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102 lines
6.8 KiB
102 lines
6.8 KiB
# Dataset / Model parameters
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data: 'dataset/splitted' # path to dataset
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model: 'tf_efficientnet_b0' # Name of model to train (default: "countception")
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pretrained: True # Start with pretrained version of specified network (if avail)
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initial_checkpoint: '' # Initialize model from this checkpoint (default: none)
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resume: '' # Resume full model and optimizer state from checkpoint (default: none)
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no_resume_opt: False # prevent resume of optimizer state when resuming model
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num_classes: 2 # number of label classes (default: 1000)
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gp: null # Global pool type, one of (fast, avg, max, avgmax, avgmaxc). Model default if None.
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img_size: 224 # Image patch size (default: None => model default)
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crop_pct: null # Input image center crop percent (for validation only)
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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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batch_size: 16 # input batch size for training (default: 32)
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validation_batch_size_multiplier: 1 # ratio of validation batch size to training batch size (default: 1)
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# Optimizer parameters
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opt: 'Adam' # Optimizer (default: "sgd"
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opt_eps: null # Optimizer Epsilon (default: None, use opt default)
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opt_betas: null # Optimizer Betas (default: None, use opt default)
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momentum: 0.9 # Optimizer momentum (default: 0.9)
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weight_decay: 0.0 # weight decay (default: 0.0001)
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clip_grad: null # Clip gradient norm (default: None, no clipping)
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# Learning rate schedule parameters
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sched: 'plateau' # LR scheduler (default: "step")
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lr: 0.0001 # learning rate (default: 0.01)
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lr_noise: null # learning rate noise on/off epoch percentages
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lr_noise_pct: 0.67 # learning rate noise limit percent (default: 0.67)
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lr_noise_std: 1.0 # learning rate noise std-dev (default: 1.0)
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lr_cycle_mul: 1.0 # learning rate cycle len multiplier (default: 1.0)
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lr_cycle_limit: 1 # learning rate cycle limit
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warmup_lr: 0.0001 # warmup learning rate (default: 0.0001)
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min_lr: 0.00001 # lower lr bound for cyclic schedulers that hit 0 (1e-5)
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epochs: 30 # number of epochs to train (default: 2)
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start_epoch: null # manual epoch number (useful on restarts)
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decay_epochs: 5 # epoch interval to decay LR
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warmup_epochs: 10 # epochs to warmup LR, if scheduler supports
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cooldown_epochs: 0 # epochs to cooldown LR at min_lr, after cyclic schedule ends
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patience_epochs: 5 # patience epochs for Plateau LR scheduler (default: 10)
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decay_rate: 0.1 # LR decay rate (default: 0.1)
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# Augmentation & regularization parameters
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no_aug: False # Disable all training augmentation, override other train aug args
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scale: [1, 1] # Random resize scale (default: 0.08 1.0)
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ratio: [0.8, 1.2] # Random resize aspect ratio (default: 0.75 1.33)
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hflip: 0.5 # Horizontal flip training aug probability
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vflip: 0.0 # Vertical flip training aug probability
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color_jitter: 0.1 # Color jitter factor (default: 0.4)
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aa: null # Use AutoAugment policy. "v0" or "original". (default: None)
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aug_splits: 0 # Number of augmentation splits (default: 0, valid: 0 or >=2)
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jsd: False # Enable Jensen-Shannon Divergence + CE loss. Use with `--aug-splits`.
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reprob: 0.0 # Random erase prob (default: 0.)
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remode: 'const' # Random erase mode (default: "const")
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recount: 1 # Random erase count (default: 1)
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resplit: False # Do not random erase first (clean) augmentation split
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mixup: 0.0 # mixup alpha, mixup enabled if > 0. (default: 0.)
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cutmix: 0.0 # cutmix alpha, cutmix enabled if > 0. (default: 0.
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cutmix_minmax: # cutmix min/max ratio, overrides alpha and enables cutmix if set (default: None)
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mixup_prob: 1.0 # Probability of performing mixup or cutmix when either/both is enabled
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mixup_switch_prob: 0.5 # Probability of switching to cutmix when both mixup and cutmix enabled
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mixup_mode: 'batch' # How to apply mixup/cutmix params. Per "batch", "pair", or "elem"
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mixup_off_epoch: 0 # Turn off mixup after this epoch, disabled if 0 (default: 0)
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smoothing: 0.0 # Label smoothing (default: 0.1)
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train_interpolation: 'random' # Training interpolation (random, bilinear, bicubic default: "random"
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drop: 0.0 # Dropout rate (default: 0.)
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drop_connect: null # Drop connect rate, DEPRECATED, use drop-path (default: None)
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drop_path: null # Drop path rate (default: None)
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drop_block: null # Drop block rate (default: None)
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# Batch norm parameters (only works with gen_efficientnet based models currently)
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bn_tf: bull # Use Tensorflow BatchNorm defaults for models that support it (default: False)
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bn_momentum: null # BatchNorm momentum override (if not None)
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bn_eps: null # BatchNorm epsilon override (if not None)
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sync_bn: False # Enable NVIDIA Apex or Torch synchronized BatchNorm.
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dist_bn: '' # Distribute BatchNorm stats between nodes after each epoch ("broadcast", "reduce", or "")
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split_bn: False # Enable separate BN layers per augmentation split.
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# Model Exponential Moving Average
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model_ema: False # Enable tracking moving average of model weights
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model_ema_force_cpu: False # Force ema to be tracked on CPU, rank=0 node only. Disables EMA validation.
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model_ema_decay: 0.9998 # decay factor for model weights moving average (default: 0.9998)
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# Misc
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seed: 42 # random seed (default: 42)
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log_interval: 50 # how many batches to wait before logging training status
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recovery_interval: 0 # how many batches to wait before writing recovery checkpoint
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workers: 1 # how many training processes to use (default: 1)
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num_gpu: 1 # Number of GPUS to use
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save_images: False # save images of input bathes every log interval for debugging
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amp: False # use NVIDIA Apex AMP or Native AMP for mixed precision training
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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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channels_last: False # Use channels_last memory layout
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pin_mem: False # Pin CPU memory in DataLoader for more efficient (sometimes) transfer to GPU.
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no_prefetcher: False # disable fast prefetcher
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output: '' # path to output folder (default: none, current dir)
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eval_metric: 'top1' # Best metric (default: "top1"
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tta: 0 # Test/inference time augmentation (oversampling) factor. 0=None (default: 0)
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local_rank: 0 #
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use_multi_epochs_loader: False # use the multi-epochs-loader to save time at the beginning of every epoch |