data: 'dataset/splitted/val'       # path to dataset
model: 'tf_efficientnet_b0'      # Name of model to train (default: "countception"
# path to latest checkpoint (default: none)
checkpoint: 'output/train/tf_efficientnet_b0-224/model_best.pth.tar'

workers: 4                # number of McD loading workers (default: 2)
batch_size: 16            # mini-batch size (default: 256)
img_size: 224             # Input image dimension, uses model default if empty
crop_pct: null            # Input image center crop pct
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: 2            # Number classes in dataset
class_map: ''             # path to class to idx mapping file (default: "")
gp: null                  # Global pool type, one of (fast, avg, max, avgmax, avgmaxc). Model default if None.
log_freq: 10              # batch logging frequency (default: 10)
pretrained: False         # use pre-trained model
num_gpu: 1                # Number of GPUS to use
no_test_pool: False       # disable test time pool
no_prefetcher: False      # disable fast prefetcher
pin_mem: False            # Pin CPU memory in DataLoader for more efficient (sometimes) transfer to GPU
channels_last: False      # Use channels_last memory layout
amp: False                # Use AMP mixed precision. Defaults to Apex, fallback to native Torch AMP.
apex_amp: False           # Use NVIDIA Apex AMP mixed precision
native_amp: False         # Use Native Torch AMP mixed precision
tf_preprocessing: False   # Use Tensorflow preprocessing pipeline (require CPU TF installed
use_ema: False            # use ema version of weights if present
torchscript: False        # convert model torchscript for inference
lagacy_jit: False         # use legacy jit mode for pytorch 1.5/1.5.1/1.6 to get back fusion performance
results_file: ''          # Output csv file for validation results (summary)
real_labels: ''           # Real labels JSON file for imagenet evaluation
valid_labels: ''          # Valid label indices txt file for validation of partial label space