ResNet50_ReID.yaml

# global configs
Global:
  checkpoints: null
  pretrained_model: null
  output_dir: "./output/"
  device: "gpu"
  save_interval: 1
  eval_during_train: True
  eval_interval: 1
  epochs: 120
  print_batch_step: 10
  use_visualdl: False
  # used for static mode and model export
  image_shape: [3, 224, 224]
  save_inference_dir: "./inference"
  eval_mode: "retrieval"

# model architecture
Arch:
  name: "RecModel"
  infer_output_key: "features"
  infer_add_softmax: False
  Backbone:
    name: "ResNet50_last_stage_stride1"
    pretrained: True
  BackboneStopLayer:
    name: "avg_pool"
  Neck:
    name: "VehicleNeck"
    in_channels: 2048
    out_channels: 512
  Head:
    name: "CircleMargin"
    margin: 0.35
    scale:  64
    embedding_size: 512
    class_num: 3000

# loss function config for traing/eval process
Loss:
  Train:
    - CELoss:
        weight: 1.0
    - PairwiseCosface:
        margin: 0.35
        gamma: 64
        weight: 1.0
  Eval:
    - CELoss:
        weight: 1.0

Optimizer:
  name: Momentum
  momentum: 0.9
  lr:
    name: Cosine
    learning_rate: 0.04
  regularizer:
    name: 'L2'
    coeff: 0.0001

# data loader for train and eval
DataLoader:
  Train:
    dataset:
        name: ImageNetDataset
        image_root: "dataset/LogoDet-3K-crop/train/"
        cls_label_path: "dataset/LogoDet-3K-crop/train_list.txt"
        transform_ops:
          - DecodeImage:
              to_rgb: True
              channel_first: False
          - ResizeImage:
              size: 224
          - RandFlipImage:
              flip_code: 1
          - AugMix:
              prob: 0.5
          - NormalizeImage:
              scale: 0.00392157
              mean: [0.485, 0.456, 0.406]
              std: [0.229, 0.224, 0.225]
              order: ''
          - RandomErasing:
              EPSILON: 0.5
    sampler:
        name: PKSampler
        batch_size: 128
        sample_per_id: 2
        drop_last: True

    loader:
        num_workers: 6
        use_shared_memory: True
  Eval:
    Query:
      dataset:
        name: ImageNetDataset
        image_root: "dataset/LogoDet-3K-crop/val/"
        cls_label_path: "dataset/LogoDet-3K-crop/query_list.txt"
        transform_ops:
          - DecodeImage:
              to_rgb: True
              channel_first: False
          - ResizeImage:
              size: 224
          - NormalizeImage:
              scale: 0.00392157
              mean: [0.485, 0.456, 0.406]
              std: [0.229, 0.224, 0.225]
              order: ''
      sampler:
          name: DistributedBatchSampler
          batch_size: 128
          drop_last: False
          shuffle: False
      loader:
          num_workers: 8
          use_shared_memory: True

    Gallery:
      dataset:
          name: ImageNetDataset
          image_root: "dataset/LogoDet-3K-crop/train/"
          cls_label_path: "dataset/LogoDet-3K-crop/train_list.txt"
          transform_ops:
            - DecodeImage:
                to_rgb: True
                channel_first: False
            - ResizeImage:
                size: 224
            - NormalizeImage:
                scale: 0.00392157
                mean: [0.485, 0.456, 0.406]
                std: [0.229, 0.224, 0.225]
                order: ''
      sampler:
          name: DistributedBatchSampler
          batch_size: 128
          drop_last: False
          shuffle: False
      loader:
          num_workers: 8
          use_shared_memory: True

Metric:
  Eval:
    - Recallk:
        topk: [1, 5]
    - mAP: {}