PyTorch CPN(Cascaded Pyramid Network)

This is a PyTorch re-implementation of CPN (Cascaded Pyramid Network), winner of MSCOCO keypoints2017 challenge. The TensorFlow version can be found here, which is implemented by the paper author.

Evaluation results on COCO minival dataset

Method	Base Model	Input Size	BBox	AP @0.5:0.95	AP @0.5	AP @0.75	AP medium	AP large
CPN	ResNet-50	256x192	Ground Truth	71.2	91.4	78.3	68.6	75.2
CPN	ResNet-50	256x192	Detection Result	69.2	88.0	76.2	65.8	75.6
CPN	ResNet-50	384x288	Ground Truth	74.1	92.5	80.6	70.6	79.5
CPN	ResNet-50	384x288	Detection Result	72.2	89.2	78.6	68.1	79.3
CPN	ResNet-101*	384x288	Ground Truth	74.0	92.3	80.6	71.1	78.7
CPN	ResNet-101*	384x288	Detection Result	72.3	89.2	78.9	68.7	79.1

Thanks Tiamo666 and mingloo for training and testing ResNet-50-384x288CPN model. And thanks Tiamo666 for training and testing ResNet-101-384x288CPN model.
If you have interests in this repo, welcome to test other model configurations together.

* CPN-ResNet-101-384x288 model is fine-tuned from the previous pre-trained model. If you train it from scratch, it should get a higher result.

Usage

For training

1. Clone the repository

git clone https://github.com/GengDavid/pytorch-cpn

We'll call the directory that you cloned ROOT_DIR.

2. Download MSCOCO2017 images and annotations from http://cocodataset.org/#download. And put images and annotation files follow the struture showed in data/README.md
   After placing data and annotation files. Please run label_transform.py at ROOT_DIR to transform the annotation fomat.

3. Initialize cocoapi

git submodule init
git submodule update
cd cocoapi/PythonAPI
make

It will build cocoapi tools automatically.

4. Install requirement
   This repo require following dependences.

• PyTorch >= 0.4.1
• numpy >= 1.7.1
• scipy >= 0.13.2
• python-opencv >= 3.3.1
• tqdm > 4.11.1
• skimage >= 0.13.1

5. Training

cd ROOT_DIR/MODEL_DIR/
python3 train.py

For example, to train CPN with input resolution 256x192, just change directory into ROOT_DIR/256.192.model, and run the script.

For more args, see by using

python train.py --help

For Validation

cd ROOT_DIR/MODEL_DIR/
python3 test.py -t PRE-TRAINED_MODEL_NAME

-t meas use which pre-trained model to test.
For more args, see by using

python test.py --help

If you want to test a pre-trained model, please place the pre-trained model into ROOT_DIR/MODEL_DIR/checkpoint directory. Please make sure your have put the corresponding model into the folder.

For example, to run pre-trained CPN model with input resolution 256x192,

python3 test.py -t 'CPN256x192'

This pre-trained model is provided below.

Pre-trained models:

COCO.res50.256x192.CPN (updated!)
COCO.res50.384x288.CPN (updated!)
COCO.res101.384x288.CPN^* (new)
* CPN-ResNet-101-384x288 model is fine-tuned from the previous pre-trained model. If you train it from scratch, it should get a higher result.

Detection results on Minival dataset

The detection results are tranformed from results in tf version of cpn.
detection_minival

Acknowledgements

Thanks chenyilun95, bearpaw and last-one for sharing their codes, which helps me a lot to build this repo.
Thanks Tiamo666 for testing ResNet-50-384x288CPN and ResNet-101-384x288CPNmodel.
Thanks mingloo for contribution.
Thanks mkocabas for helping me test other configurations.

Others

If you have any questions or find some mistakes about this re-implementation, please open an issue to let me know.
If you want to know more details about the original implementation, you can check tf version of cpn.

Troubleshooting

1. Thanks Tiamo666 to point it out that the refineNet is implemented in a different way from the original paper(this can reach a higher results, but it will cost more memory).
2. See issue #10 and issue #7.
   Codes and results have been updated!(2018/9/6)

Reference

[1] Chen, Y., Wang, Z., Peng, Y., Zhang, Z., Yu, G., Sun, J.: Cascaded pyramid network for multi-person pose estimation. CVPR (2018)