datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
CVPR-BiomedSegFMThis repository contains the BiomedSegFM dataset, a crucial resource for the CVPR 2025 Competition: Foundation Models for 3D Biomedical Image Segmentation.
Foundation Models for Interactive 3D Biomedical Image Segmentation (Homepage)
Foundation Models for Text-guided 3D Biomedical Image Segmentation (Homepage)
CVPR 2025 Competition: Foundation Models for 3D Biomedical Image Segmentation
Highly recommend watching the webinar recording to learn about the task settings and… See the full description on the dataset page: https://huggingface.co/datasets/junma/CVPR-BiomedSegFM.CVPR_2024_Papers
Dataset Card for cvpr2024_papers
This is a FiftyOne dataset with 2379 samples.
The dataset consists of images of the first page for accepted papers to CVPR 2024, plus their abstract and other metadata.
Installation
If you haven't already, install FiftyOne:
pip install -U fiftyone
Usage
import fiftyone as fo
import fiftyone.utils.huggingface as fouh
# Load the dataset
# Note: other available arguments include 'split', 'max_samples', etc
dataset =… See the full description on the dataset page: https://huggingface.co/datasets/Voxel51/CVPR_2024_Papers.cv-projectCVPR26-3DCTFMCompetition
CVPR26-3DCTFMCompetition Dataset
This repository contains data prepared for the CVPR 2026 Workshop Challenge: Foundation Models for General CT Image Diagnosis.
Data origin
The datasets included here are from previously published works:
COVID-CT
Rahimzadeh, M., Attar, A., & Sakhaei, S. M. (2021). A fully automated deep learning-based network for detecting COVID-19 from a new and large lung CT scan dataset. Biomedical Signal Processing and Control, 102588.… See the full description on the dataset page: https://huggingface.co/datasets/kmin06/CVPR26-3DCTFMCompetition.THINGS-EEG-MEG_CVPR2026CVPR-2026CVPR_workshop_efficiencyVLMEEG_Image_CVPR_ALL_subj
EEG-Based Visual Classification Dataset
This dataset is used in the paper "Guess What I Think: Streamlined EEG-to-Image Generation with Latent Diffusion Models" available on arxiv and the code is available at this repository.
The dataset is constructed to be used in controlnet scenarios.
This dataset includes EEG data from 6 subjects. The recording protocol included 40 object classes with 50 images each, taken from the ImageNet dataset, giving a total of 2,000 images. Visual stimuli… See the full description on the dataset page: https://huggingface.co/datasets/luigi-s/EEG_Image_CVPR_ALL_subj.CVPR-2026-WorldModel-Track-Dataset
GigaBrain Challenge 2026 (CVPR 2026 Workshop Competition)
Registration
To access the dataset you must register your team.
Required information:
Team name
Team leader
Team members
Organization
Leader email
Click Request Access to participate.
Resources
After approval you will be able to download:
Training dataset
Test dataset
Baseline model
Evaluation scripts
CVPR2025CVPR_2023_OCR
CVPR_2023_OCR
OCR Data.
CVPR_Papers
CVPR Papers
Since 2013, deep learning has revolutionized computer vision, with CVPR (IEEE Conference on Computer Vision and Pattern Recognition) serving as the premier venue documenting this transformative journey. From AlexNet's breakthrough to the rise of Transformers, CVPR papers chronicle the complete trajectory of computer vision advancement.
CVPR Papers is a comprehensive dataset containing all papers from CVPR 2013 to present, including metadata and… See the full description on the dataset page: https://huggingface.co/datasets/choucsan/CVPR_Papers.papercli-papers-cvpr
AI Conference & Journal Papers - CVPR PDF Storage
This repository is a storage shard containing the raw PDF files for CVPR papers. It is part of the larger AI Conference & Journal Papers dataset project.
⚠️ Important: This repository only contains the sharded PDF binary files. It does not contain the searchable metadata (titles, abstracts, authors, etc.).
