datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
VisualWebInstruct
VisualWebInstruct: Scaling up Multimodal Instruction Data through Web Search
VisualWebInstruct is a large-scale, diverse multimodal instruction dataset designed to enhance vision-language models' reasoning capabilities. The dataset contains approximately 900K question-answer (QA) pairs, with 40% consisting of visual QA pairs associated with 163,743 unique images, while the remaining 60% are text-only QA pairs.
Please also checkout our more recent verified version at Huggingface.… See the full description on the dataset page: https://huggingface.co/datasets/TIGER-Lab/VisualWebInstruct.placement_visual_conditionedThis dataset was created using LeRobot.
Dataset Structure
meta/info.json:
{
"codebase_version": "v2.1",
"robot_type": "hessian",
"total_episodes": 197,
"total_frames": 66397,
"total_tasks": 1,
"total_videos": 591,
"total_chunks": 1,
"chunks_size": 1000,
"fps": 60,
"splits": {
"train": "0:197"
},
"data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet",
"video_path":… See the full description on the dataset page: https://huggingface.co/datasets/DistantSky/placement_visual_conditioned.VisualWebInstruct
VisualWebInstruct: Scaling up Multimodal Instruction Data through Web Search
VisualWebInstruct is a large-scale, diverse multimodal instruction dataset designed to enhance vision-language models' reasoning capabilities. The dataset contains approximately 900K question-answer (QA) pairs, with 40% consisting of visual QA pairs associated with 163,743 unique images, while the remaining 60% are text-only QA pairs.
Links
GitHub Repository
Research Paper
Project Website… See the full description on the dataset page: https://huggingface.co/datasets/taoye1992/VisualWebInstruct.grounded-visual-spatial-reasoning
Grounded Visual Spatial Reasoning
Code for generating the annotations can be found here: github.com
Dataset Summary
This dataset extends the Visual Spatial Reasoning (VSR) dataset with visual grounding annotations: each caption is annotated with COCO-category object mentions, their positions , and corresponding bounding boxes in the image.
Data instance
Each sample instance has the following structure:
Field
Type
Description
image_file
string… See the full description on the dataset page: https://huggingface.co/datasets/tomhodemon/grounded-visual-spatial-reasoning.Joint-VisualCoT
Joint VisualCoT
Joint evidence SFT on Visual-CoT document pages. One assistant target:
{"bboxes_2d": [[x1,y1,x2,y2], ...], "selected_sentences": ["..."], "score_img": 0.0, "score_text": 0.0}
Boxes are integer xyxy in [0, 1000]. Images are not in this repo; resolve image under Visual-CoT cot_image_data/{image}
(deepcs233/Visual-CoT).
Code: Chenfei-Liao/MMProvenceChenfei.
Paper protocol
Image-level no-leak: Stage2 test images never enter Stage1 train (splits/image_splits.json).… See the full description on the dataset page: https://huggingface.co/datasets/Chenfei-Liao/Joint-VisualCoT.visualears-bench-results
🗂️ visualears-bench-results
English + فارسی · Part of Shenava 1.0 · Project hub · SLT paper submission
🌟 At a glance | معرفی سریع
English
فارسی
🎯 Purpose
Benchmark result dataset and leaderboard artifacts.
دادهها و مصنوعات نتایج بنچمارک برای نگهداری خروجی مدلها، امتیازها و منشأ اجرای ارزیابی.
🧩 Role
evaluation and benchmarking asset
مصنوع ارزیابی و بنچمارک
📦 Snapshot
4 files; approximately 51.61 KB
4 فایل؛ حدود 51.61 KB
🧱 Packaging
0… See the full description on the dataset page: https://huggingface.co/datasets/Reza2kn/visualears-bench-results.Visual-WetlandBirds-Dataset
Dataset Card for Visual WetlandBirds Dataset
The Visual WetlandBirds Dataset is a fine-grained spatio-temporal dataset specifically designed for bird behavior detection and species classification. This version has been converted to work well with the Hugging Face Hub, with the original dataset available at Zenodo.
The dataset was introduced in the paper Visual WetlandBirds Dataset: Bird Species Identification and Behavior Recognition in Videos.
