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

Pytorch implementation of "Training a 85.4% Top-1 Accuracy Vision Transformer with 56M Parameters on ImageNet"

TSP: Temporally-Sensitive Pretraining of Video Encoders for Localization Tasks

Converts given image (png, jpg, etc) to amogus gif.

CR-FIQA: Face Image Quality Assessment by Learning Sample Relative Classifiability

Pytorch implementation of various High Dynamic Range (HDR) Imaging algorithms

Machine learning Bot detection technique, based on United States election dataset

《Single Image Reflection Removal Beyond Linearity》(CVPR 2019)

CKD - Collaborative Knowledge Distillation for Heterogeneous Information Network Embedding

Hierarchical probabilistic 3D U-Net, with attention mechanisms (—𝘈𝘵𝘵𝘦𝘯𝘵𝘪𝘰𝘯 𝘜-𝘕𝘦𝘵, 𝘚𝘌𝘙𝘦𝘴𝘕𝘦𝘵) and a nested decoder structure with deep supervision (—𝘜𝘕𝘦𝘵++).

A computational block to solve entity alignment over textual attributes in a knowledge graph creation pipeline.

Unleashing Transformers: Parallel Token Prediction with Discrete Absorbing Diffusion for Fast High-Resolution Image Generation from Vector-Quantized Codes

Spectrum Surveying: Active Radio Map Estimation with Autonomous UAVs

Provably Rare Gem Miner.

Python calculations for the position of the sun and moon.

[CVPR 2021] Involution: Inverting the Inherence of Convolution for Visual Recognition, a brand new neural operator

Yolo object detection - Yolo object detection with python

Pytorch codes for "Self-supervised Multi-view Stereo via Effective Co-Segmentation and Data-Augmentation"

C3DPO - Canonical 3D Pose Networks for Non-rigid Structure From Motion.

Revisiting Self-Training for Few-Shot Learning of Language Model.

利用python脚本实现微信、支付宝账单的合并，并保存到excel文件实现自动记账，可查看可视化图表。