To search, browse, or filter papers, you must use the Main Parent Repository:
👉 Main Dataset & Metadata:… See the full description on the dataset page: https://huggingface.co/datasets/GenAI4ELab/papercli-papers-cvpr.CVPR_2024_OCR
CVPR_2024_OCR
OCR Data.
CVPR2026CVPR_2024_Papers_with_Embeddings
Dataset Card for Voxel51/CVPR_2024_Papers
This is a FiftyOne dataset with 2379 samples.
Installation
If you haven't already, install FiftyOne:
pip install -U fiftyone
Usage
import fiftyone as fo
import fiftyone.utils.huggingface as fouh
# Load the dataset
# Note: other available arguments include 'max_samples', etc
dataset = fouh.load_from_hub("harpreetsahota/CVPR_2024_Papers_with_Embeddings")
# Launch the App
session = fo.launch_app(dataset)… See the full description on the dataset page: https://huggingface.co/datasets/harpreetsahota/CVPR_2024_Papers_with_Embeddings.CVPR25-OSGNet-TACoS
TACoS Feature for OSGNet
This repository provides the TACoS features for Object-Shot Enhanced Grounding Network (OSGNet).
For installation, dataset preparation, training, and evaluation, please refer to the main repository:
GitHub: https://github.com/iLearn-Lab/CVPR25-OSGNet
Paper: https://openaccess.thecvf.com/content/CVPR2025/html/Feng_Object-Shot_Enhanced_Grounding_Network_for_Egocentric_Video_CVPR_2025_paper.html
Checkpoints: https://huggingface.co/iLearn-Lab/CVPR25-OSGNet
CVPR2024-papersCVPR2024-papers-abstract-indexCVPR2023-papersCV_project
Omni Instrument CV Project Dataset
The Omni Instrument CV Project is a compact robotics dataset combining:
Raw ROS 2 MCAP bag (stereo, depth, TF, odom)
Ground truth mesh (STL)
Distilled synchronized dataset (100 samples)
Contents
1. Raw Data
Contains a stripped ROS 2 MCAP recording:
/zed/zedxm/left/color/rect/image
/zed/zedxm/right/color/rect/image
/zed/zedxm/depth/depth_registered
/zed/zedxm/odom
/tf, /tf_static
Associated CameraInfo… See the full description on the dataset page: https://huggingface.co/datasets/OmniInstrument/CV_project.CVPR2025_EditAR_releasecvpr_workshop_sam_prompt_clustersseqCVPR25-OSGNet-Ego4D-NLQ
Ego4D-NLQ Feature for OSGNet
This repository provides the NLQ features for Object-Shot Enhanced Grounding Network (OSGNet). For video feature and text feature of nlq_v2, we do not redistribute the feature in this repository. Please see nlq_v2/GROUNDNLQ_FEATURE.md for how to obtain it from GroundNLQ.
For installation, dataset preparation, training, and evaluation, please refer to the main repository:
GitHub: https://github.com/iLearn-Lab/CVPR25-OSGNet
Paper:… See the full description on the dataset page: https://huggingface.co/datasets/iLearn-Lab/CVPR25-OSGNet-Ego4D-NLQ.CVPR2022_CoordGAN_releaseRoboTwin-CVPR-Challenge-2025CVPR25-OSGNet-Ego4D-GoalStep
Ego4D-GoalStep Feature for OSGNet
This repository provides the GoalStep Feature for Object-Shot Enhanced Grounding Network (OSGNet).
For installation, dataset preparation, training, and evaluation, please refer to the main repository:
GitHub: https://github.com/iLearn-Lab/CVPR25-OSGNet
Paper: https://openaccess.thecvf.com/content/CVPR2025/html/Feng_Object-Shot_Enhanced_Grounding_Network_for_Egocentric_Video_CVPR_2025_paper.html
Checkpoints:… See the full description on the dataset page: https://huggingface.co/datasets/iLearn-Lab/CVPR25-OSGNet-Ego4D-GoalStep.CVPR2023CVPR_OCR
CVPR_OCR
OCR Data split by Venue.