Official Code for "Constrained Mean Shift Using Distant Yet Related Neighbors for Representation Learning"

Last update: Nov 25, 2022

Related tags

Deep Learning CMSF

Overview

CMSF

Official Code for "Constrained Mean Shift Using Distant Yet Related Neighbors for Representation Learning"

Requirements

Python >= 3.7.6
PyTorch >= 1.4
torchvision >= 0.5.0
faiss-gpu >= 1.6.1

Install PyTorch and ImageNet dataset following the official PyTorch ImageNet training code. We used Python 3.7 for our experiments.

Install PyTorch (pytorch.org)

To run NN and CMSF-KM, you require to install FAISS.

FAISS:

Install FAISS (https://github.com/facebookresearch/faiss/blob/master/INSTALL.md)

Training Self-Supservised CMSF-KM

python self_supervised/train_msf_km.py \
  --cos \
  --weak_strong \
  --learning_rate 0.05 \
  --epochs 200 \
  --arch resnet50 \
  --topk 5 \
  --momentum 0.99 \
  --mem_bank_size 128000 \
  --num_clusters 50000 \
  --checkpoint_path <CHECKPOINT PATH> \
  <DATASET PATH>

Training Self-Supservised CMSF-2Q

python self_supervised/train_msf_2q.py \
  --cos \
  --weak_strong \
  --learning_rate 0.05 \
  --epochs 200 \
  --arch resnet50 \
  --topk 5 \
  --momentum 0.99 \
  --mem_bank_size 128000 \
  --topkp 5 \
  --checkpoint_path <CHECKPOINT PATH> \
  <DATASET PATH>

Training Supservised

Following command can be used to train the CMSF(Supervised Learning)

python supervised/train_sup_msf.py \
  --cos \
  --weak_strong \
  --learning_rate 0.05 \
  --epochs 200 \
  --arch resnet50 \
  --topk 10 \
  --momentum 0.99 \
  --mem_bank_size 128000 \
  --checkpoint_path <CHECKPOINT PATH> \
  <DATASET PATH>

License

This project is under the MIT license.

Official Code for "Constrained Mean Shift Using Distant Yet Related Neighbors for Representation Learning"

Related tags

Overview

CMSF

Requirements

Training Self-Supservised CMSF-KM

Training Self-Supservised CMSF-2Q

Training Supservised

License

Owner

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