datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
acdc
General information
The overall ACDC dataset was created from real clinical exams acquired at the University Hospital of Dijon. Acquired data were fully anonymized and handled within the regulations set by the local ethical committee of the Hospital of Dijon (France). Our dataset covers several well-defined pathologies with enough cases to (1) properly train machine learning methods and (2) clearly assess the variations of the main physiological parameters obtained from cine-MRI (in… See the full description on the dataset page: https://huggingface.co/datasets/msepulvedagodoy/acdc.ACDC
General information
The overall ACDC dataset was created from real clinical exams acquired at the University Hospital of Dijon. Acquired data were fully anonymized and handled within the regulations set by the local ethical committee of the Hospital of Dijon (France). Our dataset covers several well-defined pathologies with enough cases to (1) properly train machine learning methods and (2) clearly assess the variations of the main physiological parameters obtained from cine-MRI (in… See the full description on the dataset page: https://huggingface.co/datasets/MedOtter/ACDC.acdc-cycleganACDC
ACDC Dataset
This is a pre-processed version of the original
Automated Cardiac Diagnosis Challenge (ACDC) dataset.
Short-axis view images have been resampled to 1mm x 1mm x 10mm. Images have also been center cropped at the left
ventricle mask center. The size of each slice is 192 x 192 pixels.
Download the dataset using the following command:
from huggingface_hub import snapshot_download
data_dir = snapshot_download(repo_id="mathpluscode/ACDC", allow_patterns=["*.nii.gz", "*.csv"]… See the full description on the dataset page: https://huggingface.co/datasets/mathpluscode/ACDC.cardiac_cine_acdc
ACDC (Cardiac Cine-MRI)
ACDC (Automatic Cardiac Diagnosis Challenge, MICCAI 2017) is a cine‑MRI dataset for cardiac segmentation.This repository contains processed NIfTI files in Data/processed_output/acdc format.
Dataset Summary
Modality: Cardiac cine‑MRI (NIfTI)
Task: Segmentation of LV, RV, and myocardium
Frames: ED/ES + full SAX time series (sax_t)
Labels: LV/RV cavities + myocardium
Splits: train, test (as provided in processed output)
Data Structure (per… See the full description on the dataset page: https://huggingface.co/datasets/viennh2012/cardiac_cine_acdc.ACDC
This dataset was used for DINO-AugSeg (paper: https://arxiv.org/abs/2601.08078)
Source code is available on GitHub (https://github.com/apple1986/DINO-AugSeg/tree/main)
Auto-ACD
Auto-ACD
Auto-ACD is a large-scale, high-quality, audio-language dataset, building on the prior of robust audio-visual correspondence in existing video datasets, VGGSound and AudioSet.
Homepage: https://auto-acd.github.io/
Paper: https://huggingface.co/papers/2309.11500
Github: https://github.com/LoieSun/Auto-ACD
Analysis
Auto-ACD, comprising over 1.9M audio-text pairs.
As shown in figure, The text descriptions in Auto-ACD contain long texts (18 words) and… See the full description on the dataset page: https://huggingface.co/datasets/Loie/Auto-ACD.ACDC
About
This is a redistribution of the ACDC dataset.
300 cardiac MR images and corresponding segmentation masks
No change to any image or segmentation mask
Files are rearranged into Images and Masks folders
This dataset is released under the CC BY-NC-SA 4.0 license.
News 🔥
[10 Oct, 2025] This dataset is integrated into 🔥MedVision🔥
Segmentation Labels
labels_map = {
"1": "right ventricular cavity",
"2": "myocardium",
"3": "left ventricular… See the full description on the dataset page: https://huggingface.co/datasets/YongchengYAO/ACDC.permuformer_dataACDC
Automated Cardiac Diagnosis Challenge (ACDC)
This dataset contains materials from the Automated Cardiac Diagnosis Challenge (ACDC) introduced during MICCAI 2017 by Bernard et al., designed to advance research in cardiac MRI analysis, representation learning, and automated cardiac disease understanding.
