TWIST: Self-Supervised Learning by Estimating Twin Class Distributions

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 trivial solutions without specific designs such as asymmetric network, stop-gradient operation, or momentum encoder

Updates

• 12/4/2022: The performances of ViT-S (DeiT-S) and ViT-B are improved (+0.7 and +1.1 respectively), which is achieved by changing hyper-parameters (reduce the batch-size from 2048 to 1024, and change the drop path rate from 0.0 to 0.1 for ViT-B).

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	76.3%	backbone only	full ckpt	args	log	eval logs
ViT-B	86M	300	78.4%	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

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 64 \
  --bunch-size 256 \
  --clip_norm 3.0 \
  --epochs 300 \
  --mme_epochs 300 \
  --lam1 -0.6 \
  --lam2 1.0 \
  --act gelu \
  --local_crops_number 6 \
  --lr 0.0003 \
  --momentum_start 0.996 \
  --momentum_end 1.0 \
  --optim admw \
  --use_momentum_encoder 1 \
  --weight_decay 0.06 \
  --weight_decay_end 0.12 

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 32 \
  --bunch-size 256 \
  --clip_norm 3.0 \
  --epochs 300 \
  --mme_epochs 300 \
  --lam1 -0.6 \
  --lam2 1.0 \
  --act gelu \
  --local_crops_number 10 \
  --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 \
  --drop_path 0.1

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}