This repository is a PyTorch implementation of hotmap classification.
An Anaconda environment is needed. We first create a virtual env by
conda create -n cls python=3.9 --yes
and then activate it by
conda activate cls
After that, a torch==1.11.0
is suggested to be installed first.
conda install pytorch torchvision torchaudio cudatoolkit=11.3 -c pytorch --yes
and other requirements are listed in requirements.txt
, which can be installed by a simple script
pip install -r requirements.txt
In this project, we used wandb
to log information during training process. We install wandb
by
pip install wandb
After the installation, we need to login to wandb
using
wandb login
The training data is not available for public. However, you can still have your own data in .mat
, stored in ./data
folder. Don't forget to check ./utils/dataset.py
to ensure there's no problem in loading the .mat
file.
We also used tensorboard
to record the loss and accuracy during training. The logs are stored in ./log
.
We save checkppoints every 5 epochs in ./ckpt
.