Dataset Details… See the full description on the dataset page: https://huggingface.co/datasets/academic-datasets/Visual-WetlandBirds-Dataset.clean-visual-webarena-classifiedsvisual-cube-single-play-singletask-v0This dataset was created using LeRobot.
Dataset Structure
meta/info.json:
{
"codebase_version": "v2.1",
"robot_type": "ogbench_manipspace_ur5e",
"total_episodes": 1000,
"total_frames": 1000000,
"total_tasks": 1,
"total_videos": 1000,
"total_chunks": 1,
"chunks_size": 1000,
"fps": 20,
"splits": {
"train": "0:1000"
},
"data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet",
"video_path":… See the full description on the dataset page: https://huggingface.co/datasets/shivakanthsujit/visual-cube-single-play-singletask-v0.pusht_96_int1_visual_v2
pusht_96_int1_visual_v2
96px PushT PPO successful trajectory dataset.
The trajectories are generated by a 1Hz PPO PushT solver with action codec int1, rendered images in history[*].image, image_prev, and image_next at 96x96 and JPEG quality 90.
Each episode is success-only and capped at 30 environment steps.
Visual marker mode: next_action_target. In each move transition, the red cross in the rendered frame marks that transition's commanded move target. The pusher is rendered with… See the full description on the dataset page: https://huggingface.co/datasets/novastar112/pusht_96_int1_visual_v2.pusht_96_int1_visual_nomarker
pusht_96_int1_visual_nomarker
96px PushT PPO successful trajectory dataset.
The trajectories are generated by a 1Hz PPO PushT solver with action codec int1, rendered images in history[*].image, image_prev, and image_next at 96x96 and JPEG quality 90.
Each episode is success-only and capped at 30 environment steps.
Visual marker mode: none. The pusher is rendered with radius 11.0 in 512-space; physics still uses the environment's collision radius.
Prompt mode:… See the full description on the dataset page: https://huggingface.co/datasets/novastar112/pusht_96_int1_visual_nomarker.visual_robust_libero
visual_robust_libero — Experiment 08: static embodiment supervision
Data for the question "can supervising a policy on static embodiment
information (where the gripper is, which arm it is) improve cross-embodiment
task transfer?", built on LIBERO with robosuite 1.4.0 / MuJoCo 3.3.7.
24 embodiments = 6 robots x 4 grippers. Four of them carry action
demonstrations; the other 20 appear only as static or demo-free synthetic data,
which is what makes the transfer question askable.… See the full description on the dataset page: https://huggingface.co/datasets/ChiefJang/visual_robust_libero.f1-visual-analyticsbasket-white-visualizervisual_servoing_dataset_252pusht_96_norm4_visual_nomarker_allstep_thinking_trickiness_cot
PushT norm4 Visual Nomarker All-Step Thinking Trickiness COT
This dataset is derived from successful PushT visual-nomarker trajectories in novastar112/pusht_96_norm4_visual_nomarker.
Each row contains one full successful trajectory from the first move through the final stop action.
Main files:
training/pusht_allstep_thinking_cot.jsonl.gz: 500,000 train rows.
testing/pusht_allstep_thinking_cot.jsonl.gz: 200 test rows.
metadata/final_scan_validation.json: full local scan after repair… See the full description on the dataset page: https://huggingface.co/datasets/novastar112/pusht_96_norm4_visual_nomarker_allstep_thinking_trickiness_cot.africa-synth-disability-visual-impairment-low-vision-all
Visual Impairment & Low Vision Services (SSA) | Africa (Electric Sheep Africa metadata inventory)
Size category: 10K<n<100K - Formats: csv - 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… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/africa-synth-disability-visual-impairment-low-vision-all.ConvAI2-Qwen-original-Qwen3.5-35B-A3B
Visual Memory Results: convai2-qwen-original
This dataset contains the scored output of a visual-memory perplexity experiment.
