LSTM_POSE_MACHINE_TENSORFLOW

Tensorflow implementation of LSTM Pose Machines

Prerequisites

• Python 3.5
• tensorflow-gpu 1.12
• scipy
• sklearn
• pillow
• pandas
• numpy

All the requiremenst can be installed by running:

pip install -r requirements.txt

Code based on tensorflow-gpu 1.12

set hyper-parameters in

train.py

T = 5                            # how many timestamps to look back into 
outclass = 21                    # number of joints to be tracked
learning_rate = 8e-6
batch_size = 4                   # batch size* temporal must be atleast total number of images in the dataset otherwise the batches will be reported as the same images in a cyclic manner
epochs = 101
begin_epoch = 0
save_dir = './ckpt/'                        # to save model
data_dir = './001L0/'                        # the train data dir
label_dir = './labels/001L0.json'           # the label dir

save_dir_val= './validation_info/'          # dir to save the validation info i.e.the csv
data_dir_val = './001L00/'                # dir to find the validation dataset
label_dir_val = './labels/001L0.json'       # dir to find the validation labels should point to a json file

To train and validate run:

python lstm_pm_train.py

For prediction run:

python lstm_pm_prediction.py

Dataset Credits

https://github.com/HowieMa/lstm_pm_pytorch

References

LSTM Pose Machines

lawy623/LSTM_Pose_Machines