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backbone_conf.yaml
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backbone_conf.yaml
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MobileFaceNet:
feat_dim: 512
out_h: 7
out_w: 7
ResNet:
depth: 152
drop_ratio: 0.4
net_mode: ir_se
feat_dim: 512
out_h: 7
out_w: 7
EfficientNet:
width: 1.0
depth: 1.0
image_size: 112
drop_ratio: 0.2
out_h: 7
out_w: 7
feat_dim: 512
HRNet:
NAME: cls_hrnet
out_h: 7
out_w: 7
feat_dim: 512
IMAGE_SIZE:
- 112
- 112
EXTRA:
STAGE1:
NUM_MODULES: 1
NUM_RANCHES: 1
BLOCK: BOTTLENECK
NUM_BLOCKS:
- 4
NUM_CHANNELS:
- 64
FUSE_METHOD: SUM
STAGE2:
NUM_MODULES: 1
NUM_BRANCHES: 2
BLOCK: BASIC
NUM_BLOCKS:
- 4
- 4
NUM_CHANNELS:
- 18
- 36
FUSE_METHOD: SUM
STAGE3:
NUM_MODULES: 4
NUM_BRANCHES: 3
BLOCK: BASIC
NUM_BLOCKS:
- 4
- 4
- 4
NUM_CHANNELS:
- 18
- 36
- 72
FUSE_METHOD: SUM
STAGE4:
NUM_MODULES: 3
NUM_BRANCHES: 4
BLOCK: BASIC
NUM_BLOCKS:
- 4
- 4
- 4
- 4
NUM_CHANNELS:
- 18
- 36
- 72
- 144
FUSE_METHOD: SUM
GhostNet:
width: 1.0
drop_ratio: 0.2
out_h: 7
out_w: 7
feat_dim: 512
AttentionNet:
stage1_modules: 1
stage2_modules: 1
stage3_modules: 1
feat_dim: 512
out_h: 7
out_w: 7
TF-NAS:
feat_dim: 512
drop_ratio: 0.2
out_h: 7
out_w: 7
ResNeSt:
depth: 50
drop_ratio: 0.4
feat_dim: 512
out_h: 7
out_w: 7
ReXNet:
input_ch: 16
final_ch: 180
width_mult: 1.0
depth_mult: 1.0
use_se: 0
se_ratio: 12
out_h: 7
out_w: 7
feat_dim: 512
dropout_ratio: 0.2
LightCNN:
depth: 29
out_h: 7
out_w: 7
feat_dim: 512
dropout_ratio: 0.2
RepVGG:
blocks1: 4
blocks2: 6
blocks3: 16
blocks4: 1
width1: 2
width2: 2
width3: 2
width4: 4
out_h: 7
out_w: 7
feat_dim: 512
SwinTransformer:
img_size: 224
patch_size: 4
in_chans: 3
embed_dim: 96
depths:
- 2
- 2
- 18
- 2
num_heads:
- 3
- 6
- 12
- 24
window_size: 7
mlp_ratio: 4.0
drop_rate: 0.0
drop_path_rate: 0.3