{"id":17260342,"url":"https://github.com/mkocabas/pose-residual-network","last_synced_at":"2025-04-09T07:07:32.572Z","repository":{"id":111840897,"uuid":"140543160","full_name":"mkocabas/pose-residual-network","owner":"mkocabas","description":"Code for the Pose Residual Network introduced in 'MultiPoseNet: Fast Multi-Person Pose Estimation using Pose Residual Network (ECCV 2018)' paper","archived":false,"fork":false,"pushed_at":"2018-09-14T09:41:29.000Z","size":22,"stargazers_count":346,"open_issues_count":12,"forks_count":92,"subscribers_count":21,"default_branch":"master","last_synced_at":"2025-04-02T02:46:42.115Z","etag":null,"topics":["deep-learning","deep-neural-networks","eccv","eccv-2018","keras","pose-estimation","python","tensorflow"],"latest_commit_sha":null,"homepage":"https://arxiv.org/abs/1807.04067","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/mkocabas.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2018-07-11T08:12:02.000Z","updated_at":"2024-08-02T14:58:40.000Z","dependencies_parsed_at":"2023-09-08T02:47:49.787Z","dependency_job_id":null,"html_url":"https://github.com/mkocabas/pose-residual-network","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mkocabas%2Fpose-residual-network","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mkocabas%2Fpose-residual-network/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mkocabas%2Fpose-residual-network/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mkocabas%2Fpose-residual-network/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/mkocabas","download_url":"https://codeload.github.com/mkocabas/pose-residual-network/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":247994121,"owners_count":21030050,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["deep-learning","deep-neural-networks","eccv","eccv-2018","keras","pose-estimation","python","tensorflow"],"created_at":"2024-10-15T07:47:56.504Z","updated_at":"2025-04-09T07:07:32.557Z","avatar_url":"https://github.com/mkocabas.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Pose Residual Network\n\nThis repository contains a Keras implementation of the Pose Residual Network (PRN) presented in our ECCV 2018 paper:\n\nMuhammed Kocabas, Salih Karagoz, Emre Akbas. MultiPoseNet: Fast Multi-Person Pose Estimation using Pose Residual Network. In ECCV, 2018. [Arxiv](https://arxiv.org/abs/1807.04067)\n\nPRN is described in Section 3.2 of the  paper.\n\n\n## Getting Started\nWe have tested our method on [COCO Dataset](http://cocodataset.org)\n\n### Prerequisites\n\n```\npython\ntensorflow\nkeras\nnumpy\ntqdm\npycocotools\nprogress\nscikit-image\n```\n\n### Installing\n\n1. Clone this repository: \n`git clone https://github.com/mkocabas/pose-residual-network.git`\n\n2. Install [Tensorflow](https://www.tensorflow.org/install/).\n\n3. ```pip install -r src/requirements.txt```\n\n4. To download COCO dataset train2017 and val2017 annotations run: `bash data/coco.sh`. (data size: ~240Mb)\n\n## Training\n\n`python main.py`\n\nFor more options take a look at `opt.py`\n\n## Results\nResults on COCO val2017 Ground Truth data.\n\n```\n Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets= 20 ] = 0.894\n Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets= 20 ] = 0.971\n Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets= 20 ] = 0.912\n Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets= 20 ] = 0.875\n Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets= 20 ] = 0.918\n Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 20 ] = 0.909\n Average Recall     (AR) @[ IoU=0.50      | area=   all | maxDets= 20 ] = 0.972\n Average Recall     (AR) @[ IoU=0.75      | area=   all | maxDets= 20 ] = 0.928\n Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets= 20 ] = 0.896\n Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets= 20 ] = 0.947\n```\n\n## License\n\n## Other Implementations\n\n[Pytorch Version](https://github.com/salihkaragoz/pose-residual-network-pytorch)\n\n\n## Citation\nIf you find this code useful for your research, please consider citing our paper:\n```\n@Inproceedings{kocabas18prn,\n  Title          = {Multi{P}ose{N}et: Fast Multi-Person Pose Estimation using Pose Residual Network},\n  Author         = {Kocabas, Muhammed and Karagoz, Salih and Akbas, Emre},\n  Booktitle      = {European Conference on Computer Vision (ECCV)},\n  Year           = {2018}\n}\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmkocabas%2Fpose-residual-network","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmkocabas%2Fpose-residual-network","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmkocabas%2Fpose-residual-network/lists"}