Awesome
ProFormer: Learning Data-efficient Representations of Body Movement with Prototype-based Feature Augmentation and Visual Transformers
This repository contains the code for ProFormer based on the code from SL-DML
Requirements
pip install -r requirements.txt
- install pytorch metric learning library from pip
Precalculated Representations
The precalculated representations can be downloaded from the following links:
Quick Start
pip install -r requirements.txt
export DATASET_FOLDER="$(pwd)/data"
mkdir -p data/ntu/
wget https://agas.uni-koblenz.de/datasets/sl-dml/ntu_120_one_shot.zip
unzip ntu_120_one_shot.zip -d $DATASET_FOLDER/ntu/ntu_swap_axes_testswapaxes
python train.py dataset=ntu_swap_axis
when returning you have to set the dataset folder again:
export DATASET_FOLDER="$(pwd)/data"
python train.py dataset=ntu_swap_axis
Training
Note, the following commands require an environment variable $DATASET_FOLDER
to be existing.
NTU 120 One-Shot
Training for the NTU 120 one-shot action recognition experiments can be executed like:
python train.py dataset=ntu_swap_axis
During development, we suggest using the classes A002, A008, A014, A020, A026, A032, A038, A044, A050, A056, A062, A068, A074, A080, A086, A092, A098, A104, A110, A116
as validation classes.