--- annotations_creators: [] language: en license: bsd-2-clause size_categories: - 10K 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 >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](http://human-pose.mpi-inf.mpg.de/contents/andriluka14cvpr.pdf) #### Who are the source data producers? [Source data producers](http://human-pose.mpi-inf.mpg.de/#contact) ### 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](http://human-pose.mpi-inf.mpg.de/contents/andriluka14cvpr.pdf) ## 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**](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/)