Parameter Introduction

September 25, 2024 ยท View on GitHub

  • --name: Experiment name, used for saving models and parameters under checkpoints/, facilitating management and tracking of different experimental results.
  • --data_dir: Directory path for training data.
  • --num_worker: Number of worker threads used for data loading, affecting the parallelism and efficiency of data preprocessing.
  • --pad: Amount of padding for input data. Please distinguish this from the --pad in Position Shifting.
  • --h, --w: Height and width of the input images.
  • --rr: Random rotation applied to one or more views to enhance data diversity.
  • --ra: Random affine transformation applied to one or more views to enhance data diversity.
  • --re: Random occlusion applied to one or more views to enhance data diversity.
  • --cj: Color jitter applied to one or more views to enhance data diversity.
  • --erasing_p: Probability of random occlusion, controlling the proportion of randomly occluded areas in the images.
  • --warm_epoch: Warm-up phase, setting the learning rate to gradually increase over the first K epochs.
  • --lr: Learning rate.
  • --DA: Whether to use color data augmentation.
  • --droprate: Dropout rate.
  • --autocast: Whether to use mixed precision training.
  • --load_from: Path to the pre-loaded checkpoint for restoring the model from a previous training state.
  • --gpu_ids: Specification of the GPU devices used, supporting multi-GPU configurations for flexible training environments.
  • --batchsize: Number of samples per training step.
  • --block: Number of ClassBlocks in the model.
  • --cls_loss: Type of loss function for Representation Learning. Various preset or custom losses can be used, with CELoss as the default.
  • --feature_loss: Type of loss function for Metric Learning. Various preset or custom losses can be used, with no loss applied by default.
  • --kl_loss: Type of loss function for Mutual Learning. Various preset or custom losses can be used, with no loss applied by default.
  • --num_bottleneck: Dimensionality of feature embeddings.
  • --backbone: Backbone architecture used. Various preset or custom backbones can be selected, with cvt13 as the default.
  • --head: Head architecture used. Various preset or custom heads can be selected, with FSRA_CNN as the default.
  • --head_pool: Type of pooling used in the head, with various preset or custom pooling methods available, defaulting to max pooling.