datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
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3DPE-Crack22
3DPE-Crack22: Crack Detection in Earthen 3D-Printing
Overview
The crack-detection-earthen-3dp dataset has been curated by theInstitute of Construction Management, Digital Engineering & Robotics in Construction (ICoM)at RWTH Aachen University.
It focuses on automated crack detection in earthen 3D-printed structures, aiming to support research in:
Defect identification in additive manufacturing for construction.
Quality control and monitoring of earthen 3D-printing… See the full description on the dataset page: https://huggingface.co/datasets/ICoM-RWTH/3DPE-Crack22.rwbyhyousetsuteikoku
Bangumi Image Base of Rwby - Hyousetsu Teikoku
This is the image base of bangumi RWBY - Hyousetsu Teikoku, we detected 29 characters, 2529 images in total. The full dataset is here.
Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual. If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).… See the full description on the dataset page: https://huggingface.co/datasets/BangumiBase/rwbyhyousetsuteikoku.RWTD-COCO
RWTD-COCO
Single natural appearance transitions built from COCO-Stuff by deterministic reuse of human annotation.
One of the four evaluation routes in the ICLR 2027 submission on sub-semantic
image segmentation: partitioning an image into regions that are coherent in
appearance and describable in language, but that need not correspond to any
object, part or material class.
Images: 256
Code: github.com/aviadcohz/Qwen2SAM_Detecture_Benchmark
Weights: aviadcohz/Detecture-ICLR-2027… See the full description on the dataset page: https://huggingface.co/datasets/aviadcohz/RWTD-COCO.bongard-rwr-plus
Bongard RWR+
Bongard Problems (BPs) provide a challenging testbed for abstract visual reasoning (AVR), requiring models to identify visual concepts from just a few examples and describe them in natural language.
Early BP benchmarks featured synthetic black-and-white drawings, which might not fully capture the complexity of real-world scenes.
Subsequent BP datasets employed real-world images, albeit
the represented concepts are identifiable from high-level image features, reducing… See the full description on the dataset page: https://huggingface.co/datasets/bongard-rwr-plus/bongard-rwr-plus.bongard-rwr-plus-l2
Bongard RWR+/L2
This is a variant of the dataset featuring two images per side. The original dataset, which includes six images per side, can be found here.
Bongard Problems (BPs) provide a challenging testbed for abstract visual reasoning (AVR), requiring models to identify visual concepts from just a few examples and describe them in natural language.
Early BP benchmarks featured synthetic black-and-white drawings, which might not fully capture the complexity of real-world scenes.… See the full description on the dataset page: https://huggingface.co/datasets/spawlonka/bongard-rwr-plus-l2.bongard-rwr-plus
Bongard RWR+
Bongard Problems (BPs) provide a challenging testbed for abstract visual reasoning (AVR), requiring models to identify visual concepts from just a few examples and describe them in natural language.
Early BP benchmarks featured synthetic black-and-white drawings, which might not fully capture the complexity of real-world scenes.
Subsequent BP datasets employed real-world images, albeit
%with real-world
the represented concepts are identifiable from high-level image… See the full description on the dataset page: https://huggingface.co/datasets/spawlonka/bongard-rwr-plus.bongard-rwr-plus-l6
Bongard RWR+/L6
This is a variant of the dataset that features six images per side and reduced image diversity across instances. The original dataset is available here.
Bongard Problems (BPs) provide a challenging testbed for abstract visual reasoning (AVR), requiring models to identify visual concepts from just a few examples and describe them in natural language.
Early BP benchmarks featured synthetic black-and-white drawings, which might not fully capture the complexity of… See the full description on the dataset page: https://huggingface.co/datasets/spawlonka/bongard-rwr-plus-l6.RWDS
🌍 Dataset Card for Real-World Distribution Shifts (RWDS)
This repository contains the data presented in Benchmarking Object Detectors under Real-World Distribution Shifts in Satellite Imagery.
📊 Dataset Description
Homepage: https://RWGAI.com/RWDS/
Repository: https://github.com/RWGAI/RWDS
Paper: Benchmarking Object Detectors under Real-World Distribution Shifts in Satellite Imagery
Leaderboard: N/A
Point of Contact: salemadi@hbku.edu.qa
🎯 Dataset Summary… See the full description on the dataset page: https://huggingface.co/datasets/RWGAI/RWDS.bongard-rwr-plus-l4
Bongard RWR+/L4
This is a variant of the dataset featuring four images per side. The original dataset, which includes six images per side, can be found here.
Bongard Problems (BPs) provide a challenging testbed for abstract visual reasoning (AVR), requiring models to identify visual concepts from just a few examples and describe them in natural language.
Early BP benchmarks featured synthetic black-and-white drawings, which might not fully capture the complexity of real-world… See the full description on the dataset page: https://huggingface.co/datasets/spawlonka/bongard-rwr-plus-l4.rwy_obb-300-65-65_v2
Runway Object-Oriented Bounding Box (OBB) Segmentation Dataset v2
Dataset Description
This dataset contains aerial/satellite imagery for runway segmentation using oriented bounding boxes (OBB). The dataset is specifically designed for semantic segmentation tasks focusing on runway detection and delineation in aerial imagery.
Dataset Statistics
Split
Images
Labels
Train
303
303
Val
65
65
Test
65
65
Classes
{
"0": "_background_"… See the full description on the dataset page: https://huggingface.co/datasets/Spatiallysaying/rwy_obb-300-65-65_v2.519_proj_testRWAVS
AV-NeRF: Learning Neural Fields for Real-World Audio-Visual Scene Synthesis
Susan Liang, Chao Huang, Yapeng Tian, Anurag Kumar, Chenliang Xu
RWAVS Dataset
We provide the Real-World Audio-Visual Scene (RWAVS) Dataset.
