Datasets:
Tasks:
Object Detection
Modalities:
Image
Languages:
English
Size:
10K<n<100K
ArXiv:
Libraries:
FiftyOne
License:
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End of preview.
Dataset Card for DensePose-COCO
DensePose-COCO is a large-scale ground-truth dataset with image-to-surface correspondences manually annotated on COCO images.
This is a FiftyOne dataset with 33929 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("Voxel51/DensePose-COCO")
# Launch the App
session = fo.launch_app(dataset)
Dataset Details
Dataset Description
- Curated by: Rıza Alp Güler, Natalia Neverova, Iasonas Kokkinos
- Language(s) (NLP): en
- License: cc-by-nc-2.0
Dataset Sources
- Repository: https://github.com/facebookresearch/Densepose
- Paper : https://arxiv.org/abs/1802.00434
- Homepage: http://densepose.org/
Uses
Dense human pose estimation
Dataset Structure
Name: DensePoseCOCO
Media type: image
Num samples: 33929
Persistent: False
Tags: []
Sample fields:
id: fiftyone.core.fields.ObjectIdField
filepath: fiftyone.core.fields.StringField
tags: fiftyone.core.fields.ListField(fiftyone.core.fields.StringField)
metadata: fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.metadata.ImageMetadata)
detections: fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Detections)
segmentations: fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Detections)
keypoints: fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Keypoints)
The dataset has 2 splits: "train" and "val". Samples are tagged with their split.
Dataset Creation
Curation Rationale
Please refer the homepage and the paper for the curation rationale.
Annotation process
Please refer the github repo for the annotation process.
Citation
BibTeX:
@InProceedings{Guler2018DensePose,
title={DensePose: Dense Human Pose Estimation In The Wild},
author={R\{i}za Alp G\"uler, Natalia Neverova, Iasonas Kokkinos},
journal={The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
year={2018}
}
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