PP-HGNetV2-B4.yaml

Global:
  model: PP-HGNetV2-B4
  mode: check_dataset # check_dataset/train/evaluate/predict
  dataset_dir: "dataset"
  device: gpu:0
  output: "output"

CheckDataset:
  convert:
    enable: False
    src_dataset_type: null
  split:
    enable: False
    train_percent: null
    val_percent: null

Train:
  num_classes: 91
  epochs_iters: 100
  batch_size: 64
  learning_rate: 0.05
  pretrain_weight_path: https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPHGNetV2_B4_ssld_pretrained.pdparams
  warmup_steps: 5
  resume_path: null
  log_interval: 1
  eval_interval: 1
  save_interval: 5

Evaluate:
  weight_path: "output/best_model/best_model.pdparams"
  log_interval: 1

Export:
  weight_path: https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPHGNetV2_B4_ssld_pretrained.pdparams
  # weight_path: "./output/best_model/inference/inference"

Predict:
  batch_size: 1
  model_dir: "output/best_model/inference"
  input: "cropped_3.jpg"
  kernel_option:
    run_mode: paddle