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configs | 1 year ago | |
docs | 1 year ago | |
mindpose | 1 year ago | |
tests/models | 1 year ago | |
tools | 1 year ago | |
.gitignore | 1 year ago | |
CONTRIBUTING.md | 1 year ago | |
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requirements.txt | 1 year ago | |
setup.py | 1 year ago |
Introduction |
Installation |
Get Started |
Tutorials |
Model List |
Supported Algorithms |
Notes
MindPose is an open-source toolbox for pose estimation based on MindSpore. It collects a series of classic and SoTA vision models, such as HRNet, along with their pre-trained weights and training strategies.
>>> import mindpose
# create a model
>>> network = mindpose.create_network(backbone_name='resnet50', head_name="simple_baseline_head")
State-of-The-Art. MindPose provides various CNN-based and Transformer-based vision models. Their pretrained weights and performance reports are provided to help users select and reuse the right model.
Flexibility and efficiency. MindPose is built on MindSpore which is an efficent DL framework that can be run on different hardware platforms (GPU/CPU/Ascend). It supports both graph mode for high efficiency and pynative mode for flexibility.
To install the dependency, please run
pip install -r requirements.txt
MindSpore can be easily installed by following the official instructions where you can select your hardware platform for the best fit. To run in distributed mode, openmpi is required to install.
The following instructions assume the desired dependency is fulfilled.
The released version of MindPose can be installed via PyPI
as follows:
pip install mindpose
The latest version of MindPose can be installed as follows:
pip install git+https://github.com/mindspore-lab/mindpose.git
Notes: MindPose can be installed on Linux and Mac but not on Windows currently.
TODO
It is easy to train your model on a standard or customized dataset using tools/train.py
, where the training strategy is configured with a yaml config file.
You can configure your model and other components by writing a yaml config file. Here is an example of training using a preset yaml file.
mpirun --allow-run-as-root -n 4 python tools/train.py --config configs/hrnet/hrnet_w32_ascend.yaml
To run training on the OpenI cloud platform:
tools/train_on_openi.py
as the starting file.config
and specify the path to the yaml config file on the website UI interface.To evalute the model performance, please run tools/eval.py
# validate a trained checkpoint
python tools/eval.py --config=configs/hrnet/hrnet_w32_ascend.yaml --ckpt=/path/to/model.ckpt
TODO
Currently, MindPose supports the model families listed below. More models with pre-trained weights are under development and will be released soon.
Please see configs for the details about model performance and pretrained weights.
TODO
We appreciate all kind of contributions including issues and PRs to make MindPose better.
Please refer to CONTRIBUTING.md for the contributing guideline. Please follow the Model Template and Guideline for contributing a model that fits the overall interface :)
This project follows the Apache License 2.0 open-source license.
MindPose is an open-source project jointly developed by the MindSpore team.
Sincere thanks to all participating researchers and developers for their hard work on this project.
We also acknowledge the computing resources provided by OpenI.
If you find this project useful in your research, please consider citing:
@misc{MindSpore Pose 2022,
title={{MindSpore Pose}:MindSpore Pose Toolbox and Benchmark},
author={MindSpore Vision Contributors},
howpublished = {\url{https://github.com/mindspore-lab/mindpose/}},
year={2022}
}
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