|
_base_ = [ |
|
'../_base_/datasets/coco_detection.py', |
|
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' |
|
] |
|
|
|
model = dict( |
|
type='FOVEA', |
|
pretrained='torchvision://resnet50', |
|
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'), |
|
neck=dict( |
|
type='FPN', |
|
in_channels=[256, 512, 1024, 2048], |
|
out_channels=256, |
|
start_level=1, |
|
num_outs=5, |
|
add_extra_convs='on_input'), |
|
bbox_head=dict( |
|
type='FoveaHead', |
|
num_classes=80, |
|
in_channels=256, |
|
stacked_convs=4, |
|
feat_channels=256, |
|
strides=[8, 16, 32, 64, 128], |
|
base_edge_list=[16, 32, 64, 128, 256], |
|
scale_ranges=((1, 64), (32, 128), (64, 256), (128, 512), (256, 2048)), |
|
sigma=0.4, |
|
with_deform=False, |
|
loss_cls=dict( |
|
type='FocalLoss', |
|
use_sigmoid=True, |
|
gamma=1.50, |
|
alpha=0.4, |
|
loss_weight=1.0), |
|
loss_bbox=dict(type='SmoothL1Loss', beta=0.11, loss_weight=1.0)), |
|
|
|
train_cfg=dict(), |
|
test_cfg=dict( |
|
nms_pre=1000, |
|
score_thr=0.05, |
|
nms=dict(type='nms', iou_threshold=0.5), |
|
max_per_img=100)) |
|
data = dict(samples_per_gpu=4, workers_per_gpu=4) |
|
|
|
optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001) |
|
|
