pcb_market1501_train.yaml

data:
  root: '~/datasets/reid'
  sources: ['market1501']
  targets: ['market1501']
  height: 384
  width: 128
  transforms: ['rc', 'rf', 're']

model:
  name: 'bpbreid'
  bpbreid:
    mask_filtering_training: False
    mask_filtering_testing: False  # no visibility scores used at training for PCB
    learnable_attention_enabled: True
    backbone: 'hrnet32'
    test_embeddings: ['conct']  # the holistic concatenated embedding alone is used to compute query-gallery distances
    masks:
      type: 'stripes'  # PCB uses horizontal stripes
      parts_num: 6  # we use 6 horizontal stripes here

loss:
  name: 'part_based'
  part_based:
    name: 'part_averaged_triplet_loss'
    ppl: 'cl'
    weights:
      globl:
        id: 0.
        tr: 0.
      foreg:
        id: 0.
        tr: 0.
      conct:
        id: 0.
        tr: 0.
      parts:
        id: 1.  # For PCB, we use the cross-entropy (identity) loss on each of the 6 horizontal stripes
        tr: 0.
      pixls:
        ce: 0.  # no body part prediction loss for PCB

train:
  batch_size: 64

test:
  evaluate: False
  batch_size: 64
  visrank: True