Boosting Contrastive Self-Supervised Learning with False Negative Cancellation

This repository contains pretrained models and code base for the paper "Boosting Contrastive Self-Supervised Learning with False Negative Cancellation".

Pre-trained models

	Semi-supervised (1%)	Semi-supervised (10%)	Linear eval	Supervised
Top-1	63.7	71.1	74.4	76.5
Top-5	85.3	90.2	91.8	93.3

Pretrained FNC model (with linear eval head): gs://fnc_checkpoints/ResNet-50/pretrained_linear_eval
Fine-tuned FNC model on 1% of labels: gs://fnc_checkpoints/ResNet-50/finetuned_semi1pt
Fine-tuned FNC models on 10% of labels: gs://fnc_checkpoints/ResNet-50/finetuned_semi10pt

Environment setup

Our models are trained with TPUs. It is recommended to run distributed training with TPUs when using our code for pretraining.

Install dependencies:

pip install -r requirements.txt

Add TPU official models to PYTHONPATH:

PYTHONPATH=/your_path/tpu/models/official:$PYTHONPATH

Pretraining

To pretrain the model on ImageNet with Cloud TPUs, first check out the Google Cloud TPU tutorial for basic information on how to use Google Cloud TPUs.

Once you have created virtual machine with Cloud TPUs, and pre-downloaded the ImageNet data for tensorflow_datasets, please set the following enviroment variables:

TPU_NAME=<tpu-name>
STORAGE_BUCKET=gs://<storage-bucket>
DATA_DIR=$STORAGE_BUCKET/<path-to-tensorflow-dataset>
MODEL_DIR=$STORAGE_BUCKET/<path-to-store-checkpoints>

The following command can be used to pretrain a ResNet-50 on ImageNet:

python run.py --train_mode=pretrain \
  --train_batch_size=4096 --train_epochs=100 --temperature=0.1 \
  --learning_rate=0.1 --learning_rate_scaling=sqrt --weight_decay=1e-4 \
  --dataset=imagenet2012 --image_size=224 --eval_split=validation \
  --data_dir=$DATA_DIR --model_dir=$MODEL_DIR \
  --use_tpu=True --tpu_name=$TPU_NAME --train_summary_steps=0

Finetuning the linear head (linear eval)

For fine-tuning a linear head on ImageNet using Cloud TPUs, first set the CHKPT_DIR to pretrained model dir and set a new MODEL_DIR, then use the following command:

python run.py --mode=train --train_mode=finetune \
  --fine_tune_after_block=4 --zero_init_logits_layer=True \
  --variable_schema='(?!global_step|(?:.*/|^)Momentum|head)' \
  --global_bn=True --optimizer=momentum --learning_rate=0.005 --weight_decay=0 \
  --train_epochs=90 --train_batch_size=1024 --warmup_epochs=0 \
  --dataset=imagenet2012 --image_size=224 --eval_split=validation \
  --data_dir=$DATA_DIR --model_dir=$MODEL_DIR --checkpoint=$CHKPT_DIR \
  --use_tpu=True --tpu_name=$TPU_NAME --train_summary_steps=0

Semi-supervised learning and fine-tuning the whole network

You can access 1% and 10% ImageNet subsets used for semi-supervised learning via tensorflow datasets: simply set dataset=imagenet2012_subset/1pct and dataset=imagenet2012_subset/10pct in the command line for fine-tuning on these subsets.

You can also find image IDs of these subsets in imagenet_subsets/.

To fine-tune the whole network on ImageNet (1% of labels), refer to the following command:

python run.py --mode=train --train_mode=finetune \
  --fine_tune_after_block=-1 --zero_init_logits_layer=True \
  --variable_schema='(?!global_step|(?:.*/|^)Momentum|head_supervised)' \
  --global_bn=True --optimizer=lars --learning_rate=0.005 \
  --learning_rate_scaling=sqrt --weight_decay=0 \
  --train_epochs=60 --train_batch_size=1024 --warmup_epochs=0 \
  --dataset=imagenet2012_subset/1pct --image_size=224 --eval_split=validation \
  --data_dir=$DATA_DIR --model_dir=$MODEL_DIR --checkpoint=$CHKPT_DIR \
  --use_tpu=True --tpu_name=$TPU_NAME --train_summary_steps=0 \
  --ft_proj_selector=1

Cite

FNC paper:

@article{huynh2022fnc,
  title={Boosting Contrastive Self-Supervised Learning with False Negative Cancellation},
  author={Huynh, Tri and Kornblith, Simon and Walter, Matthew R. and Maire, Michael and Khademi, Maryam},
  journal={Proceedings of the IEEE Winter Conference on Applications of Computer Vision (WACV)},
  year={2022}
}

Acknowledgement

The code base is developed based on SimCLR repository.

Disclaimer

This is not an official Google product.

google-research/fnc