|
|
|
model = dict(
|
|
type="RetinaNet",
|
|
backbone=dict(
|
|
type="ResNet",
|
|
depth=50,
|
|
num_stages=4,
|
|
out_indices=(0, 1, 2, 3),
|
|
frozen_stages=1,
|
|
norm_cfg=dict(type="BN", requires_grad=True),
|
|
norm_eval=True,
|
|
style="pytorch",
|
|
init_cfg=dict(type="Pretrained", checkpoint="torchvision://resnet50"),
|
|
),
|
|
neck=dict(
|
|
type="FPN",
|
|
in_channels=[256, 512, 1024, 2048],
|
|
out_channels=256,
|
|
start_level=1,
|
|
add_extra_convs="on_input",
|
|
num_outs=5,
|
|
),
|
|
bbox_head=dict(
|
|
type="RetinaHead",
|
|
num_classes=10,
|
|
in_channels=256,
|
|
stacked_convs=4,
|
|
feat_channels=256,
|
|
anchor_generator=dict(
|
|
type="AnchorGenerator",
|
|
octave_base_scale=4,
|
|
scales_per_octave=3,
|
|
ratios=[0.5, 1.0, 2.0],
|
|
strides=[8, 16, 32, 64, 128],
|
|
),
|
|
bbox_coder=dict(
|
|
type="DeltaXYWHBBoxCoder",
|
|
target_means=[0.0, 0.0, 0.0, 0.0],
|
|
target_stds=[1.0, 1.0, 1.0, 1.0],
|
|
),
|
|
loss_cls=dict(
|
|
type="FocalLoss",
|
|
use_sigmoid=True,
|
|
gamma=2.0,
|
|
alpha=0.25,
|
|
loss_weight=1.0,
|
|
),
|
|
loss_bbox=dict(type="L1Loss", loss_weight=1.0),
|
|
),
|
|
|
|
train_cfg=dict(
|
|
assigner=dict(
|
|
type="MaxIoUAssigner",
|
|
pos_iou_thr=0.5,
|
|
neg_iou_thr=0.4,
|
|
min_pos_iou=0,
|
|
ignore_iof_thr=-1,
|
|
),
|
|
allowed_border=-1,
|
|
pos_weight=-1,
|
|
debug=False,
|
|
),
|
|
test_cfg=dict(
|
|
nms_pre=1000,
|
|
min_bbox_size=0,
|
|
score_thr=0.05,
|
|
nms=dict(type="nms", iou_threshold=0.5),
|
|
max_per_img=100,
|
|
),
|
|
)
|
|
|
