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# model settings | |
norm_cfg = dict(type='BN', requires_grad=False) | |
model = dict( | |
type='FasterRCNN', | |
data_preprocessor=dict( | |
type='DetDataPreprocessor', | |
mean=[103.530, 116.280, 123.675], | |
std=[1.0, 1.0, 1.0], | |
bgr_to_rgb=False, | |
pad_size_divisor=32), | |
backbone=dict( | |
type='ResNet', | |
depth=50, | |
num_stages=3, | |
strides=(1, 2, 2), | |
dilations=(1, 1, 1), | |
out_indices=(2, ), | |
frozen_stages=1, | |
norm_cfg=norm_cfg, | |
norm_eval=True, | |
style='caffe', | |
init_cfg=dict( | |
type='Pretrained', | |
checkpoint='open-mmlab://detectron2/resnet50_caffe')), | |
rpn_head=dict( | |
type='RPNHead', | |
in_channels=1024, | |
feat_channels=1024, | |
anchor_generator=dict( | |
type='AnchorGenerator', | |
scales=[2, 4, 8, 16, 32], | |
ratios=[0.5, 1.0, 2.0], | |
strides=[16]), | |
bbox_coder=dict( | |
type='DeltaXYWHBBoxCoder', | |
target_means=[.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', | |
shared_head=dict( | |
type='ResLayer', | |
depth=50, | |
stage=3, | |
stride=2, | |
dilation=1, | |
style='caffe', | |
norm_cfg=norm_cfg, | |
norm_eval=True, | |
init_cfg=dict( | |
type='Pretrained', | |
checkpoint='open-mmlab://detectron2/resnet50_caffe')), | |
bbox_roi_extractor=dict( | |
type='SingleRoIExtractor', | |
roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=0), | |
out_channels=1024, | |
featmap_strides=[16]), | |
bbox_head=dict( | |
type='BBoxHead', | |
with_avg_pool=True, | |
roi_feat_size=7, | |
in_channels=2048, | |
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))), | |
# model training and testing settings | |
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=12000, | |
max_per_img=2000, | |
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=6000, | |
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))) | |