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Generate mCE dataset

We assume that you prepare raw images ImageNet2012 in the following path.

# replace this with yours
IMAGENET_RAW_PATH=/home1/irteam/user/jklee/food-fighters/models/tensorflow/imagenet_raw

You can create an mCE evaluation set for ImageNet using the following command:

python CE_validation_generator.py \
--validation_dir=${IMAGENET_RAW_PATH}/validation \
--output_dir=${IMAGENET_RAW_PATH}/validation_256_224 \
--RESIZE_SIZE=256 \
--CROP_SIZE=224

--RESIZE_SIZE=292 and --CROP_SIZE=256 means that first resize the shorter size of each image to 256 pixels while the aspect ratio is main-tained, then we center crop image of 224x224 size.

When evaluating model with BigLittleNet, since the resolution is 256x256 for validation, make validation set using the following command:

python CE_validation_generator.py \
--validation_dir=${IMAGENET_RAW_PATH}/validation \
--output_dir=${IMAGENET_RAW_PATH}/validation_292_256 \
--RESIZE_SIZE=292 \
--CROP_SIZE=256