Self-Classifier: Self-Supervised Classification Network

Official PyTorch implementation and pretrained models of the paper Self-Supervised Classification Network. Self-Classifier is a self-supervised end-to-end classification neural network. It learns labels and representations simultaneously in a single-stage end-to-end manner.

Self-Classifier architecture. Two augmented views of the same image are processed by a shared network. The cross-entropy of the two views is minimized to promote same class prediction while avoiding degenerate solutions by asserting a uniform prior. The resulting model learns representations and class labels in a single-stage end-to-end unsupervised manner. CNN: Convolutional Neural Network; FC: Fully Connected.

Setup

1. Install Conda environment:

    conda env create -f ./environment.yml
   

2. Install Apex with CUDA extension:

    export TORCH_CUDA_ARCH_LIST="7.0"  # see https://en.wikipedia.org/wiki/CUDA#GPUs_supported
    pip install git+git://github.com/NVIDIA/[email protected] --install-option="--cuda_ext"         
   

Training & Evaluation

Distributed training & evaluation is available via Slurm. See SBATCH scripts here.

IMPORTANT: set DATASET_PATH, EXPERIMENT_PATH and PRETRAINED_PATH to match your local paths.

Training

For training self-classifier on 4 nodes of 4 GPUs each for 800 epochs run:

    sbatch ./scripts/train.sh

Evaluation

Image Classification with Linear Models

For training a supervised linear classifier on a frozen backbone, run:

    sbatch ./scripts/eval.sh

Unsupervised Image Classification

For computing unsupervised image classification metrics (NMI: Normalized Mutual Information, AMI: Adjusted Normalized Mutual Information and ARI: Adjusted Rand-Index) and generating qualitative examples, run:

    sbatch ./scripts/cls_eval.sh

Image Classification with kNN

For running K-nearest neighbor classifier on ImageNet validation set, run:

    sbatch ./scripts/knn_eval.sh

Ablation study

For training the 100-epoch ablation study baseline, run:

    sbatch ./scripts/ablation/train_100ep.sh

For training any of the ablation study runs presented in the paper, run:

    sbatch ./scripts/ablation//.sh

Pretrained Models

Download pretrained 100/800 epochs models here.

Qualitative Examples (classes predicted by Self-Classifier on ImageNet validation set)

Low entropy classes predicted by Self-Classifier on ImageNet validation set. Images are sampled randomly from each predicted class. Note that the predicted classes capture a large variety of different backgrounds and viewpoints.

To reproduce qualitative examples, run:

    sbatch ./scripts/cls_eval.sh

License

See the LICENSE file for more details.

Citation

If you find this repository useful in your research, please cite:

@article{amrani2021self,
  title={Self-Supervised Classification Network},
  author={Amrani, Elad and Bronstein, Alex},
  journal={arXiv preprint arXiv:2103.10994},
  year={2021}
}