Experiment metadata
{
"experiment": {
"model_name": "Qwen/Qwen3.5-35B-A3B",
"hf_results_repo": "visual-memory/ConvAI2-Qwen-original-Qwen3.5-35B-A3B",
"results_jsonl": "results/ConvAI2-Qwen-original-Qwen3.5-35B-A3B.jsonl",
"hf_dataset": "visual-memory/ConvAI2-With-Ids_1k-no-redundancy",
"hf_mapping_dataset":… See the full description on the dataset page: https://huggingface.co/datasets/visual-memory/ConvAI2-Qwen-original-Qwen3.5-35B-A3B.mmu-norm-visual
BTSbot alert triplets and Galaxy10 RGB — derived visual images (release v1)
This repository contains two separate visual-image data for image-based tasks. Their values do not include calibrated photometry.
BTSbot (17,394 alert rows for 8,697 matched ZTF objects)
Each row contains a 63×63 science/reference/difference triplet. The upstream processing normalized these images and converted NaNs and padding to zero. This L1 dataset preserves that representation… See the full description on the dataset page: https://huggingface.co/datasets/kshitijd/mmu-norm-visual.ConvAI2-ERNIE-original-Qwen3.5-35B-A3B
Visual Memory Results: convai2-ernie-original
This dataset contains the scored output of a visual-memory perplexity experiment.
Experiment metadata
{
"experiment": {
"model_name": "Qwen/Qwen3.5-35B-A3B",
"hf_results_repo": "visual-memory/ConvAI2-ERNIE-original-Qwen3.5-35B-A3B",
"results_jsonl": "results/ConvAI2-ERNIE-original-Qwen3.5-35B-A3B.jsonl",
"hf_dataset": "visual-memory/ConvAI2-With-Ids_1k-no-redundancy",
"hf_mapping_dataset":… See the full description on the dataset page: https://huggingface.co/datasets/visual-memory/ConvAI2-ERNIE-original-Qwen3.5-35B-A3B.ConvAI2-Qwen-enhanced-Qwen3.5-35B-A3B
Visual Memory Results: convai2-qwen-enhanced
This dataset contains the scored output of a visual-memory perplexity experiment.
Experiment metadata
{
"experiment": {
"model_name": "Qwen/Qwen3.5-35B-A3B",
"hf_results_repo": "visual-memory/ConvAI2-Qwen-enhanced-Qwen3.5-35B-A3B",
"results_jsonl": "results/ConvAI2-Qwen-enhanced-Qwen3.5-35B-A3B.jsonl",
"hf_dataset": "visual-memory/ConvAI2-With-Ids_1k-no-redundancy",
"hf_mapping_dataset":… See the full description on the dataset page: https://huggingface.co/datasets/visual-memory/ConvAI2-Qwen-enhanced-Qwen3.5-35B-A3B.ConvAI2-Qwen-enhanced-gemma-4-26B-A4B-it
Visual Memory Results: convai2-qwen-enhanced
This dataset contains the scored output of a visual-memory perplexity experiment.
Experiment metadata
{
"experiment": {
"model_name": "google/gemma-4-26B-A4B-it",
"hf_results_repo": "visual-memory/ConvAI2-Qwen-enhanced-gemma-4-26B-A4B-it",
"results_jsonl": "results/ConvAI2-Qwen-enhanced-gemma-4-26B-A4B-it.jsonl",
"hf_dataset": "visual-memory/ConvAI2-With-Ids_1k-no-redundancy",
"hf_mapping_dataset":… See the full description on the dataset page: https://huggingface.co/datasets/visual-memory/ConvAI2-Qwen-enhanced-gemma-4-26B-A4B-it.ConvAI2-FLUX-enhanced-Qwen3.5-35B-A3B
Visual Memory Results: convai2-flux-enhanced
This dataset contains the scored output of a visual-memory perplexity experiment.
