[Fix] Fix bisenetv2_fcn_ohem_4x4_1024x1024_160k_cityscapes.py (#1901)
MengzhangLI
GitHub
parent
5b2f19aae4
commit
3e3ed9ad67
|
|
@ -42,7 +42,7 @@ The low-level details and high-level semantics are both essential to the semanti
|
|||
| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download |
|
||||
| ---------------- | --------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ---------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
|
||||
| BiSeNetV2 | BiSeNetV2 | 1024x1024 | 160000 | 7.64 | 31.77 | 73.21 | 75.74 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/bisenetv2/bisenetv2_fcn_4x4_1024x1024_160k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv2/bisenetv2_fcn_4x4_1024x1024_160k_cityscapes/bisenetv2_fcn_4x4_1024x1024_160k_cityscapes_20210902_015551-bcf10f09.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv2/bisenetv2_fcn_4x4_1024x1024_160k_cityscapes/bisenetv2_fcn_4x4_1024x1024_160k_cityscapes_20210902_015551.log.json) |
|
||||
| BiSeNetV2 (OHEM) | BiSeNetV2 | 1024x1024 | 160000 | 7.64 | - | 73.57 | 75.80 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/bisenetv2/bisenetv2_fcn_ohem_4x4_1024x1024_160k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv2/bisenetv2_fcn_ohem_4x4_1024x1024_160k_cityscapes/bisenetv2_fcn_ohem_4x4_1024x1024_160k_cityscapes_20210902_112947-5f8103b4.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv2/bisenetv2_fcn_ohem_4x4_1024x1024_160k_cityscapes/bisenetv2_fcn_ohem_4x4_1024x1024_160k_cityscapes_20210902_112947.log.json) |
|
||||
| BiSeNetV2 (OHEM) | BiSeNetV2 | 1024x1024 | 160000 | 7.64 | - | 75.30 | 77.06 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/bisenetv2/bisenetv2_fcn_ohem_4x4_1024x1024_160k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv2/bisenetv2_fcn_ohem_4x4_1024x1024_160k_cityscapes/bisenetv2_fcn_ohem_4x4_1024x1024_160k_cityscapes_20220808_172324-8bf0aaba.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv2/bisenetv2_fcn_ohem_4x4_1024x1024_160k_cityscapes/bisenetv2_fcn_ohem_4x4_1024x1024_160k_cityscapes_20220808_172324.log.json) |
|
||||
| BiSeNetV2 (4x8) | BiSeNetV2 | 1024x1024 | 160000 | 15.05 | - | 75.76 | 77.79 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/bisenetv2/bisenetv2_fcn_4x8_1024x1024_160k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv2/bisenetv2_fcn_4x8_1024x1024_160k_cityscapes/bisenetv2_fcn_4x8_1024x1024_160k_cityscapes_20210903_000032-e1a2eed6.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv2/bisenetv2_fcn_4x8_1024x1024_160k_cityscapes/bisenetv2_fcn_4x8_1024x1024_160k_cityscapes_20210903_000032.log.json) |
|
||||
| BiSeNetV2 (FP16) | BiSeNetV2 | 1024x1024 | 160000 | 5.77 | 36.65 | 73.07 | 75.13 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/bisenetv2/bisenetv2_fcn_fp16_4x4_1024x1024_160k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv2/bisenetv2_fcn_fp16_4x4_1024x1024_160k_cityscapes/bisenetv2_fcn_fp16_4x4_1024x1024_160k_cityscapes_20210902_045942-b979777b.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv2/bisenetv2_fcn_fp16_4x4_1024x1024_160k_cityscapes/bisenetv2_fcn_fp16_4x4_1024x1024_160k_cityscapes_20210902_045942.log.json) |
