Semester project
Student: Aleksandra Novikova, Data Science
Supervisor: Mohamed Ossama Ahmed Abdelfattah
-
Report.pdf
- project report -
TCL/
- implementation of the contrastive loss method with noisy data for training robust models. The main code is taken from the repository TCL. Models (backbones) are taken from repositories MotionBERT and pySKL. -
pyskl/
- implementation of different input types for aPoseConv3D
model. The main code is taken from the repository pySKL.
Details can be found in the respective repositories.
- PoseConv3D model training:
python main.py ntu60 RGB --seed 123 --input_f Skeleton --in_channels 68 --strategy classwise --arch resnet18 --num_segments 8 --second_segments 8 --threshold 0.8 --gd 20 --epochs 1000 --percentage 0.95 -j 2 --dropout 0.5 --consensus_type=avg --eval-freq=1 --print-freq 50 --shift --shift_div=8 --shift_place=blockres --npb --gpus 0 --mu 1 --gamma 5 --gamma2 0 --use_group_contrastive --sup_thresh 0 --batch-size 32 --valbatchsize 32 --lr_backbone1 0.001 --lr_decay1 0.9 --noise_alpha 0.1
- MotionBERT model training:
python main.py ntu60 RGB --seed 123 --input_f Skeleton --in_channels 68 --strategy classwise --arch resnet18 --num_segments 8 --second_segments 8 --threshold 0.8 --gd 20 --epochs 1000 --percentage 0.95 -j 2 --dropout 0.5 --consensus_type=avg --eval-freq=1 --print-freq 50 --shift --shift_div=8 --shift_place=blockres --npb --gpus 0 --mu 1 --gamma 5 --gamma2 0 --use_group_contrastive --sup_thresh 0 --batch-size 32 --valbatchsize 32 --lr_backbone1 0.001 --lr_decay1 0.9 --noise_alpha 0.1 --model_type motionbert
where
noise_alpha
- percentage of added noise to datagamma
- coefficient before instance contrastive lossgamma2
- coefficient before group contrastive loss
bash tools/dist_train.sh configs/posec3d/slowonly_r50_ntu60_xsub/joint.py 1 --validate --test-last --test-best
configs/posec3d/slowonly_r50_ntu60_xsub/joint.py
for3D Heatmaps
configs/posec3d/slowonly_r50_ntu60_xsub/joint_grayscale.py
forGrayscale
configs/posec3d/slowonly_r50_ntu60_xsub/joint_skeleton.py
forSkeleton