The pre-trained models (base network with linear classifier layer) can be found below.
| Model checkpoint and hub-module | ImageNet Top-1 |
|---|---|
| ResNet50 (1x) | 69.1 |
| ResNet50 (2x) | 74.2 |
| ResNet50 (4x) | 76.6 |
A note on the signatures of the TensorFlow Hub module: default is the representation output of the base network; logits_sup is the supervised classification logits for ImageNet 1000 categories. Others (e.g. initial_max_pool, block_group1) are middle layers of ResNet; refer to resnet.py for the specifics. See this tutorial for additional information regarding use of TensorFlow Hub modules.
Our models are trained with TPUs. It is recommended to run distributed training with TPUs when using our code for pretraining.
Our code can also run on a single GPU. It does not support multi-GPUs, for reasons such as global BatchNorm and contrastive loss across cores.
The code is compatible with both TensorFlow v1 and v2. See requirements.txt for all prerequisites, and you can also install them using the following command.
pip install -r requirements.txt
To pretrain the model on CIFAR-10 with a single GPU, try the following command:
python run.py --train_mode=pretrain \
--train_batch_size=512 --train_epochs=1000 \
--learning_rate=1.0 --weight_decay=1e-6 --temperature=0.5 \
--dataset=cifar10 --image_size=32 --eval_split=test --resnet_depth=18 \
--use_blur=False --color_jitter_strength=0.5 \
--model_dir=/tmp/simclr_test --use_tpu=False
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 (which reflects the default hyperparameters in our paper):
python run.py --train_mode=pretrain \
--train_batch_size=4096 --train_epochs=100 \
--learning_rate=0.3 --weight_decay=1e-6 --temperature=0.1 \
--dataset=imagenet2012 --image_size=224 --eval_split=validation \
--data_dir=$DATA_DIR --model_dir=$MODEL_DIR \
--use_tpu=True --tpu_name=$TPU_NAME --train_summary_steps=0
A batch size of 4096 requires at least 32 TPUs. 100 epochs takes around 6 hours with 32 TPU v3s.
To fine-tune a linear head (with a single GPU), try the following command:
python run.py --mode=train_then_eval --train_mode=finetune \
--fine_tune_after_block=4 --zero_init_logits_layer=True \
--variable_schema='(?!global_step|(?:.*/|^)LARSOptimizer|head)' \
--global_bn=False --optimizer=momentum --learning_rate=0.1 --weight_decay=0.0 \
--train_epochs=100 --train_batch_size=512 --warmup_epochs=0 \
--dataset=cifar10 --image_size=32 --eval_split=test --resnet_depth=18 \
--checkpoint=/tmp/simclr_test --model_dir=/tmp/simclr_test_ft --use_tpu=False
You can check the results using tensorboard, such as
python -m tensorboard.main --logdir=/tmp/simclr_test
As a reference, the above runs on CIFAR-10 should give you around 91% accuracy, though it can be further optimized.
For fine-tuning a linear head on ImageNet using Cloud TPUs, first set the CHKPT_DIR to pretrained model dir and set a new MODEL_DIR, then use the following command:
python run.py --mode=train_then_eval --train_mode=finetune \
--fine_tune_after_block=4 --zero_init_logits_layer=True \
--variable_schema='(?!global_step|(?:.*/|^)LARSOptimizer|head)' \
--global_bn=False --optimizer=momentum --learning_rate=0.1 --weight_decay=1e-6 \
--train_epochs=90 --train_batch_size=4096 --warmup_epochs=0 \
--dataset=imagenet2012 --image_size=224 --eval_split=validation \
--data_dir=$DATA_DIR --model_dir=$MODEL_DIR --checkpoint=$CHKPT_DIR \
--use_tpu=True --tpu_name=$TPU_NAME --train_summary_steps=0
As a reference, the above runs on ImageNet should give you around 64.5% accuracy.
Image IDs of ImageNet 1% and 10% subsets used for semi-supervised learning can be found in imagenet_subsets/.
Our arXiv paper.
@article{chen2020simple,
title={A Simple Framework for Contrastive Learning of Visual Representations},
author={Chen, Ting and Kornblith, Simon and Norouzi, Mohammad and Hinton, Geoffrey},
journal={arXiv preprint arXiv:2002.05709},
year={2020}
}
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