The dataset can be downloaded from this Hugging Face repository.
After you download the dataset, you can decompress the RWAVS_Release.zip.
unzip RWAVS_Release.zip
cd release/
The data is organized with the following directory structure.
./release/
├── 1
│… See the full description on the dataset page: https://huggingface.co/datasets/susanliang/RWAVS.rwanda-spatialbongard-rwr-plus-gs
Bongard RWR+/GS
This is a variant of the dataset that consists of greyscale images. The original dataset is available here.
Bongard Problems (BPs) provide a challenging testbed for abstract visual reasoning (AVR), requiring models to identify visual concepts from just a few examples and describe them in natural language.
Early BP benchmarks featured synthetic black-and-white drawings, which might not fully capture the complexity of real-world scenes.
Subsequent BP datasets employed… See the full description on the dataset page: https://huggingface.co/datasets/spawlonka/bongard-rwr-plus-gs.RWFS
scamai-deepfake-detector-dataset
This repository contains the dataset used in the research paper 'Do Deepfake Detectors Work in Reality?', done by Scam AI.
Real-World Faceswap Dataset (RWFS)
Overview
This repository contains the Real-World Faceswap Dataset (RWFS) used in our research paper "Do Deepfake Detectors Work in Reality?". RWFS is the first dataset specifically designed to reflect real-world deepfakes as they appear in the wild, rather than in… See the full description on the dataset page: https://huggingface.co/datasets/Scam-AI/RWFS.rwth-jellyroll-deformation-2021-raw
RWTH jelly-roll deformation low-SOC cycling and CT dataset raw mirror
BSEBench status: raw_mirror_pending_validation
This repository is a raw mirror of the RWTH Aachen University Publications research-data record Raw cycle data and CT images of the development of jelly roll deformation in 18650 lithium-ion batteries at low state of charge.
Source record: https://publications.rwth-aachen.de/record/818660
DOI: https://doi.org/10.18154/RWTH-2021-04558
Institution: RWTH Aachen… See the full description on the dataset page: https://huggingface.co/datasets/bsebench-org/rwth-jellyroll-deformation-2021-raw.bongard-rwr-plus-gs
Bongard RWR+/GS
This is a variant of the dataset that consists of greyscale images. The original dataset is available here.
Bongard Problems (BPs) provide a challenging testbed for abstract visual reasoning (AVR), requiring models to identify visual concepts from just a few examples and describe them in natural language.
Early BP benchmarks featured synthetic black-and-white drawings, which might not fully capture the complexity of real-world scenes.
Subsequent BP datasets employed… See the full description on the dataset page: https://huggingface.co/datasets/bongard-rwr-plus/bongard-rwr-plus-gs.RWTD
RWTD
Real-world texture photographs, one appearance transition per image.
One of the four evaluation routes in the ICLR 2027 submission on sub-semantic
image segmentation: partitioning an image into regions that are coherent in
appearance and describable in language, but that need not correspond to any
object, part or material class.
Images: 253
Code: github.com/aviadcohz/Qwen2SAM_Detecture_Benchmark
Weights: aviadcohz/Detecture-ICLR-2027
All four routes in one download:… See the full description on the dataset page: https://huggingface.co/datasets/aviadcohz/RWTD.exo10-original-ArxivQA-selected-degbongard-rwr-plus-l5
Bongard RWR+/L5
This is a variant of the dataset featuring five images per side. The original dataset, which includes six images per side, can be found here.
Bongard Problems (BPs) provide a challenging testbed for abstract visual reasoning (AVR), requiring models to identify visual concepts from just a few examples and describe them in natural language.
Early BP benchmarks featured synthetic black-and-white drawings, which might not fully capture the complexity of real-world… See the full description on the dataset page: https://huggingface.co/datasets/bongard-rwr-plus/bongard-rwr-plus-l5.rwy_obb_mask2former-300-65-65
Runway Object-Oriented Bounding Box (OBB) Mask2Former Dataset v2
Dataset Description
This dataset contains aerial/satellite imagery for runway segmentation using Mask2Former architecture. The dataset supports both semantic and instance segmentation tasks, specifically designed for runway detection and delineation in aerial imagery.
Key Features
Architecture: Optimized for Mask2Former universal segmentation
Task Support: Semantic, instance, and panoptic… See the full description on the dataset page: https://huggingface.co/datasets/Spatiallysaying/rwy_obb_mask2former-300-65-65.bongard-rwr-plus-l3
Bongard RWR+/L3
This is a variant of the dataset featuring three images per side. The original dataset, which includes six images per side, can be found here.
Bongard Problems (BPs) provide a challenging testbed for abstract visual reasoning (AVR), requiring models to identify visual concepts from just a few examples and describe them in natural language.
Early BP benchmarks featured synthetic black-and-white drawings, which might not fully capture the complexity of real-world… See the full description on the dataset page: https://huggingface.co/datasets/spawlonka/bongard-rwr-plus-l3.exo10-realworld-db-combinedRWTDexo7-realworld-db-combined-synbongard-rwr-plus-l3
Bongard RWR+/L3
This is a variant of the dataset featuring three images per side. The original dataset, which includes six images per side, can be found here.
Bongard Problems (BPs) provide a challenging testbed for abstract visual reasoning (AVR), requiring models to identify visual concepts from just a few examples and describe them in natural language.
Early BP benchmarks featured synthetic black-and-white drawings, which might not fully capture the complexity of real-world… See the full description on the dataset page: https://huggingface.co/datasets/bongard-rwr-plus/bongard-rwr-plus-l3.Image2GPS_projectmod-rwkv-paper-imageenglish_OmniDocBench_with_eval