Datasets:
Tasks:
Object Detection
Modalities:
Image
Languages:
English
Size:
10K<n<100K
ArXiv:
Libraries:
FiftyOne
License:
File size: 4,076 Bytes
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---
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: >
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.
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) |