Datasets:
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: >
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
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.
This is a FiftyOne dataset with 24984 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/MPII_Human_Pose_Dataset")
# Launch the App
session = fo.launch_app(dataset)
Dataset Details
Dataset Description
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
- Website: http://human-pose.mpi-inf.mpg.de/#
- Paper: http://human-pose.mpi-inf.mpg.de/contents/andriluka14cvpr.pdf
Uses
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
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
Data Collection and Processing
See Section 2. Dataset - Data collection paragraph of this paper
Who are the source data producers?
Annotations
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
See Section 2. Dataset - Data annotation paragraph of this paper
Citation
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 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