The dataset includes cine cardiac MRI acquisitions from healthy subjects and patients with multiple cardiac pathologies.
🫀 Dataset Description
This repository provides… See the full description on the dataset page: https://huggingface.co/datasets/chehablab/ACDC.cad-test-imagesacdc
General information
The overall ACDC dataset was created from real clinical exams acquired at the University Hospital of Dijon. Acquired data were fully anonymized and handled within the regulations set by the local ethical committee of the Hospital of Dijon (France). Our dataset covers several well-defined pathologies with enough cases to (1) properly train machine learning methods and (2) clearly assess the variations of the main physiological parameters obtained from cine-MRI (in… See the full description on the dataset page: https://huggingface.co/datasets/MohidAbdullah/acdc.acdcSource
This folk of the ACDC dataset zhuyinheng/acdc.
Reference
O. Bernard, A. Lalande, C. Zotti, F. Cervenansky, et al.
"Deep Learning Techniques for Automatic MRI Cardiac Multi-structures Segmentation and Diagnosis: Is the Problem Solved ?" in IEEE Transactions on Medical Imaging, vol. 37, no. 11, pp. 2514-2525, Nov. 2018
doi: 10.1109/TMI.2018.2837502
ACDC-FlowRegautomated_cardiac_diagnosis_competition.ACDC
Dataset Card for "automated_cardiac_diagnosis_competition.ACDC"
More Information needed
ACDC_fullquiver_mutation_equivalence
Mutation Equivalence of Quivers
Quivers and quiver mutations are central to the combinatorial study of cluster algebras,
algebraic structures with connections to Poisson Geometry, string theory, and
Teichmuller theory. Quivers are directed (multi)graphs, and a quiver mutation
is a local transformation centered at a chosen node of the graph that involves
adding, deleting, and reversing the orientation of specific edges based on
a set of combinatorial rules. A fundamental open… See the full description on the dataset page: https://huggingface.co/datasets/ACDRepo/quiver_mutation_equivalence.ACDC-RunsThese are the runs from the paper Towards Automated Circuit Discovery for Mechanistic Interpretability (Arthur Conmy et al., 2023).
This repository contains the actual hypotheses that the various algorithms tested found. It's intended to help with reproducibility.
ACDC-PNG
Dataset Download
I created a convenient PNG-formatted version of the ACDC dataset.This version was converted from the files provided in the SSL4MIS repository:SSL4MIS ACDC Version
If you use the dataset in your research, please make sure to cite the original ACDC paper.
Dataset Structure
XXX/
│
├── train-label/ # Labeled training set
│ ├── image/ # Input images (.png)
│ └── mask/ # Corresponding segmentation masks… See the full description on the dataset page: https://huggingface.co/datasets/yscript/ACDC-PNG.acd
ACD — Anti-Cancer Peptide Dataset
ACD is a three-stage, cascading benchmark for anti-cancer peptide (ACP) research. It combines peptide sequences and provenance with precomputed conventional, molecular, sequence, and structure representations for 18,611 unique peptides.
The three prediction stages are:
Stage 1 — ACP identification: distinguish ACPs from non-ACPs.
Stage 2 — target-annotation identification: among ACPs, distinguish peptides with a retained cancer-site annotation… See the full description on the dataset page: https://huggingface.co/datasets/tanthinhdt/acd.qg_acdoublem75This dataset was created using LeRobot.
acdc
General information
The overall ACDC dataset was created from real clinical exams acquired at the University Hospital of Dijon. Acquired data were fully anonymized and handled within the regulations set by the local ethical committee of the Hospital of Dijon (France). Our dataset covers several well-defined pathologies with enough cases to (1) properly train machine learning methods and (2) clearly assess the variations of the main physiological parameters obtained from cine-MRI (in… See the full description on the dataset page: https://huggingface.co/datasets/Mythscalm42/acdc.symmetric_group_characters_22
Characters of Irreducible Representations of the Symmetric Group, S22S_{22}S22One way to understand the algebraic structure of the set of permutations of nnn elements
(the symmetric group, SnS_nSn) is
through its representation theory [1], which converts algebraic questions into linear
algebra questions that are often easier to solve. A representation of group GGG on vector
space VVV, is a map ϕ:G→GL(V)\phi:G \rightarrow GL(V)ϕ:G→GL(V) that converts elements of ggg to invertible… See the full description on the dataset page: https://huggingface.co/datasets/ACDRepo/symmetric_group_characters_22.mheight_function_10
The mHeight Function of a Permutation of Size 10
Truly challenging open problems in mathematics often require the development
of new mathematical constructions (or even entire new areas of mathematics).
