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}
  }

Dataset Card Authors

Kishan Savant