Experiment metadata
{
"experiment": {
"model_name": "Qwen/Qwen3.5-35B-A3B",
"hf_results_repo": "visual-memory/ConvAI2-FLUX-enhanced-Qwen3.5-35B-A3B",
"results_jsonl": "results/ConvAI2-FLUX-enhanced-Qwen3.5-35B-A3B.jsonl",
"hf_dataset": "visual-memory/ConvAI2-With-Ids_1k-no-redundancy",
"hf_mapping_dataset":… See the full description on the dataset page: https://huggingface.co/datasets/visual-memory/ConvAI2-FLUX-enhanced-Qwen3.5-35B-A3B.visual_servoing_dataset_pool_252visual_genome_revisedConvAI2-FLUX-original-Qwen3.5-35B-A3B
Visual Memory Results: convai2-flux-original
This dataset contains the scored output of a visual-memory perplexity experiment.
Experiment metadata
{
"experiment": {
"model_name": "Qwen/Qwen3.5-35B-A3B",
"hf_results_repo": "visual-memory/ConvAI2-FLUX-original-Qwen3.5-35B-A3B",
"results_jsonl": "results/ConvAI2-FLUX-original-Qwen3.5-35B-A3B.jsonl",
"hf_dataset": "visual-memory/ConvAI2-With-Ids_1k-no-redundancy",
"hf_mapping_dataset":… See the full description on the dataset page: https://huggingface.co/datasets/visual-memory/ConvAI2-FLUX-original-Qwen3.5-35B-A3B.ConvAI2-Qwen-original-gemma-4-26B-A4B-it
Visual Memory Results: convai2-qwen-original
This dataset contains the scored output of a visual-memory perplexity experiment.
Experiment metadata
{
"experiment": {
"model_name": "google/gemma-4-26B-A4B-it",
"hf_results_repo": "visual-memory/ConvAI2-Qwen-original-gemma-4-26B-A4B-it",
"results_jsonl": "results/ConvAI2-Qwen-original-gemma-4-26B-A4B-it.jsonl",
"hf_dataset": "visual-memory/ConvAI2-With-Ids_1k-no-redundancy",
"hf_mapping_dataset":… See the full description on the dataset page: https://huggingface.co/datasets/visual-memory/ConvAI2-Qwen-original-gemma-4-26B-A4B-it.ConvAI2-FLUX-enhanced-gemma-4-26B-A4B-it
Visual Memory Results: convai2-flux-enhanced
This dataset contains the scored output of a visual-memory perplexity experiment.
Experiment metadata
{
"experiment": {
"model_name": "google/gemma-4-26B-A4B-it",
"hf_results_repo": "visual-memory/ConvAI2-FLUX-enhanced-gemma-4-26B-A4B-it",
"results_jsonl": "results/ConvAI2-FLUX-enhanced-gemma-4-26B-A4B-it.jsonl",
"hf_dataset": "visual-memory/ConvAI2-With-Ids_1k-no-redundancy",
"hf_mapping_dataset":… See the full description on the dataset page: https://huggingface.co/datasets/visual-memory/ConvAI2-FLUX-enhanced-gemma-4-26B-A4B-it.ConvAI2-ERNIE-enhanced-Qwen3.5-35B-A3B
Visual Memory Results: convai2-ernie-enhanced
This dataset contains the scored output of a visual-memory perplexity experiment.
Experiment metadata
{
"experiment": {
"model_name": "Qwen/Qwen3.5-35B-A3B",
"hf_results_repo": "visual-memory/ConvAI2-ERNIE-enhanced-Qwen3.5-35B-A3B",
"results_jsonl": "results/ConvAI2-ERNIE-enhanced-Qwen3.5-35B-A3B.jsonl",
"hf_dataset": "visual-memory/ConvAI2-With-Ids_1k-no-redundancy",
"hf_mapping_dataset":… See the full description on the dataset page: https://huggingface.co/datasets/visual-memory/ConvAI2-ERNIE-enhanced-Qwen3.5-35B-A3B.yt-personalities
Dataset Information
name: Youtubers by Big Five Personality Traits
license: gpl-3.0
Description
description: |
In trait theory, the Big Five personality traits (sometimes known as the five-factor model of personality or OCEAN or CANOE models) are a group of five characteristics used to study personality:
Openness to Experience (inventive/curious vs. consistent/cautious)
Conscientiousness (efficient/organized vs. extravagant/careless)
Extraversion (outgoing/energetic… See the full description on the dataset page: https://huggingface.co/datasets/visualcomments/yt-personalities.