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# model settings
model = dict(
type="FCOS",
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=False),
norm_eval=True,
style="caffe",
init_cfg=dict(
type="Pretrained",
checkpoint="open-mmlab://detectron/resnet50_caffe",
),
),
neck=dict(
type="FPN",
in_channels=[256, 512, 1024, 2048],
out_channels=256,
start_level=1,
add_extra_convs="on_output", # use P5
num_outs=5,
relu_before_extra_convs=True,
),
bbox_head=dict(
type="FCOSHead",
num_classes=10,
in_channels=256,
stacked_convs=4,
feat_channels=256,
strides=[8, 16, 32, 64, 128],
loss_cls=dict(
type="FocalLoss",
use_sigmoid=True,
gamma=2.0,
alpha=0.25,
loss_weight=1.0,
),
loss_bbox=dict(type="IoULoss", loss_weight=1.0),
loss_centerness=dict(
type="CrossEntropyLoss", use_sigmoid=True, loss_weight=1.0
),
),
# training and testing settings
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,
),
)
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