H36m Toolbox
| Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
|---|---|---|---|---|---|---|---|---|---|---|
| Talon | 1,168 | 12 | a year ago | 41 | August 24, 2017 | 65 | apache-2.0 | Python | ||
| Dureader | 924 | a year ago | 19 | apache-2.0 | Python | |||||
| Baseline Systems of DuReader Dataset | ||||||||||
| Knowledge Graph Learning | 662 | 8 months ago | 336 | mit | ||||||
| A curated list of awesome knowledge graph tutorials, projects and communities. | ||||||||||
| Annotated Semantic Relationships Datasets | 565 | 2 years ago | ||||||||
| A collections of public and free annotated datasets of relationships between entities/nominals (Portuguese and English) | ||||||||||
| Dl_eventextractionpapers | 555 | 8 months ago | 1 | mit | ||||||
| 2015年以来基于深度学习方法的事件抽取论文整理 | ||||||||||
| Covid 19 Repo Data | 442 | 10 months ago | 15 | cc0-1.0 | ||||||
| Data archive of identifiable COVID-19 related public projects on GitHub | ||||||||||
| Casrel | 440 | 3 years ago | 1 | mit | Python | |||||
| A Novel Cascade Binary Tagging Framework for Relational Triple Extraction. Accepted by ACL 2020. | ||||||||||
| Oie Resources | 435 | a year ago | 2 | |||||||
| A curated list of Open Information Extraction (OIE) resources: papers, code, data, etc. | ||||||||||
| Docred | 323 | 3 years ago | mit | Python | ||||||
| Dataset and codes for ACL 2019 DocRED: A Large-Scale Document-Level Relation Extraction Dataset. | ||||||||||
| Cmrc2018 | 313 | a year ago | 4 | cc-by-sa-4.0 | Python | |||||
| A Span-Extraction Dataset for Chinese Machine Reading Comprehension (CMRC 2018) | ||||||||||
This code is built on top of anibali/h36m-fetch
Human3.6M is a 3D human pose dataset containing 3.6 million human poses and corresponding images. The scripts in this repository make it easy to download, extract, and preprocess the images and annotations from Human3.6M.
Please do not ask me for a copy of the Human3.6M dataset. I do not own the data, nor do I have permission to redistribute it. Please visit http://vision.imar.ro/human3.6m/ in order to request access and contact the maintainers of the dataset.
Requirements
- Python 3
axel- CDF (https://www.scivision.dev/spacepy-install-anaconda-python/)
Usage
- Firstly, you will need to create an account at http://vision.imar.ro/human3.6m/ to gain access to the dataset.
- Once your account has been approved, log in and inspect your cookies to find your PHPSESSID.
- Copy the configuration file
config.ini.exampletoconfig.iniand fill in your PHPSESSID. - download_all.py -> extract_all.py -> video_to_images.py -> generate_labels.py
License
The code in this repository is licensed under the terms of the Apache License, Version 2.0.
Please read the
license agreement for the
Human3.6M dataset itself, which specifies citations you must make when
using the data in your own research. The file metadata.xml is directly
copied from the "Visualisation and large scale prediction software"
bundle from the Human3.6M website, and is subject to the same license
agreement.
