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https://github.com/yuliangxiu/mobilepose
Light-weight Single Person Pose Estimator
https://github.com/yuliangxiu/mobilepose
data-augmentation dataloader deep-learning deeppose dsntnn heatmap lightweight machine-learning mobile-device mobilenetv2 pose-estimation pytorch real-time realtime resnet-18 shufflenet shufflenet-v2 shufflenetv2 squeezenet
Last synced: 1 day ago
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Light-weight Single Person Pose Estimator
- Host: GitHub
- URL: https://github.com/yuliangxiu/mobilepose
- Owner: YuliangXiu
- Created: 2018-01-11T13:59:23.000Z (almost 7 years ago)
- Default Branch: master
- Last Pushed: 2024-05-03T19:39:25.000Z (6 months ago)
- Last Synced: 2024-08-08T23:18:46.906Z (3 months ago)
- Topics: data-augmentation, dataloader, deep-learning, deeppose, dsntnn, heatmap, lightweight, machine-learning, mobile-device, mobilenetv2, pose-estimation, pytorch, real-time, realtime, resnet-18, shufflenet, shufflenet-v2, shufflenetv2, squeezenet
- Language: Jupyter Notebook
- Homepage: http://xiuyuliang.cn
- Size: 37 MB
- Stars: 633
- Watchers: 20
- Forks: 148
- Open Issues: 13
-
Metadata Files:
- Readme: README.md
- Funding: .github/FUNDING.yml
Awesome Lists containing this project
README
# MobilePose
MobilePose is a **Tiny** PyTorch implementation of single person 2D pose estimation framework. The aim is to provide the interface of the training/inference/evaluation, and the dataloader with various data augmentation options. And final trained model can satisfy basic requirements(speed+size+accuracy) for mobile device.
Some codes for networks and display are brought from:
1. [pytorch-mobilenet-v2](https://github.com/tonylins/pytorch-mobilenet-v2)
2. [Vanilla FCN, U-Net, SegNet, PSPNet, GCN, DUC](https://github.com/zijundeng/pytorch-semantic-segmentation)
3. [Shufflenet-v2-Pytorch](https://github.com/ericsun99/Shufflenet-v2-Pytorch)
4. [tf-pose-estimation](https://github.com/ildoonet/tf-pose-estimation)
5. [dsntnn](https://github.com/anibali/dsntnn)
## NEWS!
- Apr 2021: [Siyuan Pan](https://github.com/pansiyuan123) provides [MNN version](https://market.mnn.zone/s/#/modelmarket/detail/107)!
- Mar 2019: Support running on MacBook with decent FPS!
- Feb 2019: **ALL** the pretrained model files are avaliable!
## Requirements
- Python 3.7
- PyTorch 1.0
- [dsntnn 1.0](https://github.com/anibali/dsntnn)
## Evaluation Results
|Model(+DUC+DSNTNN)|Parmas(M)|Flops(G)|[email protected]:0.95|[email protected]|[email protected]:0.95|[email protected]|Link|
|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
|ResNet18|12.26|1.64|**68.2**|93.9|**79.7**|96.7|[51.5M](https://drive.google.com/open?id=17Z1zetIVDI4_8-ZoFgTRsjHtDpwGtjRT)|
|MobileNetV2|3.91|0.49|67.5|**94.9**|79.4|**97.1**|[16.6M](https://drive.google.com/open?id=15Ihv1bVQv6_tYTFlECJMNrXEmrrka5g4)|
|ShuffleNetV2|2.92|**0.31**|61.5|91.6|74.8|95.5|[12.4M](https://drive.google.com/open?id=184Zg4E6HbbizPFYcELMXCd7mwWXdUd3U)|
|SqueezeNet1.1|**2.22**|0.63|58.4|92.1|72.3|95.8|[9.3M](https://drive.google.com/open?id=1RePeiBJHeHvmYTQ5vAUJHC5CstHIBcP0)|
## Features
- [x] multi-thread dataloader with augmentations (dataloader.py)
- [x] training and inference (training.py)
- [x] performance evaluation (eval.py)
- [x] multiple models support (network.py)
- [x] ipython notebook visualization (demo.ipynb)
- [x] Macbook camera realtime display script (run_webcam.py)
## Usage
1. Installation:
```shell
pip install -r requirements.txt
```
2. Training:
```shell
python training.py --model shufflenetv2 --gpu 0 --inputsize 224 --lr 1e-3 --batchsize 128 --t7 ./models/shufflenetv2_224_adam_best.t7
```
3. Evaluation
```shell
ln -s cocoapi/PythonAPI/pycocotools
cd cocoapi/PythonAPI && make
python eval.py --t7 ./models/resnet18_224_adam_best.t7 --model resnet18 --gpu 0
```
4. Web Camera Demo (MacBook)
```shell
python run_webcam.py --model squeezenet --inp_dim 224 --camera 0
```
## Contributors
MobilePose is developed and maintained by [Yuliang Xiu](http://xiuyuliang.cn/about/), [Zexin Chen](https://github.com/ZexinChen) and [Yinghong Fang](https://github.com/Fangyh09). Thanks for [Siyuan Pan](https://github.com/pansiyuan123)'s implementation of [mnn version](https://market.mnn.zone/s/#/modelmarket/detail/107).
## License
MobilePose is freely available for free non-commercial use. For commercial queries, please contact [Cewu Lu](http://www.mvig.org/).