🔔🔔🔔 This project is shutdown owing to a very bad coding architecture, the new version of this project will be release soon.
Create a virtual environment and upgrade the version of pip
python3 -m venv .env
source .env/bin/activate
python -m pip install -U pip
Install the required packages
cd /path/to/cloned/repo/directory
pip install -r requirements.txt
Install Pytorch packages, the version of the Nvidia driver is 535 and the CUDA version is 12.1 on a Ubuntu-based system
pip3 install torch torchvision torchaudio
- dataset
- [ds].py where
dsis any dataset name - getds.py used as a mapping from args to a dataset
- [ds].py where
- loss
- [loss].py where
lossis any loss name
- [loss].py where
- metrics
- [metric].py where
metricis any metric name
- [metric].py where
- model
- [model_name]
- *.py
- [model_name]
- utils
- main.py
- mapping.py
- requirements.txt
- trainer.py
- Data sources and related annotations must be put into
dataset/sourcefolder, which is an ignored folder. A custom dataset class inheritingtorch.utils.data.Datasetof Pytorch and read all data fromdataset/source. ⚠️ ⚠️ : Do not change the content of any file insidedataset/, just create a new.pyfile to contribute your custom dataset.
- Create a new folder/new file
.pyto store your model architecture/loss function and its sub-components ⚠️ ⚠️ : Do not change the content of any file insidemodel/andloss/
- For collaborators, create a new branch with the name as follows:
<task>-<name>(i.e. model_deeplabv3) and then pull a request to merge to branchmain. - For outer collaborators, fork this repo, and then pull a request also.
- For review the pull request, you can tag the owner of this repo, the owner will merge and complete the final task to emerge your contribution thoroughly.