|
|
|
model = dict( |
|
type='FastRCNN', |
|
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), |
|
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=80, |
|
bbox_coder=dict( |
|
type='DeltaXYWHBBoxCoder', |
|
target_means=[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( |
|
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( |
|
rcnn=dict( |
|
score_thr=0.05, |
|
nms=dict(type='nms', iou_threshold=0.5), |
|
max_per_img=100))) |
|
|
