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Get Started with ZCls

  1. Add dataset path to config_file, like CIFAR100

    NAME: 'CIFAR100'
TRAIN_ROOT: './data/cifar'
TEST_ROOT: './data/cifar'

    Note 1: current support CIFAR10/CIFAR100/FashionMNIST/ImageNet

    Note 2: use BGR image format

  2. Add environment variable

    $ export PYTHONPATH=/path/to/ZCls

  3. Train

    $ CUDA_VISIBLE_DEVICES=0 python tool/train.py -cfg=configs/cifar/r50_cifar100_224_e100_rmsprop.yaml

    After training, the corresponding model can be found in outputs/

  4. Using pretrained model, refer to Pretrained Model

  5. If finished the training halfway, resume it like this

    $ CUDA_VISIBLE_DEVICES=0 python tool/train.py -cfg=configs/cifar/r50_cifar100_224_e100_rmsprop.yaml --resume

  6. Use multiple GPU to train

    $ CUDA_VISIBLE_DEVICES=0<,1,2,3> python tool/train.py -cfg=configs/cifar/r50_cifar100_224_e100_rmsprop.yaml -g=<N>