docs/action.md

Skeleton-based Action Recognition

Data

The NTURGB+D 2D detection results are provided by pyskl using HRNet.

1. Download ntu60_hrnet.pkl and ntu120_hrnet.pkl to data/action/.
2. Download the 1-shot split here and put it to data/action/.

Running

NTURGB+D

Train from scratch:

# Cross-subject
python train_action.py \
--config configs/action/MB_train_NTU60_xsub.yaml \
--checkpoint checkpoint/action/MB_train_NTU60_xsub

# Cross-view
python train_action.py \
--config configs/action/MB_train_NTU60_xview.yaml \
--checkpoint checkpoint/action/MB_train_NTU60_xview

Finetune from pretrained MotionBERT:

# Cross-subject
python train_action.py \
--config configs/action/MB_ft_NTU60_xsub.yaml \
--pretrained checkpoint/pretrain/MB_release \
--checkpoint checkpoint/action/FT_MB_release_MB_ft_NTU60_xsub

# Cross-view
python train_action.py \
--config configs/action/MB_ft_NTU60_xview.yaml \
--pretrained checkpoint/pretrain/MB_release \
--checkpoint checkpoint/action/FT_MB_release_MB_ft_NTU60_xview

Evaluate:

# Cross-subject
python train_action.py \
--config configs/action/MB_train_NTU60_xsub.yaml \
--evaluate checkpoint/action/MB_train_NTU60_xsub/best_epoch.bin 

# Cross-view
python train_action.py \
--config configs/action/MB_train_NTU60_xview.yaml \
--evaluate checkpoint/action/MB_train_NTU60_xview/best_epoch.bin 

NTURGB+D-120 (1-shot)

Train from scratch:

python train_action_1shot.py \
--config configs/action/MB_train_NTU120_oneshot.yaml \
--checkpoint checkpoint/action/MB_train_NTU120_oneshot

Finetune from a pretrained model:

python train_action_1shot.py \
--config configs/action/MB_ft_NTU120_oneshot.yaml \
--pretrained checkpoint/pretrain/MB_release \
--checkpoint checkpoint/action/FT_MB_release_MB_ft_NTU120_oneshot

Evaluate:

python train_action_1shot.py \
--config configs/action/MB_train_NTU120_oneshot.yaml \
--evaluate checkpoint/action/MB_train_NTU120_oneshot/best_epoch.bin