Official codes: Self-Supervised Learning by Estimating Twin Class Distribution

Related tags

Deep LearningTWIST
Overview

TWIST: Self-Supervised Learning by Estimating Twin Class Distributions

Architecture

Codes and pretrained models for TWIST:

@article{wang2021self,
  title={Self-Supervised Learning by Estimating Twin Class Distributions},
  author={Wang, Feng and Kong, Tao and Zhang, Rufeng and Liu, Huaping and Li, Hang},
  journal={arXiv preprint arXiv:2110.07402},
  year={2021}
}

TWIST is a novel self-supervised representation learning method by classifying large-scale unlabeled datasets in an end-to-end way. We employ a siamese network terminated by a softmax operation to produce twin class distributions of two augmented images. Without supervision, we enforce the class distributions of different augmentations to be consistent. In the meantime, we regularize the class distributions to make them sharp and diverse. TWIST can naturally avoid the trivial solutions without specific designs such as asymmetric network, stop-gradient operation, or momentum encoder.

formula

Models and Results

Main Models for Representation Learning

arch params epochs linear download
Model with multi-crop and self-labeling
ResNet-50 24M 850 75.5% backbone only full ckpt args log eval logs
ResNet-50w2 94M 250 77.7% backbone only full ckpt args log eval logs
DeiT-S 21M 300 75.6% backbone only full ckpt args log eval logs
ViT-B 86M 300 77.3% backbone only full ckpt args log eval logs
Model without multi-crop and self-labeling
ResNet-50 24M 800 72.6% backbone only full ckpt args log eval logs

Model for unsupervised classification

arch params epochs NMI AMI ARI ACC download
ResNet-50 24M 800 74.4 57.7 30.1 40.5 backbone only full ckpt args log
Top-3 predictions for unsupervised classification

Top-3

Semi-Supervised Results

arch 1% labels 10% labels 100% labels
resnet-50 61.5% 71.7% 78.4%
resnet-50w2 67.2% 75.3% 80.3%

Detection Results

Task AP all AP 50 AP 75
VOC07+12 detection 58.1 84.2 65.4
COCO detection 41.9 62.6 45.7
COCO instance segmentation 37.9 59.7 40.6

Single-node Training

ResNet-50 (requires 8 GPUs, Top-1 Linear 72.6%)

python3 -m torch.distributed.launch --nproc_per_node=8 --use_env train.py \
  --data-path ${DATAPATH} \
  --output_dir ${OUTPUT} \
  --aug barlow \
  --batch-size 256 \
  --dim 32768 \
  --epochs 800

Multi-node Training

ResNet-50 (requires 16 GPUs spliting over 2 nodes for multi-crop training, Top-1 Linear 75.5%)

python3 -m torch.distributed.launch --nproc_per_node=8 --use_env \
  --nnodes=${WORKER_NUM} \
  --node_rank=${MACHINE_ID} \
  --master_addr=${HOST} \
  --master_port=${PORT} train.py \
  --data-path ${DATAPATH} \
  --output_dir ${OUTPUT}

ResNet-50w2 (requires 32 GPUs spliting over 4 nodes for multi-crop training, Top-1 Linear 77.7%)

python3 -m torch.distributed.launch --nproc_per_node=8 --use_env \
  --nnodes=${WORKER_NUM} \
  --node_rank=${MACHINE_ID} \
  --master_addr=${HOST} \
  --master_port=${PORT} train.py \
  --data-path ${DATAPATH} \
  --output_dir ${OUTPUT} \
  --backbone 'resnet50w2' \
  --batch-size 60 \
  --bunch-size 240 \
  --epochs 250 \
  --mme_epochs 200

DeiT-S (requires 16 GPUs spliting over 2 nodes for multi-crop training, Top-1 Linear 75.6%)

python3 -m torch.distributed.launch --nproc_per_node=8 --use_env \
  --nnodes=${WORKER_NUM} \
  --node_rank=${MACHINE_ID} \
  --master_addr=${HOST} \
  --master_port=${PORT} train.py \
  --data-path ${DATAPATH} \
  --output_dir ${OUTPUT} \
  --backbone 'vit_s' \
  --batch-size 128 \
  --bunch-size 256 \
  --clip_norm 3.0 \
  --epochs 300 \
  --mme_epochs 300 \
  --lam1 -0.6 \
  --lam2 1.0 \
  --local_crops_number 6 \
  --lr 0.0005 \
  --momentum_start 0.996 \
  --momentum_end 1.0 \
  --optim admw \
  --use_momentum_encoder 1 \
  --weight_decay 0.06 \
  --weight_decay_end 0.06

