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

Automatic library of congress classification, using word embeddings from book titles and synopses.

Half Instance Normalization Network for Image Restoration

dyld_shared_cache processing / Single-Image loading for BinaryNinja

Person Re-identification

Using deep actor-critic model to learn best strategies in pair trading

Facial Expression Detection In The Realtime

Julia package for multiway (inverse) covariance estimation.

A MNIST-like fashion product database. Benchmark

Reinforcement learning library in JAX.

On Evaluation Metrics for Graph Generative Models

An imperfect information game is a type of game with asymmetric information

PyTorch implementations of the beta divergence loss.

Live Hand Tracking Using Python

IA for recognising Traffic Signs using Keras [Tensorflow]

Data, model training, and evaluation code for "PubTables-1M: Towards a universal dataset and metrics for training and evaluating table extraction models".

A repository for the paper "Improved Adversarial Systems for 3D Object Generation and Reconstruction".

PyTorch implementation for our NeurIPS 2021 Spotlight paper "Long Short-Term Transformer for Online Action Detection".

Tutel MoE: An Optimized Mixture-of-Experts Implementation

Adversarial Texture Optimization from RGB-D Scans (CVPR 2020).

Adaptive Denoising Training (ADT) for Recommendation.