mmsegmentation中config deeplabv3_r50的网络结构
时间: 2023-10-01 10:07:35 浏览: 74
mmsegmentation中config deeplabv3_r50的网络结构如下:
```python
norm_cfg = dict(type='BN', requires_grad=True)
model = dict(
type='EncoderDecoder',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch'),
decode_head=dict(
type='ASPPHead',
in_channels=2048,
in_index=3,
channels=512,
dilations=(1, 12, 24, 36),
dropout_ratio=0.1,
num_classes=19,
norm_cfg=norm_cfg,
align_corners=False,
loss_decode=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),
auxiliary_head=dict(
type='FCNHead',
in_channels=1024,
in_index=2,
channels=256,
num_convs=1,
concat_input=False,
dropout_ratio=0.1,
num_classes=19,
norm_cfg=norm_cfg,
align_corners=False,
loss_decode=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)))
```
其中,该模型使用ResNet-50作为backbone,ASPPHead作为decode_head,FCNHead作为auxiliary_head。最终输出19个类别的分割结果。