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

Related tags

Deep Learning TWIST
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}
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