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main.py
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readme.md

Skeleton Based Sign Language Recognition

Data preparation

  1. Extract whole-body keypoints data following the instruction in ../data_process/wholepose

  2. Run the following code to prepare the data for GCN.

     cd data_gen/
 python sign_gendata.py
 python gen_bone_data.py
 python gen_motion.py

Usage

Train:

python main.py --config config/sign/train/train_joint.yaml

python main.py --config config/sign/train/train_bone.yaml

python main.py --config config/sign/train/train_joint_motion.yaml

python main.py --config config/sign/train/train_bone_motion.yaml

Finetune:

python main.py --config config/sign/finetune/train_joint.yaml

python main.py --config config/sign/finetune/train_bone.yaml

python main.py --config config/sign/finetune/train_joint_motion.yaml

python main.py --config config/sign/finetune/train_bone_motion.yaml

Test:

python main.py --config config/sign/test/test_joint.yaml

python main.py --config config/sign/test/test_bone.yaml

python main.py --config config/sign/test/test_joint_motion.yaml

python main.py --config config/sign/test/test_bone_motion.yaml

Test Finetuned:

python main.py --config config/sign/test_finetuned/test_joint.yaml

python main.py --config config/sign/test_finetuned/test_bone.yaml

python main.py --config config/sign/test_finetuned/test_joint_motion.yaml

python main.py --config config/sign/test_finetuned/test_bone_motion.yaml

Multi-stream ensemble:

  1. Copy the results .pkl files from all streams (joint, bone, joint motion and bone motion) to ../ensemble/gcn and renamed them correctly.
  2. Follow the instruction in ../ensemble/gcn to obtained the results of multi-stream ensemble.