING-ZCH/TFQ-Data-Lite.TFQ-Bench-Lite
TFQ-Bench: A Benchmark for Evaluating Image Implication Understanding
TFQ-Bench is a rigorous evaluation benchmark designed to assess the capabilities of MLLMs in understanding visual metaphors, sarcasm, and implicit meanings via True-False Questions.
It serves as a complement to existing benchmarks like II-Bench (Multiple-Choice Question) and CII-Bench (Open-Style Question), offering a lower-bound difficulty check that tests a model's ability to verify specific propositions about… See the full description on the dataset page: https://huggingface.co/datasets/MING-ZCH/TFQ-Bench-Lite.minicar-dataset
🏎️ MiniCar Autonomous Driving Dataset
自動運転ミニカー用のトレーニングデータセット
概要
このデータセットには以下が含まれます：
カメラ画像
センサーデータ（IMU等）
アノテーション（ステアリング角度、スロットル）
データ構造
minicar-dataset/
├── train/
│ ├── images/ # カメラ画像 (JPG/PNG)
│ ├── sensors/ # センサーデータ (CSV)
│ └── annotations.csv # ラベルデータ
├── test/
│ └── ...
└── README.md
使い方
from datasets import load_dataset
dataset = load_dataset("Romihi50/minicar-dataset")
# トレーニングデータ
for sample in… See the full description on the dataset page: https://huggingface.co/datasets/Romihi50/minicar-dataset.