Longqi-S / keras_cpn

Cascaded Pyramid Network (CPN) based on keras (tensorflow backend)

Cascaded Pyramid Network (CPN) based on keras (tensorflow backend)

Results on COCO minival dataset (Single Model)

Note that our testing code is based on detection results from original tf-cpn (In COCO validation, detector AP is 41.1 whose human AP is 55.3).

Method	Base Model	Input Size	AP @0.5:0.95	AP @0.5	AP @0.75	AP medium	AP large
CPN	ResNet-50	384x288	71.1	88.9	77.7	67.2	78.0

We trained CPN model based on 4 GTX1080ti (11G) using 2days.

You can download our model here: https://github.com/Longqi-S/keras_cpn/releases/download/v0.1/cpn_resnet50_cpn_0065.h5

Prepare

1. Download MSCOCO images from http://cocodataset.org/#download. We train in COCO trainvalminusminival dataset and validate in minival dataset. Then put the data and evaluation PythonAPI in $CPN_ROOT/data/COCO/MSCOCO.

   We use the human detection results same with tf-cpn, you can download it here: https://github.com/Longqi-S/keras_cpn/releases/download/v0.1/person_detection_minival411_human553.json.coco

   After preparation, file stucture should be like below:

data/
       |->COCO/
       |    |->dets/
       |    |    |->person_detection_minival411_human553.json.coco
       |    |->MSCOCO/
       |    |    |->PythonAPI/
       |    |    |->train2014/
       |    |    |->val2014/
       |    |    |->person_keypoints_minival2014.json
       |    |    |->person_keypoints_trainvalminusminival2014.json

2. Download the base model (ResNet) weights from [keras model_zoo]

cd $CPN_ROOT/data
sh get_pretrain_model.sh

3. Setup your environment by first running

pip3 install -r requirement.txt

Train

To train a CPN model, use train.py in root folder.

python3 train.py --model data/pretrain/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5 --cfg configs/e2e_CPN_ResNet50_FPN_cfg.py

After the training finished, output is written underneath $CPN_ROOT/logs/ which looks like below

logs/
       |->resnet50_cpn20180819T1607/
       |    |->events.out.tfevents.1534666165.9507
       |    |->cpn_resnet50_cpn_0001.h5
       |    |->cpn_resnet50_cpn_0002.h5
       |    |->...

Test

Run the testing code in the postprocessing folder.

cd postprocessing
python3 mptest.py -d 0 -m cpn_resnet50_cpn_0002.h5 -c ../configs/e2e_CPN_ResNet50_FPN_cfg.py

notice

We can only use just one GPU to test.

How to draw network architecture

Go to lib/utils/

python3 draw_net.py --mode 0 --cfg configs/e2e_CPN_ResNet50_FPN_cfg.py

mode: 0 means train; 1 means inference; cfg : choose which network to draw;