mmdetection-2.26.0/configs/legacy_1.x/ssd300_coco_v1.py

_base_ = [
 '../_base_/models/ssd300.py', '../_base_/datasets/coco_detection.py',
 '../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
]
# model settings
input_size = 300
model = dict(
 bbox_head=dict(
 type='SSDHead',
 anchor_generator=dict(
 type='LegacySSDAnchorGenerator',
 scale_major=False,
 input_size=input_size,
 basesize_ratio_range=(0.15, 0.9),
 strides=[8, 16, 32, 64, 100, 300],
 ratios=[[2], [2, 3], [2, 3], [2, 3], [2], [2]]),
 bbox_coder=dict(
 type='LegacyDeltaXYWHBBoxCoder',
 target_means=[.0, .0, .0, .0],
 target_stds=[0.1, 0.1, 0.2, 0.2])))
# dataset settings
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
img_norm_cfg = dict(mean=[123.675, 116.28, 103.53], std=[1, 1, 1], to_rgb=True)
train_pipeline = [
 dict(type='LoadImageFromFile', to_float32=True),
 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=img_norm_cfg['mean'],
 to_rgb=img_norm_cfg['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', img_scale=(300, 300), keep_ratio=False),
 dict(type='Normalize', **img_norm_cfg),
 dict(type='RandomFlip', flip_ratio=0.5),
 dict(type='DefaultFormatBundle'),
 dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
]
test_pipeline = [
 dict(type='LoadImageFromFile'),
 dict(
 type='MultiScaleFlipAug',
 img_scale=(300, 300),
 flip=False,
 transforms=[
 dict(type='Resize', keep_ratio=False),
 dict(type='Normalize', **img_norm_cfg),
 dict(type='ImageToTensor', keys=['img']),
 dict(type='Collect', keys=['img']),
 ])
]
data = dict(
 samples_per_gpu=8,
 workers_per_gpu=3,
 train=dict(
 _delete_=True,
 type='RepeatDataset',
 times=5,
 dataset=dict(
 type=dataset_type,
 ann_file=data_root + 'annotations/instances_train2017.json',
 img_prefix=data_root + 'train2017/',
 pipeline=train_pipeline)),
 val=dict(pipeline=test_pipeline),
 test=dict(pipeline=test_pipeline))
# optimizer
optimizer = dict(type='SGD', lr=2e-3, momentum=0.9, weight_decay=5e-4)
optimizer_config = dict(_delete_=True)
dist_params = dict(backend='nccl', port=29555)

# NOTE: `auto_scale_lr` is for automatically scaling LR,
# USER SHOULD NOT CHANGE ITS VALUES.
# base_batch_size = (8 GPUs) x (8 samples per GPU)
auto_scale_lr = dict(base_batch_size=64)