A large-scale video dataset for the training and evaluation of 3D human pose estimation models

Overview

ASPset-510

ASPset logo

ASPset-510 (Australian Sports Pose Dataset) is a large-scale video dataset for the training and evaluation of 3D human pose estimation models. It contains 17 different amateur subjects performing 30 sports-related actions each, for a total of 510 action clips.

This repository contains Python code for working with ASPset-510.

If you don't want to use these scripts and would prefer to directly download the data yourself, ASPset-510 is available on the Internet Archive at https://archive.org/details/aspset510.

Requirements

Core

$ conda env create -f environment.yml

GUI (Optional)

$ conda env update -f environment-gui.yml

PyTorch (Optional)

$ conda env update -f environment-torch.yml

Scripts

Downloading the dataset

download_data.py downloads and extracts ASPset-510 data.

Example usage:

$ python src/aspset510/bin/download_data.py --data-dir=./data

Note that by default the original archive files will be downloaded and kept in the archives subdirectory of whichever path you set using --data-dir. To set a different path for the archives, use the --archive-dir option. To download the archives without extracting them, use the --skip-extraction option.

Browsing clips from the dataset

browse_clips.py provides a graphical user interface for browsing clips from ASPset-510.

Example usage:

$ python src/aspset510/bin/browse_clips.py --data-dir=./data

Screenshot of the clip browser GUI

Acknowledgments and license

ASPset-510 is brought to you by La Trobe University and the Australian Institute of Sport. It is dedicated to the public domain under the CC0 1.0 license.

If you find this dataset useful for your own work, please cite the following paper:

@article{nibali2021aspset,
  title={{ASPset}: An Outdoor Sports Pose Video Dataset With {3D} Keypoint Annotations},
  author={Nibali, Aiden and Millward, Joshua and He, Zhen and Morgan, Stuart},
  journal={Image and Vision Computing},
  pages={104196},
  year={2021},
  issn={0262-8856},
  doi={https://doi.org/10.1016/j.imavis.2021.104196},
  url={https://www.sciencedirect.com/science/article/pii/S0262885621001013},
  publisher={Elsevier}
}
Owner
Aiden Nibali
Aiden Nibali
[CVPR 2021] Exemplar-Based Open-Set Panoptic Segmentation Network (EOPSN)

EOPSN: Exemplar-Based Open-Set Panoptic Segmentation Network (CVPR 2021) PyTorch implementation for EOPSN. We propose open-set panoptic segmentation t

Jaedong Hwang 49 Dec 30, 2022
RL algorithm PPO and IRL algorithm AIRL written with Tensorflow.

RL algorithm PPO and IRL algorithm AIRL written with Tensorflow. They have a parallel sampling feature in order to increase computation speed (especially in high-performance computing (HPC)).

Fangjian Li 3 Dec 28, 2021
Seach Losses of our paper 'Loss Function Discovery for Object Detection via Convergence-Simulation Driven Search', accepted by ICLR 2021.

CSE-Autoloss Designing proper loss functions for vision tasks has been a long-standing research direction to advance the capability of existing models

Peidong Liu(刘沛东) 54 Dec 17, 2022
Dense Contrastive Learning (DenseCL) for self-supervised representation learning, CVPR 2021.

Dense Contrastive Learning for Self-Supervised Visual Pre-Training This project hosts the code for implementing the DenseCL algorithm for se

Xinlong Wang 491 Jan 03, 2023
MQBench: Towards Reproducible and Deployable Model Quantization Benchmark

MQBench: Towards Reproducible and Deployable Model Quantization Benchmark We propose a benchmark to evaluate different quantization algorithms on vari

494 Dec 29, 2022
Annealed Flow Transport Monte Carlo

Annealed Flow Transport Monte Carlo Open source implementation accompanying ICML 2021 paper by Michael Arbel*, Alexander G. D. G. Matthews* and Arnaud

DeepMind 30 Nov 21, 2022
This is the official repository for our paper: ''Pruning Self-attentions into Convolutional Layers in Single Path''.

Pruning Self-attentions into Convolutional Layers in Single Path This is the official repository for our paper: Pruning Self-attentions into Convoluti

Zhuang AI Group 77 Dec 26, 2022
Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance Fields.

This repository contains the code release for Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance Fields. This implementation is written in JAX, and is a fork of Google's JaxNeRF

Google 625 Dec 30, 2022
A Tensorflow based library for Time Series Modelling with Gaussian Processes

Markovflow Documentation | Tutorials | API reference | Slack What does Markovflow do? Markovflow is a Python library for time-series analysis via prob

Secondmind Labs 24 Dec 12, 2022
Repository for benchmarking graph neural networks

Benchmarking Graph Neural Networks Updates Nov 2, 2020 Project based on DGL 0.4.2. See the relevant dependencies defined in the environment yml files

NTU Graph Deep Learning Lab 2k Jan 03, 2023
Google-drive-to-sqlite - Create a SQLite database containing metadata from Google Drive

google-drive-to-sqlite Create a SQLite database containing metadata from Google

Simon Willison 140 Dec 04, 2022
Improving Transferability of Representations via Augmentation-Aware Self-Supervision

Improving Transferability of Representations via Augmentation-Aware Self-Supervision Accepted to NeurIPS 2021 TL;DR: Learning augmentation-aware infor

hankook 38 Sep 16, 2022
Many Class Activation Map methods implemented in Pytorch for CNNs and Vision Transformers. Including Grad-CAM, Grad-CAM++, Score-CAM, Ablation-CAM and XGrad-CAM

Class Activation Map methods implemented in Pytorch pip install grad-cam ⭐ Tested on many Common CNN Networks and Vision Transformers. ⭐ Includes smoo

Jacob Gildenblat 6.6k Jan 06, 2023
Code repository for the paper "Doubly-Trained Adversarial Data Augmentation for Neural Machine Translation" with instructions to reproduce the results.

Doubly Trained Neural Machine Translation System for Adversarial Attack and Data Augmentation Languages Experimented: Data Overview: Source Target Tra

Steven Tan 1 Aug 18, 2022
Deep Inertial Prediction (DIPr)

Deep Inertial Prediction For more information and context related to this repo, please refer to our website. Getting Started (non Docker) Note: you wi

Arcturus Industries 12 Nov 11, 2022
Code for paper "Context-self contrastive pretraining for crop type semantic segmentation"

Code for paper "Context-self contrastive pretraining for crop type semantic segmentation" Setting up a python environment Follow the instruction in ht

Michael Tarasiou 11 Oct 09, 2022
Boosting Monocular Depth Estimation Models to High-Resolution via Content-Adaptive Multi-Resolution Merging

Boosting Monocular Depth Estimation Models to High-Resolution via Content-Adaptive Multi-Resolution Merging This repository contains an implementation

Computational Photography Lab @ SFU 1.1k Jan 02, 2023
Recurrent Scale Approximation (RSA) for Object Detection

Recurrent Scale Approximation (RSA) for Object Detection Codebase for Recurrent Scale Approximation for Object Detection in CNN published at ICCV 2017

Yu Liu (Louis) 239 Dec 28, 2022
Implementation of hyperparameter optimization/tuning methods for machine learning & deep learning models

Hyperparameter Optimization of Machine Learning Algorithms This code provides a hyper-parameter optimization implementation for machine learning algor

Li Yang 1.1k Dec 19, 2022
Fast Scattering Transform with CuPy/PyTorch

Announcement 11/18 This package is no longer supported. We have now released kymatio: http://www.kymat.io/ , https://github.com/kymatio/kymatio which

Edouard Oyallon 289 Dec 07, 2022