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mmsegmentation | mmsegmentation-master/configs/ocrnet/ocrnet_hr18_512x1024_160k_cityscapes.py | _base_ = [
'../_base_/models/ocrnet_hr18.py', '../_base_/datasets/cityscapes.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py'
]
| 163 | 31.8 | 74 | py |
mmsegmentation | mmsegmentation-master/configs/ocrnet/ocrnet_hr18_512x1024_40k_cityscapes.py | _base_ = [
'../_base_/models/ocrnet_hr18.py', '../_base_/datasets/cityscapes.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py'
]
| 162 | 31.6 | 74 | py |
mmsegmentation | mmsegmentation-master/configs/ocrnet/ocrnet_hr18_512x1024_80k_cityscapes.py | _base_ = [
'../_base_/models/ocrnet_hr18.py', '../_base_/datasets/cityscapes.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
]
| 162 | 31.6 | 74 | py |
mmsegmentation | mmsegmentation-master/configs/ocrnet/ocrnet_hr18_512x512_160k_ade20k.py | _base_ = [
'../_base_/models/ocrnet_hr18.py', '../_base_/datasets/ade20k.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py'
]
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(decode_head=[
dict(
type='FCNHead',
in_channels=[18, 36, 72, 144],
channels=sum([18, 36, 72, 144]),
in_index=(0, 1, 2, 3),
input_transform='resize_concat',
kernel_size=1,
num_convs=1,
concat_input=False,
dropout_ratio=-1,
num_classes=150,
norm_cfg=norm_cfg,
align_corners=False,
loss_decode=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
dict(
type='OCRHead',
in_channels=[18, 36, 72, 144],
in_index=(0, 1, 2, 3),
input_transform='resize_concat',
channels=512,
ocr_channels=256,
dropout_ratio=-1,
num_classes=150,
norm_cfg=norm_cfg,
align_corners=False,
loss_decode=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),
])
| 1,107 | 29.777778 | 74 | py |
mmsegmentation | mmsegmentation-master/configs/ocrnet/ocrnet_hr18_512x512_20k_voc12aug.py | _base_ = [
'../_base_/models/ocrnet_hr18.py',
'../_base_/datasets/pascal_voc12_aug.py', '../_base_/default_runtime.py',
'../_base_/schedules/schedule_20k.py'
]
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(decode_head=[
dict(
type='FCNHead',
in_channels=[18, 36, 72, 144],
channels=sum([18, 36, 72, 144]),
in_index=(0, 1, 2, 3),
input_transform='resize_concat',
kernel_size=1,
num_convs=1,
concat_input=False,
dropout_ratio=-1,
num_classes=21,
norm_cfg=norm_cfg,
align_corners=False,
loss_decode=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
dict(
type='OCRHead',
in_channels=[18, 36, 72, 144],
in_index=(0, 1, 2, 3),
input_transform='resize_concat',
channels=512,
ocr_channels=256,
dropout_ratio=-1,
num_classes=21,
norm_cfg=norm_cfg,
align_corners=False,
loss_decode=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),
])
| 1,118 | 29.243243 | 77 | py |
mmsegmentation | mmsegmentation-master/configs/ocrnet/ocrnet_hr18_512x512_40k_voc12aug.py | _base_ = [
'../_base_/models/ocrnet_hr18.py',
'../_base_/datasets/pascal_voc12_aug.py', '../_base_/default_runtime.py',
'../_base_/schedules/schedule_40k.py'
]
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(decode_head=[
dict(
type='FCNHead',
in_channels=[18, 36, 72, 144],
channels=sum([18, 36, 72, 144]),
in_index=(0, 1, 2, 3),
input_transform='resize_concat',
kernel_size=1,
num_convs=1,
concat_input=False,
dropout_ratio=-1,
num_classes=21,
norm_cfg=norm_cfg,
align_corners=False,
loss_decode=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
dict(
type='OCRHead',
in_channels=[18, 36, 72, 144],
in_index=(0, 1, 2, 3),
input_transform='resize_concat',
channels=512,
ocr_channels=256,
dropout_ratio=-1,
num_classes=21,
norm_cfg=norm_cfg,
align_corners=False,
loss_decode=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),
])
| 1,118 | 29.243243 | 77 | py |
mmsegmentation | mmsegmentation-master/configs/ocrnet/ocrnet_hr18_512x512_80k_ade20k.py | _base_ = [
'../_base_/models/ocrnet_hr18.py', '../_base_/datasets/ade20k.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
]
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(decode_head=[
dict(
type='FCNHead',
in_channels=[18, 36, 72, 144],
channels=sum([18, 36, 72, 144]),
in_index=(0, 1, 2, 3),
input_transform='resize_concat',
kernel_size=1,
num_convs=1,
concat_input=False,
dropout_ratio=-1,
num_classes=150,
norm_cfg=norm_cfg,
align_corners=False,
loss_decode=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
dict(
type='OCRHead',
in_channels=[18, 36, 72, 144],
in_index=(0, 1, 2, 3),
input_transform='resize_concat',
channels=512,
ocr_channels=256,