ViT-B (requires 32 GPUs spliting over 4 nodes for multi-crop training, Top-1 Linear 77.3%)

python3 -m torch.distributed.launch --nproc_per_node=8 --use_env \
  --nnodes=${WORKER_NUM} \
  --node_rank=${MACHINE_ID} \
  --master_addr=${HOST} \
  --master_port=${PORT} train.py \
  --data-path ${DATAPATH} \
  --output_dir ${OUTPUT} \
  --backbone 'vit_b' \
  --batch-size 64 \
  --bunch-size 256 \
  --clip_norm 3.0 \
  --epochs 300 \
  --mme_epochs 300 \
  --lam1 -0.6 \
  --lam2 1.0 \
  --local_crops_number 6 \
  --lr 0.00075 \
  --momentum_start 0.996 \
  --momentum_end 1.0 \
  --optim admw \
  --use_momentum_encoder 1 \
  --weight_decay 0.06 \
  --weight_decay_end 0.06

Linear Classification

For ResNet-50

python3 evaluate.py \
  ${DATAPATH} \
  ${OUTPUT}/checkpoint.pth \
  --weight-decay 0 \
  --checkpoint-dir ${OUTPUT}/linear_multihead/ \
  --batch-size 1024 \
  --val_epoch 1 \
  --lr-classifier 0.2

For DeiT-S

python3 -m torch.distributed.launch --nproc_per_node=8 evaluate_vitlinear.py \
  --arch vit_s \
  --pretrained_weights ${OUTPUT}/checkpoint.pth \
  --lr 0.02 \
  --data_path ${DATAPATH} \
  --output_dir ${OUTPUT} \

For ViT-B

python3 -m torch.distributed.launch --nproc_per_node=8 evaluate_vitlinear.py \
  --arch vit_b \
  --pretrained_weights ${OUTPUT}/checkpoint.pth \
  --lr 0.0015 \
  --data_path ${DATAPATH} \
  --output_dir ${OUTPUT} \

Semi-supervised Learning

Command for training semi-supervised classification

1% Percent (61.5%)

python3 evaluate.py ${DATAPATH} ${MODELPATH} \
  --weights finetune \
  --lr-backbone 0.04 \
  --lr-classifier 0.2 \
  --train-percent 1 \
  --weight-decay 0 \
  --epochs 20 \
  --backbone 'resnet50'

10% Percent (71.7%)

python3 evaluate.py ${DATAPATH} ${MODELPATH} \
  --weights finetune \
  --lr-backbone 0.02 \
  --lr-classifier 0.2 \
  --train-percent 10 \
  --weight-decay 0 \
  --epochs 20 \
  --backbone 'resnet50'

100% Percent (78.4%)

python3 evaluate.py ${DATAPATH} ${MODELPATH} \
  --weights finetune \
  --lr-backbone 0.01 \
  --lr-classifier 0.2 \
  --train-percent 100 \
  --weight-decay 0 \
  --epochs 30 \
  --backbone 'resnet50'

Detection

Instruction

  1. Install detectron2.

  2. Convert a pre-trained MoCo model to detectron2's format:

    python3 detection/convert-pretrain-to-detectron2.py ${MODELPATH} ${OUTPUTPKLPATH}

  3. Put dataset under "detection/datasets" directory, following the directory structure requried by detectron2.

  4. Training: VOC

    cd detection/
python3 train_net.py \
  --config-file voc_fpn_1fc/pascal_voc_R_50_FPN_24k_infomin.yaml \
  --num-gpus 8 \
  MODEL.WEIGHTS ../${OUTPUTPKLPATH}

    COCO

    python3 train_net.py \
  --config-file infomin_configs/R_50_FPN_1x_infomin.yaml \
  --num-gpus 8 \
  MODEL.WEIGHTS ../${OUTPUTPKLPATH}
Owner
Bytedance Inc.
Bytedance Inc.
GitHub Repository
Generates all variables from your .tf files into a variables.tf file.

tfvg Generates all variables from your .tf files into a variables.tf file. It searches for every var.variable_name in your .tf files and generates a v

1 Dec 01, 2022
Repository for reproducing `Model-Based Robust Deep Learning`

Model-Based Robust Deep Learning (MBRDL) In this repository, we include the code necessary for reproducing the code used in Model-Based Robust Deep Le

Alex Robey 16 Sep 19, 2022
Label-Free Model Evaluation with Semi-Structured Dataset Representations

Label-Free Model Evaluation with Semi-Structured Dataset Representations Prerequisites This code uses the following libraries Python 3.7 NumPy PyTorch

8 Oct 06, 2022
Learning hidden low dimensional dyanmics using a Generalized Onsager Principle and neural networks

OnsagerNet Learning hidden low dimensional dyanmics using a Generalized Onsager Principle and neural networks This is the original pyTorch implemenati

Haijun.Yu 3 Aug 24, 2022
OpenCV, MediaPipe Pose Estimation, Affine Transform for Icon Overlay

Yoga Pose Identification and Icon Matching Project Goal Detect yoga poses performed by a user and overlay a corresponding icon image. Running the main

Anna Garverick 1 Dec 03, 2021
Minimal deep learning library written from scratch in Python, using NumPy/CuPy.

