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

Simulation of moving particles under microscopic imaging

DECAF: Generating Fair Synthetic Data Using Causally-Aware Generative Networks

Created as part of CS50 AI's coursework. This AI makes use of knowledge entailment to calculate the best probabilities to win Minesweeper.

(ICCV 2021) ProHMR - Probabilistic Modeling for Human Mesh Recovery

PyTorch implementation of Progressive Growing of GANs for Improved Quality, Stability, and Variation.

Group Activity Recognition with Clustered Spatial Temporal Transformer

Source code for Transformer-based Multi-task Learning for Disaster Tweet Categorisation (UCD's participation in TREC-IS 2020A, 2020B and 2021A).

Unofficial reimplementation of ECAPA-TDNN for speaker recognition (EER=0.86 for Vox1_O when train only in Vox2)

Official Repo for Ground-aware Monocular 3D Object Detection for Autonomous Driving

SuMa++: Efficient LiDAR-based Semantic SLAM (Chen et al IROS 2019)

🎁 3,000,000+ Unsplash images made available for research and machine learning

Semi-supervised Semantic Segmentation with Directional Context-aware Consistency (CVPR 2021)

PyTorch implementation for NED. It can be used to manipulate the facial emotions of actors in videos based on emotion labels or reference styles.

Repositorio de los Laboratorios de Análisis Numérico / Análisis Numérico I de FAMAF, UNC.

pyspark🍒🥭 is delicious，just eat it!😋😋

Specification language for generating Generalized Linear Models (with or without mixed effects) from conceptual models

BMVC 2021: This is the github repository for "Few Shot Temporal Action Localization using Query Adaptive Transformers" accepted in British Machine Vision Conference (BMVC) 2021, Virtual

Jittor implementation of PCT:Point Cloud Transformer

[ICCV'21] PlaneTR: Structure-Guided Transformers for 3D Plane Recovery

This is a library for training and applying sparse fine-tunings with torch and transformers.