mmpretrain/configs/_base_/datasets/voc_bs16.py

# dataset settings
dataset_type = 'VOC'
data_preprocessor = dict(
    num_classes=20,
    # RGB format normalization parameters
    mean=[123.675, 116.28, 103.53],
    std=[58.395, 57.12, 57.375],
    # convert image from BGR to RGB
    to_rgb=True,
    # generate onehot-format labels for multi-label classification.
    to_onehot=True,
)

train_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(type='RandomResizedCrop', scale=224),
    dict(type='RandomFlip', prob=0.5, direction='horizontal'),
    dict(type='PackInputs'),
]

test_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(type='ResizeEdge', scale=256, edge='short'),
    dict(type='CenterCrop', crop_size=224),
    dict(
        type='PackInputs',
        # `gt_label_difficult` is needed for VOC evaluation
        meta_keys=('sample_idx', 'img_path', 'ori_shape', 'img_shape',
                   'scale_factor', 'flip', 'flip_direction',
                   'gt_label_difficult')),
]

train_dataloader = dict(
    batch_size=16,
    num_workers=5,
    dataset=dict(
        type=dataset_type,
        data_root='data/VOC2007',
        split='trainval',
        pipeline=train_pipeline),
    sampler=dict(type='DefaultSampler', shuffle=True),
)

val_dataloader = dict(
    batch_size=16,
    num_workers=5,
    dataset=dict(
        type=dataset_type,
        data_root='data/VOC2007',
        split='test',
        pipeline=test_pipeline),
    sampler=dict(type='DefaultSampler', shuffle=False),
)

test_dataloader = val_dataloader

# calculate precision_recall_f1 and mAP
val_evaluator = [
    dict(type='VOCMultiLabelMetric'),
    dict(type='VOCMultiLabelMetric', average='micro'),
    dict(type='VOCAveragePrecision')
]

test_dataloader = val_dataloader
test_evaluator = val_evaluator