Open katahiyu opened 1 year ago
I have the same problem. When I retrained the model, I couldn't reproduce the results of the paper. Also, the accuracy is very low! ! !
I think this is due to the skeleton data preparation issue (the MS-G3D's preparation can be different from those of 2s-AGCN and ST-GCN). For details, please refer to Issue 5.
I think this is due to the skeleton data preparation issue (the MS-G3D's preparation can be different from those of 2s-AGCN and ST-GCN). For details, please refer to Issue 5.
Thanks for your reply, I downloaded the MS-G3D dataset you provided, it is my train_rgb_fused.yaml configuration
# python main_rgb_fused.py recognition -c config/ntu60_xsub/train_rgb_fused.yaml
work_dir: /data/MMNet/work_dir/st-gcn/xsub/rgb_tp15_tf5_
skeleton_joints_pkl: /data/multi_model_data/results/ntu60/xsub/joint_result_msg3d.pkl
skeleton_bones_pkl: /data/multi_model_data/results/ntu60/xsub/bone_result_msg3d.pkl
# feeder
feeder: feeder.feeder_rgb_fused_ntu.Feeder
train_feeder_args:
debug: False
random_choose: False
centralization: False
random_move: False
window_size: -1
random_flip: False
random_interval: True
temporal_rgb_frames: 5
data_path: /data/multi_model_data/data/ntu/xsub/train_data_joint.npy
label_path: /data/multi_model_data/data/ntu/xsub/train_label.pkl
test_feeder_args:
debug: False
centralization: False
evaluation: True
temporal_rgb_frames: 5
data_path: /data/multi_model_data/data/ntu/xsub/val_data_joint.npy
label_path: /data/multi_model_data/data/ntu/xsub/val_label.pkl
# model
model: net.mmn.Model
model_args:
in_channels: 3
num_class: 60
dropout: 0.5
edge_importance_weighting: True
graph_args:
layout: 'ntu-rgb+d'
strategy: 'spatial'
# training
temporal_positions: 15
fix_weights: True
joint_weights: /data/MMNet/work_dir/st-gcn/xsub/joint/epoch60_model.pt
device: [1,3]
weight_decay: 0.0001
base_lr: 0.01
step: [10, 50]
batch_size: 32
test_batch_size: 32
num_epoch: 80
# debug
debug: False
If convenient, please provide the corresponding yaml file. I would appreciate your prompt reply.
Sorry for the multiple questions. I am now retraining the RGB video. I run the following command.
python main_rgb_fused.py recognition -c config/ntu60_xsub/train_rgb_fused.yaml [--work_dir <work folder>]
However, the accuracy at epoch 5 is low as shown in the image below. I would like to know if you know the cause of this problem.Here is my train_rgb_fused.yaml file.