README.md

---
annotations_creators: []
language: en
license: bsd-2-clause
size_categories:
- 10K<n<100K
task_categories:
- image-classification
- object-detection
task_ids: []
pretty_name: MPII Human Pose
tags:
- MPII Human Pose
- fiftyone
- image
- image-classification
- object-detection
- version1
dataset_summary: '



  ![image/png](dataset_preview.png)



  This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 24984 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/MPII_Human_Pose_Dataset")


  # Launch the App

  session = fo.launch_app(dataset)

  ```

  '
---

# Dataset Card for MPII Human Pose

<!-- Provide a quick summary of the dataset. -->

MPII Human Pose dataset is a state of the art benchmark for evaluation of articulated human pose estimation. 

The dataset includes around **25K images** containing over **40K people with annotated body joints**. 

The images were systematically collected using an established **taxonomy of every day human activities**. 

Overall the dataset covers **410 human activities** and each image is provided with an activity label.

Each image was extracted from a **YouTube video** and provided with preceding and following un-annotated frames. 

In addition, for the test set, richer annotations were obtained including body part occlusions and 3D torso and head orientations.

Following the best practices for the performance evaluation benchmarks in the literature we withhold the test annotations to prevent overfitting and tuning on the test set.
We are working on an automatic evaluation server and performance analysis tools based on rich test set annotations.

![image/png](dataset_preview.png)

This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 24984 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/MPII_Human_Pose_Dataset")

# Launch the App
session = fo.launch_app(dataset)
```


## Dataset Details

### Dataset Description

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

>MPII Human Pose Dataset, Version 1.0
Copyright 2015 Max Planck Institute for Informatics
Licensed under the Simplified BSD License
Annotations and the corresponding  are freely available for research purposes. Commercial use is not allowed due to the fact that the authors do not have the copyright for the images themselves.

- **License:** bsd-2-clause

### Dataset Sources 

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

- **Website:** http://human-pose.mpi-inf.mpg.de/#
- **Paper:** http://human-pose.mpi-inf.mpg.de/contents/andriluka14cvpr.pdf

## Uses

<!-- Address questions around how the dataset is intended to be used. -->

At the time when dataset was released **(2014)**, dataset was evaluated on 2 main tasks:

* Multi-Person Pose Estimation
* Single Person 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:        MPII Human Pose
Media type:  image
Num samples: 24984
Persistent:  True
Tags:        ['version1', 'MPII Human Pose']
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)
    rectangle_id: fiftyone.core.fields.ListField(fiftyone.core.fields.IntField)
    activity:     fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Classifications)
    head_rect:    fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Detections)
    objpos:       fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Keypoints)
    scale:        fiftyone.core.fields.VectorField
    annopoints:   fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Keypoints)
    video_id:     fiftyone.core.fields.StringField
    frame_sec:    fiftyone.core.fields.IntField
```

## Dataset Creation

### Source Data

<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->

#### Data Collection and Processing

<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->

See **Section 2. Dataset - Data collection paragraph** of this [paper](http://human-pose.mpi-inf.mpg.de/contents/andriluka14cvpr.pdf)

#### Who are the source data producers?

<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->

[Source data producers](http://human-pose.mpi-inf.mpg.de/#contact)

### Annotations 

<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->

Annotation description

Annotations are stored in a matlab structure **RELEASE** having following fields

```
.annolist(imgidx) - annotations for image imgidx
    .image.name - image filename
    .annorect(ridx) - body annotations for a person ridx
        .x1, .y1, .x2, .y2 - coordinates of the head rectangle
        .scale - person scale w.r.t. 200 px height
        .objpos - rough human position in the image
        .annopoints.point - person-centric body joint annotations
            .x, .y - coordinates of a joint
            id - joint id (0 - r ankle, 1 - r knee, 2 - r hip, 3 - l hip, 4 - l knee, 5 - l ankle, 6 - pelvis, 7 - thorax, 8 - upper neck, 9 - head top, 10 - r wrist, 11 - r elbow, 12 - r shoulder, 13 - l shoulder, 14 - l elbow, 15 - l wrist)
            is_visible - joint visibility
    .vidx - video index in video_list
    .frame_sec - image position in video, in seconds

img_train(imgidx) - training/testing image assignment

single_person(imgidx) - contains rectangle id ridx of sufficiently separated individuals

act(imgidx) - activity/category label for image imgidx
    act_name - activity name
    cat_name - category name
    act_id - activity id

video_list(videoidx) - specifies video id as is provided by YouTube. To watch video on youtube go to https://www.youtube.com/watch?v=video_list(videoidx)

```

#### 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. -->

See **Section 2. Dataset - Data annotation paragraph** of this [paper](http://human-pose.mpi-inf.mpg.de/contents/andriluka14cvpr.pdf)

## 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{andriluka14cvpr,
               author = {Mykhaylo Andriluka and Leonid Pishchulin and Peter Gehler and Schiele, Bernt}
               title = {2D Human Pose Estimation: New Benchmark and State of the Art Analysis},
               booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
               year = {2014},
               month = {June}
}
```
## More Information


The following [**Github repo**](https://github.com/loloMD/51_contribution/tree/mpii_human_pose/mpii_human_pose) contains code to parse the raw data (in MATLAB format) and convert it into a FiftyOne Dataset 


Dataset conversion and data card contributed by [Loic Mandine](https://lolomd.github.io/)