SmallPebble Project status: experimental, unstable. SmallPebble is a minimal/toy automatic differentiation/deep learning library written from scratch

Sidney Radcliffe 92 Dec 30, 2022
Certifiable Outlier-Robust Geometric Perception

Certifiable Outlier-Robust Geometric Perception About This repository holds the implementation for certifiably solving outlier-robust geometric percep

83 Dec 31, 2022
Repository for "Improving evidential deep learning via multi-task learning," published in AAAI2022

Improving evidential deep learning via multi task learning It is a repository of AAAI2022 paper, “Improving evidential deep learning via multi-task le

deargen 11 Nov 19, 2022
Space robot - (Course Project) Using the space robot to capture the target satellite that is disabled and spinning, then stabilize and fix it up

Space robot - (Course Project) Using the space robot to capture the target satellite that is disabled and spinning, then stabilize and fix it up

Mingrui Yu 3 Jan 07, 2022
Unified MultiWOZ evaluation scripts for the context-to-response task.

MultiWOZ Context-to-Response Evaluation Standardized and easy to use Inform, Success, BLEU ~ See the paper ~ Easy-to-use scripts for standardized eval

Tomáš Nekvinda 38 Dec 13, 2022
A new version of the CIDACS-RL linkage tool suitable to a cluster computing environment.

Fully Distributed CIDACS-RL The CIDACS-RL is a brazillian record linkage tool suitable to integrate large amount of data with high accuracy. However,

Robespierre Pita 5 Nov 04, 2022
Photographic Image Synthesis with Cascaded Refinement Networks - Pytorch Implementation

Photographic Image Synthesis with Cascaded Refinement Networks-Pytorch (https://arxiv.org/abs/1707.09405) This is a Pytorch implementation of cascaded

Soumya Tripathy 63 Mar 27, 2022
Repository for code and dataset for our EMNLP 2021 paper - “So You Think You’re Funny?”: Rating the Humour Quotient in Standup Comedy.

AI-OpenMic Dataset The dataset is available for download via the follwing link. Repository for code and dataset for our EMNLP 2021 paper - “So You Thi

6 Oct 26, 2022
Source code for CVPR 2021 paper "Riggable 3D Face Reconstruction via In-Network Optimization"

Riggable 3D Face Reconstruction via In-Network Optimization Source code for CVPR 2021 paper "Riggable 3D Face Reconstruction via In-Network Optimizati

130 Jan 02, 2023
Implementation for On Provable Benefits of Depth in Training Graph Convolutional Networks

Implementation for On Provable Benefits of Depth in Training Graph Convolutional Networks Setup This implementation is based on PyTorch = 1.0.0. Smal

Weilin Cong 8 Oct 28, 2022
The implementation of 'Image synthesis via semantic composition'.

Image synthesis via semantic synthesis [Project Page] by Yi Wang, Lu Qi, Ying-Cong Chen, Xiangyu Zhang, Jiaya Jia. Introduction This repository gives

DV Lab 71 Jan 06, 2023
Pairwise learning neural link prediction for ogb link prediction

Pairwise Learning for Neural Link Prediction for OGB (PLNLP-OGB) This repository provides evaluation codes of PLNLP for OGB link property prediction t

Zhitao WANG 31 Oct 10, 2022
Supplementary code for the paper "Meta-Solver for Neural Ordinary Differential Equations" https://arxiv.org/abs/2103.08561

Meta-Solver for Neural Ordinary Differential Equations Towards robust neural ODEs using parametrized solvers. Main idea Each Runge-Kutta (RK) solver w

Julia Gusak 25 Aug 12, 2021
Visual odometry package based on hardware-accelerated NVIDIA Elbrus library with world class quality and performance.

Isaac ROS Visual Odometry This repository provides a ROS2 package that estimates stereo visual inertial odometry using the Isaac Elbrus GPU-accelerate

NVIDIA Isaac ROS 343 Jan 03, 2023
Logistic Bandit experiments. Official code for the paper "Jointly Efficient and Optimal Algorithms for Logistic Bandits".

Code for the paper Jointly Efficient and Optimal Algorithms for Logistic Bandits, by Louis Faury, Marc Abeille, Clément Calauzènes and Kwang-Sun Jun.

Faury Louis 1 Jan 22, 2022