This dataset represents a modest example of this. The mHeight function is
a statistic associated with a permutation that relates to all 341234123412-patterns
in the permutation. It was developed and plays a crucial role in the proof by
Gaetz and Gao [1] which resolved a… See the full description on the dataset page: https://huggingface.co/datasets/ACDRepo/mheight_function_10.weaving_patterns_6
Dataset Card for Weaving Patterns of Size, 6×56 \times 56×5
Weaving patterns are size n×(n−1)n \times (n−1)n×(n−1) matrices with {1,2,…,n}\{1, 2, \dots , n\}{1,2,…,n}-
entries introduced by [1] to study the number of reduced decompositions of the longest
permutation (which swaps nnn and 111, nnn - 111 and 222, etc.) up
to commutation equivalence. The number
of such objects counts a wide range of combinatorial phenomena, including the number of parallel sorting
networks, the number… See the full description on the dataset page: https://huggingface.co/datasets/ACDRepo/weaving_patterns_6.ACDCsymmetric_group_characters_20
Characters of Irreducible Representations of the Symmetric Group, S20S_{20}S20One way to understand the algebraic structure of the set of permutations of nnn elements
(the symmetric group, SnS_nSn) is
through its representation theory [1], which converts algebraic questions into linear
algebra questions that are often easier to solve. A representation of group GGG on vector
space VVV, is a map ϕ:G→GL(V)\phi:G \rightarrow GL(V)ϕ:G→GL(V) that converts elements of ggg to invertible… See the full description on the dataset page: https://huggingface.co/datasets/ACDRepo/symmetric_group_characters_20.symmetric_group_characters_18
Characters of Irreducible Representations of the Symmetric Group, S18S_{18}S18One way to understand the algebraic structure of the set of permutations of nnn elements
(the symmetric group, SnS_nSn) is
through its representation theory [1], which converts algebraic questions into linear
algebra questions that are often easier to solve. A representation of group GGG on vector
space VVV, is a map ϕ:G→GL(V)\phi:G \rightarrow GL(V)ϕ:G→GL(V) that converts elements of ggg to invertible… See the full description on the dataset page: https://huggingface.co/datasets/ACDRepo/symmetric_group_characters_18.robinson_schensted_knuth_correspondence_8
The Robinson-Schensted-Knuth Correspondence for Permutations of Size 8
The Robinson-Schensted-Knuth (RSK) algorithm [1,2] gives a bijection between
pairs of semistandard Young tableau of the same shape and matrices with non-negative
integer entries. The special case we consider (which is sometimes called the
Robinson-Schensted algorithm) restricts to a bijection between pairs of standard Young
tableaux and permutations in SnS_nSn. This correspondence is highly significant in… See the full description on the dataset page: https://huggingface.co/datasets/ACDRepo/robinson_schensted_knuth_correspondence_8.robinson_schensted_knuth_correspondence_9
The Robinson-Schensted-Knuth Correspondence for Permutations of Size 9
The Robinson-Schensted-Knuth (RSK) algorithm [1,2] gives a bijection between
pairs of semistandard Young tableau of the same shape and matrices with non-negative
integer entries. The special case we consider (which is sometimes called the
Robinson-Schensted algorithm) restricts to a bijection between pairs of standard Young
tableaux and permutations in SnS_nSn. This correspondence is highly significant in… See the full description on the dataset page: https://huggingface.co/datasets/ACDRepo/robinson_schensted_knuth_correspondence_9.