Teachers

Implementation of momentum^2 teacher

  1. All experiments are done with python3.6, torch==1.5.0; torchvision==0.6.0

Data Preparation

Prepare the ImageNet data in ${root_of_your_clone}/data/imagenet_train, ${root_of_your_clone}/data/imagenet_val. Since we have an internal platform(storage) to read imagenet, I have not tried the local mode. You may need to do some modification in momentum_teacher/data/dataset.py to support the local mode.

Training

Before training, ensure the path (namely ${root_of_clone}) is added in your PYTHONPATH, e.g.

export PYTHONPATH=$PYTHONPATH:${root_of_clone}

To do unsupervised pre-training of a ResNet-50 model on ImageNet in an 8-gpu machine, run:

  1. using -d to specify gpu_id for training, e.g., -d 0-7
  2. using -b to specify batch_size, e.g., -b 256
  3. using --experiment-name to specify the output folder, and the training log & models will be dumped to ‘./outputs/${experiment-name}’
  4. using -f to specify

     

     

     

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