datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
EEG_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.CVPR2025CVPR_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.CVPR2026CVPR25-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-papersCVPR2023-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.seqCVPR25-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.CVPR25-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.CVPR2023cv_proj
Object Detection Dataset
Dataset Description
This dataset contains images and labels for object detection with 9 classes:
cat
dog
cup
vase
potted plant
laptop
keyboard
remote
book
Dataset Structure
dataset/
images/
train/
val/
test/
labels/
train/
val/
test/
dataset.yaml
Classes
0: cat
1: dog
2: cup
3: vase
4: potted plant
5: laptop
6: keyboard
7: remote
8: book
Usage
This… See the full description on the dataset page: https://huggingface.co/datasets/Sean676/cv_proj.CVPR2024CVPR-2023-Accepted-Papers
CVPR 2023 Accepted Paper Meta Info Dataset
This dataset is collect from the CVPR 2023 Open Access website (https://openaccess.thecvf.com/CVPR2023) as well as the arxiv website DeepNLP paper arxiv (http://www.deepnlp.org/content/paper/cvpr2023). For researchers who are interested in doing analysis of CVPR 2023 accepted papers and potential trends, you can use the already cleaned up json files. Each row contains the meta information of a paper in the CVPR 2024 conference. To explore… See the full description on the dataset page: https://huggingface.co/datasets/DeepNLP/CVPR-2023-Accepted-Papers.BrainWash-CVPR24CVPR2024-papersCVPR-2022-Accepted-Papers
CVPR 2023 Accepted Paper Meta Info Dataset
This dataset is collect from the CVPR 2023 Open Access website (https://openaccess.thecvf.com/CVPR2023) as well as the arxiv website DeepNLP paper arxiv (http://www.deepnlp.org/content/paper/cvpr2023). For researchers who are interested in doing analysis of CVPR 2023 accepted papers and potential trends, you can use the already cleaned up json files. Each row contains the meta information of a paper in the CVPR 2024 conference. To explore… See the full description on the dataset page: https://huggingface.co/datasets/DeepNLP/CVPR-2022-Accepted-Papers.CVPR2017CVPR2022CVPR_2024_Papersdef remove_ref_tags_except_image_from_sample(sample):
"""
cleanup the deepseek exrtra tags
"""
lines = sample["text"].split('\n')
filtered_lines = []
for line in lines:
# Check if line contains any <|ref|> tag
if '<|ref|>' in line:
# Keep the line only if it contains <|ref|>image<|/ref|>
if '<|ref|>image<|/ref|>' in line:
filtered_lines.append(line)
else:
# Keep lines without <|ref|> tags… See the full description on the dataset page: https://huggingface.co/datasets/feyninc/CVPR_2024_Papers.dataset-public
Auto-Annotation with Expert-Crafted Guidelines: A Study through 3D LiDAR Detection Benchmark
Inspired by the critical bottleneck of data annotation in autonomous driving and recent advancements in foundation models, this dataset introduces a novel evaluation paradigm: Auto-Annotation from Expert-Crafted Guidelines.
Unlike traditional 3D perception benchmarks that rely on massive amounts of annotated 3D point clouds for supervised learning, AutoExpert is designed to evaluate a… See the full description on the dataset page: https://huggingface.co/datasets/autoexpert-cvpr2026-workshop/dataset-public.CVPR2021CVPR2020CVPR2018CVPR2016CVPR-2020-Accepted-Papers
CVPR 2020 Accepted Paper Meta Info Dataset
This dataset is collect from the CVPR 2020 Open Access website (https://openaccess.thecvf.com/CVPR2020) as well as the arxiv website DeepNLP paper arxiv (http://www.deepnlp.org/content/paper/cvpr2020). For researchers who are interested in doing analysis of CVPR 2020 accepted papers and potential trends, you can use the already cleaned up json files. Each row contains the meta information of a paper in the CVPR 2020 conference. To explore… See the full description on the dataset page: https://huggingface.co/datasets/DeepNLP/CVPR-2020-Accepted-Papers.CVPR2013CVPR-2024-Accepted-Papers
CVPR 2024 Accepted Paper Meta Info Dataset
This dataset is collect from the CVPR 2024 Open Access website (https://openaccess.thecvf.com/CVPR2024) as well as the arxiv website DeepNLP paper arxiv (http://www.deepnlp.org/content/paper/cvpr2024). For researchers who are interested in doing analysis of CVPR 2024 accepted papers and potential trends, you can use the already cleaned up json files. Each row contains the meta information of a paper in the CVPR 2024 conference. To explore… See the full description on the dataset page: https://huggingface.co/datasets/DeepNLP/CVPR-2024-Accepted-Papers.CV_problem_onsite