mmdetection/configs/wider_face/ssd300_8xb32-24e_widerface.py

_base_ = [
    '../_base_/models/ssd300.py', '../_base_/datasets/wider_face.py',
    '../_base_/default_runtime.py', '../_base_/schedules/schedule_2x.py'
]
model = dict(bbox_head=dict(num_classes=1))

train_pipeline = [
    dict(type='LoadImageFromFile', backend_args=_base_.backend_args),
    dict(type='LoadAnnotations', with_bbox=True),
    dict(
        type='PhotoMetricDistortion',
        brightness_delta=32,
        contrast_range=(0.5, 1.5),
        saturation_range=(0.5, 1.5),
        hue_delta=18),
    dict(
        type='Expand',
        mean={{_base_.model.data_preprocessor.mean}},
        to_rgb={{_base_.model.data_preprocessor.bgr_to_rgb}},
        ratio_range=(1, 4)),
    dict(
        type='MinIoURandomCrop',
        min_ious=(0.1, 0.3, 0.5, 0.7, 0.9),
        min_crop_size=0.3),
    dict(type='Resize', scale=(300, 300), keep_ratio=False),
    dict(type='RandomFlip', prob=0.5),
    dict(type='PackDetInputs')
]

test_pipeline = [
    dict(type='LoadImageFromFile', backend_args=_base_.backend_args),
    dict(type='Resize', scale=(300, 300), keep_ratio=False),
    dict(type='LoadAnnotations', with_bbox=True),
    dict(
        type='PackDetInputs',
        meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
                   'scale_factor'))
]

dataset_type = 'WIDERFaceDataset'
data_root = 'data/WIDERFace/'
train_dataloader = dict(
    batch_size=32, num_workers=8, dataset=dict(pipeline=train_pipeline))

val_dataloader = dict(dataset=dict(pipeline=test_pipeline))
test_dataloader = val_dataloader

# learning rate
param_scheduler = [
    dict(
        type='LinearLR', start_factor=0.001, by_epoch=False, begin=0,
        end=1000),
    dict(type='MultiStepLR', by_epoch=True, milestones=[16, 20], gamma=0.1)
]

# optimizer
optim_wrapper = dict(
    optimizer=dict(lr=0.012, momentum=0.9, weight_decay=5e-4),
    clip_grad=dict(max_norm=35, norm_type=2))

# NOTE: `auto_scale_lr` is for automatically scaling LR,
# USER SHOULD NOT CHANGE ITS VALUES.
# base_batch_size = (8 GPUs) x (32 samples per GPU)
auto_scale_lr = dict(base_batch_size=256)