|
|
|
model = dict( |
|
type="ATSS", |
|
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_output", |
|
num_outs=5, |
|
), |
|
bbox_head=dict( |
|
type="ATSSHead", |
|
num_classes=10, |
|
in_channels=256, |
|
stacked_convs=4, |
|
feat_channels=256, |
|
anchor_generator=dict( |
|
type="AnchorGenerator", |
|
ratios=[1.0], |
|
octave_base_scale=8, |
|
scales_per_octave=1, |
|
strides=[8, 16, 32, 64, 128], |
|
), |
|
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], |
|
), |
|
loss_cls=dict( |
|
type="FocalLoss", |
|
use_sigmoid=True, |
|
gamma=2.0, |
|
alpha=0.25, |
|
loss_weight=1.0, |
|
), |
|
loss_bbox=dict(type="GIoULoss", loss_weight=2.0), |
|
loss_centerness=dict( |
|
type="CrossEntropyLoss", use_sigmoid=True, loss_weight=1.0 |
|
), |
|
), |
|
|
|
train_cfg=dict( |
|
assigner=dict(type="ATSSAssigner", topk=9), |
|
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=0.6), |
|
max_per_img=100, |
|
), |
|
) |
|
|
