Voxel51/DensePose-COCO

Dataset Card for DensePose-COCO

DensePose-COCO is a large-scale ground-truth dataset with image-to-surface correspondences manually annotated on COCO images.

[image]

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

<!-- Provide a longer summary of what this dataset is. -->

• —Curated by: Rıza Alp Güler, Natalia Neverova, Iasonas Kokkinos
• —Language(s) (NLP): en
• —License: cc-by-nc-2.0

Dataset Sources

<!-- Provide the basic links for the dataset. -->

• —Repository: https://github.com/facebookresearch/Densepose
• —Paper : https://arxiv.org/abs/1802.00434
• —Homepage: http://densepose.org/

Uses

Dense human pose estimation

Dataset Structure

<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->

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

<!-- Motivation for the creation of this dataset. -->

Please refer the homepage and the paper for the curation rationale.

Annotation process

<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->

Please refer the github repo for the annotation process.

Citation

<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->

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