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solov2_light_r50_vd_fpn_dcn_512_3x.yml
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architecture: SOLOv2
use_gpu: true
max_iters: 270000
snapshot_iter: 30000
log_smooth_window: 20
save_dir: output
pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_vd_ssld_pretrained.tar
metric: COCO
weights: output/solov2_light_r50_vd_fpn_dcn_512_3x/model_final
num_classes: 81
use_ema: true
ema_decay: 0.9998
SOLOv2:
backbone: ResNet
fpn: FPN
bbox_head: SOLOv2Head
mask_head: SOLOv2MaskHead
ResNet:
depth: 50
feature_maps: [2, 3, 4, 5]
freeze_at: 0
freeze_norm: false
norm_type: sync_bn
dcn_v2_stages: [3, 4, 5]
variant: d
lr_mult_list: [0.05, 0.05, 0.1, 0.15]
FPN:
max_level: 6
min_level: 2
num_chan: 256
spatial_scale: [0.03125, 0.0625, 0.125, 0.25]
reverse_out: True
SOLOv2Head:
seg_feat_channels: 256
stacked_convs: 3
num_grids: [40, 36, 24, 16, 12]
kernel_out_channels: 128
solov2_loss: SOLOv2Loss
mask_nms: MaskMatrixNMS
dcn_v2_stages: [2,]
drop_block: True
SOLOv2MaskHead:
in_channels: 128
out_channels: 128
start_level: 0
end_level: 3
SOLOv2Loss:
ins_loss_weight: 3.0
focal_loss_gamma: 2.0
focal_loss_alpha: 0.25
MaskMatrixNMS:
pre_nms_top_n: 500
post_nms_top_n: 100
LearningRate:
base_lr: 0.01
schedulers:
- !PiecewiseDecay
gamma: 0.1
milestones: [180000, 240000]
- !LinearWarmup
start_factor: 0.
steps: 1000
OptimizerBuilder:
optimizer:
momentum: 0.9
type: Momentum
regularizer:
factor: 0.0001
type: L2
_READER_: 'solov2_light_reader.yml'