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

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.

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

FAISS:

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.

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