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Fast and accurate human pose estimation in PyTorch. Contains implementation of "Real-time 2D Multi-Person Pose Estimation on CPU: Lightweight OpenPose" paper.
| Date | Stars |
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| 2026-07-24 | 2240 |
| 2026-07-25 | 2240 |
| 2026-07-28 | 2240 |
| 2026-07-30 | 2240 |
| 2026-08-06 | 2240 |
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# Real-time 2D Multi-Person Pose Estimation on CPU: Lightweight OpenPose This repository contains training code for the paper [Real-time 2D Multi-Person Pose Estimation on CPU: Lightweight OpenPose](https://arxiv.org/pdf/1811.12004.pdf). This work heavily optimizes the [OpenPose](https://github.com/CMU-Perceptual-Computing-Lab/openpose) approach to reach real-time inference on CPU with negliable accuracy drop. It detects a skeleton (which consists of keypoints and connections between them) to identify human poses for every person inside the image. The pose may contain up to 18 keypoints: ears, eyes, nose, neck, shoulders, elbows, wrists, hips, knees, and ankles. On COCO 2017 Keypoint Detection validation set this code achives 40% AP for the single scale inference (no flip or any post-processing done). The result can be reproduced using this repository. *This repo significantly overlaps with https://github.com/opencv/openvino_training_extensions, however contains just the necessary code for human pose estimation.* <p align="center"> <img src="data/preview.jpg" /> </p> :fire: Check out our [new work](https://github.com/Daniil-Osokin/gccpm-look-into-person-cvpr19.pytorch) on accurate (and still fast) single-person pose estimation, which ranked 10<sup>th</sup> on CVPR'19 [Look-Into-Person](http://47.100.21.47:9999/index.php) challenge. :fire::fire: Check out our lightweight [3D pose estimation](https://github.com/Daniil-Osokin/lightweight-human-pose-estimation-3d-demo.pytorch), which is based on [Single-Shot Multi-Person 3D Pose Estimation From Monocular RGB](https://arxiv.org/pdf/1712.03453.pdf) paper and this work. ## Table of Contents * [Requirements](#requirements) * [Prerequisites](#prerequisites) * [Training](#training) * [Validation](#validation) * [Pre-trained model](#pre-trained-model) * [C++ demo](#cpp-demo) * [Python demo](#python-demo) * [Citation](#citation) ### Other Implementations * TensorFlow by [murdockhou](https://github.com/murdockhou/lightweight_openpose). * OpenVINO by [Pavel Druzhkov](https://github.com/openvinotoolkit/open_model_zoo/pull/1718/). ## Requirements * Ubuntu 16.04 * Python 3.6 * PyTorch 0.4.1 (should also work with 1.0, but not tested) ## Prerequisites 1. Download COCO 2017 dataset: [http://cocodataset.org/#download](http://cocodataset.org/#download) (train, val, annotations) and unpack it to `<COCO_HOME>` folder. 2. Install requirements `pip install -r requirements.txt` ## Training Training consists of 3 steps (given AP values for full validation dataset): * Training from MobileNet weights. Expected AP after this step is ~38%. * Training from weights, obtained from previous step. Expected AP after this step is ~39%. * Training from weights, obtained from previous step and increased number of refinement stages to 3 in network. Expected AP after this step is ~40% (for the network with 1 refinement stage, two next are discarded). 1. Download pre-trained MobileNet v1 weights `mobilenet_sgd_68.848.pth.tar` from: [https://github.com/marvis/pytorch-mobilenet](https://github.com/marvis/pytorch-mobilenet) (sgd option). If this doesn't work, download from [GoogleDrive](https://drive.google.com/file/d/18Ya27IAhILvBHqV_tDp0QjDFvsNNy-hv/view?usp=sharing). 2. Convert train annotations in internal format. Run `python scripts/prepare_train_labels.py --labels <COCO_HOME>/annotations/person_keypoints_train2017.json`. It will produce `prepared_train_annotation.pkl` with converted in internal format annotations. [OPTIONAL] For fast validation it is recommended to make *subset* of validation dataset. Run `python scripts/make_val_subset.py --labels <COCO_HOME>/annotations/person_keypoints_val2017.json`. It will produce `val_subset.json` with annotations just for 250 random images (out of 5000). 3. To train from MobileNet weights, run `python train.py --train-images-folder <COCO_HOME>/train2017/ --prepared-train-labels prepared_train_annotation.pkl --val-labels val_subset.json --val-images-fold
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matched fp:feace23f04f34b47, topic:deep-learning, topic:pytorch
matched fp:feace23f04f34b47, topic:pose-estimation, name:pose estimation, desc:pose estimation