selfsupervised

Code repository for double blind.

Installation

Requirements

• Python 3.6+
• PyTorch 1.6+
• mmcv 0.6.0+

To config environment, one can run:

conda create -n torch1.6 python=3.6.7
conda install pytorch==1.6.0 torchvision==0.7.0 cudatoolkit=10.1 -c pytorch
pip install mmcv==0.6.0

Notice that you should install a compatible version of PyTorch with your Cuda version (here we use cudatoolkit=10.1). Please refer to pytorch to find a detailed installation for PyTorch.

Getting Started

Before running a scipt, you would better run

ln -s ${DATASET_ROOT} dataset

to configure your data path. If your folder structure is different, you may need to change the corresponding paths in config files.

selfsupervised
├── configs
├── imagenet_label
├── modules
├── dataset
│   ├── imagenet
│   │   ├── train
│   │   ├── val
│   ├── cifar
│   │   ├── cifar-10-batches-py

We provide a task.sh and some configs to train models. Detailed information can be found in docs/task.md In sstrain.sh, one can run:

runModel ${configfile} ${logname}

For example, to run our model, one can replace the command in bin/sstrain.sh with

runModel interclass log1

The above command will use configs/imagenette/icc.py as the configuration, and output training logs and checkpoints in result/log1.