|
||||
|
||||
|
|
|
|||
|
|
@ -45,10 +45,10 @@ Models:
|
|||
- Task: Semantic Segmentation
|
||||
Dataset: Cityscapes
|
||||
Metrics:
|
||||
mIoU: 73.57
|
||||
mIoU(ms+flip): 75.8
|
||||
mIoU: 75.3
|
||||
mIoU(ms+flip): 77.06
|
||||
Config: configs/bisenetv2/bisenetv2_fcn_ohem_4x4_1024x1024_160k_cityscapes.py
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv2/bisenetv2_fcn_ohem_4x4_1024x1024_160k_cityscapes/bisenetv2_fcn_ohem_4x4_1024x1024_160k_cityscapes_20210902_112947-5f8103b4.pth
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv2/bisenetv2_fcn_ohem_4x4_1024x1024_160k_cityscapes/bisenetv2_fcn_ohem_4x4_1024x1024_160k_cityscapes_20220808_172324-8bf0aaba.pth
|
||||
- Name: bisenetv2_fcn_4x8_1024x1024_160k_cityscapes
|
||||
In Collection: BiSeNetV2
|
||||
Metadata:
|
||||
|
|
|
|||
|
|
@ -3,7 +3,67 @@ _base_ = [
|
|||
'../_base_/datasets/cityscapes_1024x1024.py',
|
||||
'../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py'
|
||||
]
|
||||
sampler = dict(type='OHEMPixelSampler', thresh=0.7, min_kept=10000)
|
||||
# sampler = dict(type='OHEMPixelSampler', thresh=0.7, min_kept=10000)
|
||||
norm_cfg = dict(type='SyncBN', requires_grad=True)
|
||||
model = dict(
|
||||
decode_head=dict(
|
||||
sampler=dict(type='OHEMPixelSampler', thresh=0.7, min_kept=10000)),
|
||||
auxiliary_head=[
|
||||
dict(
|
||||
type='FCNHead',
|
||||
in_channels=16,
|
||||
channels=16,
|
||||
num_convs=2,
|
||||
num_classes=19,
|
||||
in_index=1,
|
||||
norm_cfg=norm_cfg,
|
||||
concat_input=False,
|
||||
align_corners=False,
|
||||
sampler=dict(type='OHEMPixelSampler', thresh=0.7, min_kept=10000),
|
||||
loss_decode=dict(
|
||||
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),
|
||||
dict(
|
||||
type='FCNHead',
|
||||
in_channels=32,
|
||||
channels=64,
|
||||
num_convs=2,
|
||||
num_classes=19,
|
||||
in_index=2,
|
||||
norm_cfg=norm_cfg,
|
||||
concat_input=False,
|
||||
align_corners=False,
|
||||
sampler=dict(type='OHEMPixelSampler', thresh=0.7, min_kept=10000),
|
||||
loss_decode=dict(
|
||||
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),
|
||||
dict(
|
||||
type='FCNHead',
|
||||
in_channels=64,
|
||||
channels=256,
|
||||
num_convs=2,
|
||||
num_classes=19,
|
||||
in_index=3,
|
||||
norm_cfg=norm_cfg,
|
||||
concat_input=False,
|
||||
align_corners=False,
|
||||
sampler=dict(type='OHEMPixelSampler', thresh=0.7, min_kept=10000),
|
||||
loss_decode=dict(
|
||||
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),
|
||||
dict(
|
||||
type='FCNHead',
|
||||
in_channels=128,
|
||||
channels=1024,
|
||||
num_convs=2,
|
||||
num_classes=19,
|
||||
in_index=4,
|
||||
norm_cfg=norm_cfg,
|
||||
concat_input=False,
|
||||
align_corners=False,
|
||||
sampler=dict(type='OHEMPixelSampler', thresh=0.7, min_kept=10000),
|
||||
loss_decode=dict(
|
||||
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),
|
||||
],
|
||||
)
|
||||
|
||||
lr_config = dict(warmup='linear', warmup_iters=1000)
|
||||
optimizer = dict(lr=0.05)
|
||||
data = dict(
|
||||
|
|
|
|||
Loading…
Reference in New Issue