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pytorch-image-models/configs/inference.yaml

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data: 'dataset/test' # path to dataset
output_dir: 'output/' # path to output files
model: 'tf_efficientnet_b5' # model architecture (default: dpn92)
workers: 4 # number of McD loading workers (default: 2
batch_size: 256 # mini-batch size (default: 256)
img_size: 224 # Input image dimension
mean: null # Override mean pixel value of dataset
std: null # Override std deviation of of dataset
interpolation: '' # Image resize interpolation type (overrides model)
num_classes: 1000 # Number classes in dataset
log_freq: 10 # batch logging frequency (default: 10)
checkpoint: '' # path to latest checkpoint (default: none)
pretrained: True # use pre-trained model
num_gpu: 1 # Number of GPUS to use
no_test_pool: False # disable test time pool
topk: 5 # Top-k to output to CSV