- torch
- torchvision
- tqdm
- einops
- wandb
- pytorch-lightning
- lightning-bolts
- torchmetrics
- scipy
- timm
Optional:
- nvidia-dali
- matplotlib
- seaborn
- pandas
- umap-learn
First clone the repo.
Then, to install solo-learn with Dali and/or UMAP support, use:
pip3 install .[dali,umap,h5] --extra-index-url https://developer.download.nvidia.com/compute/redist
If no Dali/UMAP/H5 support is needed, the repository can be installed as:
pip3 install .
For local development:
pip3 install -e .[umap,h5]
# Make sure you have pre-commit hooks installed
pre-commit install
NOTE: if you are having trouble with dali, install it following their guide.
NOTE 2: consider installing Pillow-SIMD for better loading times when not using Dali.
NOTE 3: Soon to be on pip.
For pretraining the backbone, follow one of the many bash files in scripts/pretrain/.
After that, for offline linear evaluation, follow the examples in scripts/linear.
NOTE: Files try to be up-to-date and follow as closely as possible the recommended parameters of each paper, but check them before running.