README.md

---
annotations_creators: []
language: en
license: cc-by-nc-2.0
size_categories:
- 10K<n<100K
task_categories:
- object-detection
task_ids: []
pretty_name: DensePose-COCO
tags:
- fiftyone
- image
- object-detection
- segmentation
- keypoints
dataset_summary: >



  ![image/png](dataset_preview.jpg)



  This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 33929
  samples.


  ## Installation


  If you haven't already, install FiftyOne:


  ```bash

  pip install -U fiftyone

  ```


  ## Usage


  ```python

  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")

  # dataset = fouh.load_from_hub("Voxel51/DensePose-COCO", max_samples=1000)



  # Launch the App

  session = fo.launch_app(dataset)

  ```
---

# 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/png](dataset_preview.jpg)


This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 33929 samples.

## Installation

If you haven't already, install FiftyOne:

```bash
pip install -U fiftyone
```

## Usage

```python
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. -->

```plaintext
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:**

```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](https://huggingface.co/NeoKish)