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35 lines
2.2 KiB
35 lines
2.2 KiB
data: 'dataset/splitted/val' # path to dataset
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model: 'tf_efficientnet_b0' # Name of model to train (default: "countception"
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# path to latest checkpoint (default: none)
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checkpoint: 'output/train/tf_efficientnet_b0-224/model_best.pth.tar'
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workers: 4 # number of McD loading workers (default: 2)
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batch_size: 16 # mini-batch size (default: 256)
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img_size: 224 # Input image dimension, uses model default if empty
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crop_pct: null # Input image center crop pct
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mean: null # Override mean pixel value of dataset
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std: null # Override std deviation of of dataset
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interpolation: '' # Image resize interpolation type (overrides model)
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num_classes: 2 # Number classes in dataset
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class_map: '' # path to class to idx mapping file (default: "")
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gp: null # Global pool type, one of (fast, avg, max, avgmax, avgmaxc). Model default if None.
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log_freq: 10 # batch logging frequency (default: 10)
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pretrained: False # use pre-trained model
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num_gpu: 1 # Number of GPUS to use
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no_test_pool: False # disable test time pool
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no_prefetcher: False # disable fast prefetcher
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pin_mem: False # Pin CPU memory in DataLoader for more efficient (sometimes) transfer to GPU
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channels_last: False # Use channels_last memory layout
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amp: False # Use AMP mixed precision. Defaults to Apex, fallback to native Torch AMP.
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apex_amp: False # Use NVIDIA Apex AMP mixed precision
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native_amp: False # Use Native Torch AMP mixed precision
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tf_preprocessing: False # Use Tensorflow preprocessing pipeline (require CPU TF installed
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use_ema: False # use ema version of weights if present
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torchscript: False # convert model torchscript for inference
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lagacy_jit: False # use legacy jit mode for pytorch 1.5/1.5.1/1.6 to get back fusion performance
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results_file: '' # Output csv file for validation results (summary)
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real_labels: '' # Real labels JSON file for imagenet evaluation
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valid_labels: '' # Valid label indices txt file for validation of partial label space
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