|
|
|
model = dict(
|
|
type="FasterRCNN",
|
|
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,
|
|
num_outs=5,
|
|
),
|
|
rpn_head=dict(
|
|
type="RPNHead",
|
|
in_channels=256,
|
|
feat_channels=256,
|
|
anchor_generator=dict(
|
|
type="AnchorGenerator",
|
|
scales=[8],
|
|
ratios=[0.5, 1.0, 2.0],
|
|
strides=[4, 8, 16, 32, 64],
|
|
),
|
|
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="CrossEntropyLoss", use_sigmoid=True, loss_weight=1.0
|
|
),
|
|
loss_bbox=dict(type="L1Loss", loss_weight=1.0),
|
|
),
|
|
roi_head=dict(
|
|
type="StandardRoIHead",
|
|
bbox_roi_extractor=dict(
|
|
type="SingleRoIExtractor",
|
|
roi_layer=dict(type="RoIAlign", output_size=7, sampling_ratio=0),
|
|
out_channels=256,
|
|
featmap_strides=[4, 8, 16, 32],
|
|
),
|
|
bbox_head=dict(
|
|
type="Shared2FCBBoxHead",
|
|
in_channels=256,
|
|
fc_out_channels=1024,
|
|
roi_feat_size=7,
|
|
num_classes=10,
|
|
bbox_coder=dict(
|
|
type="DeltaXYWHBBoxCoder",
|
|
target_means=[0.0, 0.0, 0.0, 0.0],
|
|
target_stds=[0.1, 0.1, 0.2, 0.2],
|
|
),
|
|
reg_class_agnostic=False,
|
|
loss_cls=dict(
|
|
type="CrossEntropyLoss", use_sigmoid=False, loss_weight=1.0
|
|
),
|
|
loss_bbox=dict(type="L1Loss", loss_weight=1.0),
|
|
),
|
|
),
|
|
|
|
train_cfg=dict(
|
|
rpn=dict(
|
|
assigner=dict(
|
|
type="MaxIoUAssigner",
|
|
pos_iou_thr=0.7,
|
|
neg_iou_thr=0.3,
|
|
min_pos_iou=0.3,
|
|
match_low_quality=True,
|
|
ignore_iof_thr=-1,
|
|
),
|
|
sampler=dict(
|
|
type="RandomSampler",
|
|
num=256,
|
|
pos_fraction=0.5,
|
|
neg_pos_ub=-1,
|
|
add_gt_as_proposals=False,
|
|
),
|
|
allowed_border=-1,
|
|
pos_weight=-1,
|
|
debug=False,
|
|
),
|
|
rpn_proposal=dict(
|
|
nms_pre=2000,
|
|
max_per_img=1000,
|
|
nms=dict(type="nms", iou_threshold=0.7),
|
|
min_bbox_size=0,
|
|
),
|
|
rcnn=dict(
|
|
assigner=dict(
|
|
type="MaxIoUAssigner",
|
|
pos_iou_thr=0.5,
|
|
neg_iou_thr=0.5,
|
|
min_pos_iou=0.5,
|
|
match_low_quality=False,
|
|
ignore_iof_thr=-1,
|
|
),
|
|
sampler=dict(
|
|
type="RandomSampler",
|
|
num=512,
|
|
pos_fraction=0.25,
|
|
neg_pos_ub=-1,
|
|
add_gt_as_proposals=True,
|
|
),
|
|
pos_weight=-1,
|
|
debug=False,
|
|
),
|
|
),
|
|
test_cfg=dict(
|
|
rpn=dict(
|
|
nms_pre=1000,
|
|
max_per_img=1000,
|
|
nms=dict(type="nms", iou_threshold=0.7),
|
|
min_bbox_size=0,
|
|
),
|
|
rcnn=dict(
|
|
score_thr=0.05,
|
|
nms=dict(type="nms", iou_threshold=0.5),
|
|
max_per_img=100,
|
|
)
|
|
|
|
|
|
),
|
|
)
|
|
|
