palm_recognition/params.yaml

logger:
  level: "DEBUG"
  logfmt: "%(asctime)s: %(levelname)-8s [%(filename)s:%(lineno)d] %(message)s"
  datefmt: "%Y-%m-%d:%H:%M:%S"
preprocess:
  raw_data_root: "/Users/timho102003/Documents/datasets/11K-Hands/"
  img_fld: "Hands"
  meta_file: "HandInfo.csv"
  create_merge: false
  merge_file: "hand_augId370.csv"
  save_to: images
  verbose: "skeleton"
prepare:
  train: "train_palmar.csv"
  val: "val_palmar.csv"
  test: "test_palmar.csv"
  specify_hand: null
  img_ext: "jpg"
trainval:
  train_csv: null
  val_csv: null
  test_csv: null
  tot_data_file: 'hand_augId370.csv'
  mlflow: "remote"
  fold: 3
  specify_hand_side: null
  experiment_name: "beam_project_fold1"
  is_validate: true
  val_register_num: 1
  log_interval: 10
  gpu_type: "cuda"
  batchsize: 64
  n_worker: 2
  arcface_m: 0.5
  arcface_s: 40
  embedding_size: 512
  net_depth: 50
  drop_ratio: 0.6
  pretrained: "model_mobilefacenet.pth"
  net_mode: "mobilefacenet"
  loss_type: "CrossEntropyLoss"
  epoch: 10
  opt: "SGD"
  opt_params:
    lr: 0.003
    momentum: 0.9
    weight_decay: 0.0005
  lr_sched: "MultiStepLR"
  lr_sched_params:
    milestones: [6, 8]
    gamma: 0.1
  img_resize: [120, 120]
  img_crop: [112, 112]
  img_rflip_p: 0.5
  img_vflip_p: 0.5
  img_mean: [0.5, 0.5, 0.5]
  img_std: [0.5, 0.5, 0.5]
test:
  specify_hand_side: null
  model: "best_fold0_final.pth"
  features: "embed_right_feature.pt"
  embedding_size: 512
  net_depth: 50
  drop_ratio: 0.6
  net_mode: "mobilefacenet"
  register_num: 1
  topK: 3
onnx:
  net_mode: "mobilefacenet" #"mobilefacenet"ir_se
  model: 'best_fold0_final.pth' #"model_v1/best_e20.pth"
  embedding_size: 512
  net_depth: 50
  drop_ratio: 0.6
  output: "model.onnx"
  img_crop: [112, 112]