dropout_ratio=-1,
num_classes=150,
norm_cfg=norm_cfg,
align_corners=False,
loss_decode=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),
])
| 1,106 | 29.75 | 74 | py |
mmsegmentation | mmsegmentation-master/configs/ocrnet/ocrnet_hr18s_512x1024_160k_cityscapes.py | _base_ = './ocrnet_hr18_512x1024_160k_cityscapes.py'
model = dict(
pretrained='open-mmlab://msra/hrnetv2_w18_small',
backbone=dict(
extra=dict(
stage1=dict(num_blocks=(2, )),
stage2=dict(num_blocks=(2, 2)),
stage3=dict(num_modules=3, num_blocks=(2, 2, 2)),
stage4=dict(num_modules=2, num_blocks=(2, 2, 2, 2)))))
| 376 | 36.7 | 66 | py |
mmsegmentation | mmsegmentation-master/configs/ocrnet/ocrnet_hr18s_512x1024_40k_cityscapes.py | _base_ = './ocrnet_hr18_512x1024_40k_cityscapes.py'
model = dict(
pretrained='open-mmlab://msra/hrnetv2_w18_small',
backbone=dict(
extra=dict(
stage1=dict(num_blocks=(2, )),
stage2=dict(num_blocks=(2, 2)),
stage3=dict(num_modules=3, num_blocks=(2, 2, 2)),
stage4=dict(num_modules=2, num_blocks=(2, 2, 2, 2)))))
| 375 | 36.6 | 66 | py |
mmsegmentation | mmsegmentation-master/configs/ocrnet/ocrnet_hr18s_512x1024_80k_cityscapes.py | _base_ = './ocrnet_hr18_512x1024_80k_cityscapes.py'
model = dict(
pretrained='open-mmlab://msra/hrnetv2_w18_small',
backbone=dict(
extra=dict(
stage1=dict(num_blocks=(2, )),
stage2=dict(num_blocks=(2, 2)),
stage3=dict(num_modules=3, num_blocks=(2, 2, 2)),
stage4=dict(num_modules=2, num_blocks=(2, 2, 2, 2)))))
| 375 | 36.6 | 66 | py |
mmsegmentation | mmsegmentation-master/configs/ocrnet/ocrnet_hr18s_512x512_160k_ade20k.py | _base_ = './ocrnet_hr18_512x512_160k_ade20k.py'
model = dict(
pretrained='open-mmlab://msra/hrnetv2_w18_small',
backbone=dict(
extra=dict(
stage1=dict(num_blocks=(2, )),
stage2=dict(num_blocks=(2, 2)),
stage3=dict(num_modules=3, num_blocks=(2, 2, 2)),
stage4=dict(num_modules=2, num_blocks=(2, 2, 2, 2)))))
| 371 | 36.2 | 66 | py |
mmsegmentation | mmsegmentation-master/configs/ocrnet/ocrnet_hr18s_512x512_20k_voc12aug.py | _base_ = './ocrnet_hr18_512x512_20k_voc12aug.py'
model = dict(
pretrained='open-mmlab://msra/hrnetv2_w18_small',
backbone=dict(
extra=dict(
stage1=dict(num_blocks=(2, )),
stage2=dict(num_blocks=(2, 2)),
stage3=dict(num_modules=3, num_blocks=(2, 2, 2)),
stage4=dict(num_modules=2, num_blocks=(2, 2, 2, 2)))))
| 372 | 36.3 | 66 | py |
mmsegmentation | mmsegmentation-master/configs/ocrnet/ocrnet_hr18s_512x512_40k_voc12aug.py | _base_ = './ocrnet_hr18_512x512_40k_voc12aug.py'
model = dict(
pretrained='open-mmlab://msra/hrnetv2_w18_small',
backbone=dict(
extra=dict(
stage1=dict(num_blocks=(2, )),
stage2=dict(num_blocks=(2, 2)),
stage3=dict(num_modules=3, num_blocks=(2, 2, 2)),
stage4=dict(num_modules=2, num_blocks=(2, 2, 2, 2)))))
| 372 | 36.3 | 66 | py |
mmsegmentation | mmsegmentation-master/configs/ocrnet/ocrnet_hr18s_512x512_80k_ade20k.py | _base_ = './ocrnet_hr18_512x512_80k_ade20k.py'
model = dict(
pretrained='open-mmlab://msra/hrnetv2_w18_small',
backbone=dict(
extra=dict(
stage1=dict(num_blocks=(2, )),
stage2=dict(num_blocks=(2, 2)),
stage3=dict(num_modules=3, num_blocks=(2, 2, 2)),
stage4=dict(num_modules=2, num_blocks=(2, 2, 2, 2)))))
| 370 | 36.1 | 66 | py |
mmsegmentation | mmsegmentation-master/configs/ocrnet/ocrnet_hr48_512x1024_160k_cityscapes.py | _base_ = './ocrnet_hr18_512x1024_160k_cityscapes.py'
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
pretrained='open-mmlab://msra/hrnetv2_w48',
backbone=dict(
extra=dict(
stage2=dict(num_channels=(48, 96)),
stage3=dict(num_channels=(48, 96, 192)),
stage4=dict(num_channels=(48, 96, 192, 384)))),
decode_head=[
dict(
type='FCNHead',
in_channels=[48, 96, 192, 384],
channels=sum([48, 96, 192, 384]),
input_transform='resize_concat',
in_index=(0, 1, 2, 3),
kernel_size=1,
num_convs=1,
norm_cfg=norm_cfg,
concat_input=False,
dropout_ratio=-1,
num_classes=19,
align_corners=False,
loss_decode=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
dict(
type='OCRHead',
in_channels=[48, 96, 192, 384],
channels=512,
ocr_channels=256,
input_transform='resize_concat',
in_index=(0, 1, 2, 3),
norm_cfg=norm_cfg,
dropout_ratio=-1,
num_classes=19,
align_corners=False,
loss_decode=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0))
])
| 1,370 | 33.275 | 78 | py |
mmsegmentation | mmsegmentation-master/configs/ocrnet/ocrnet_hr48_512x1024_40k_cityscapes.py | _base_ = './ocrnet_hr18_512x1024_40k_cityscapes.py'
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
pretrained='open-mmlab://msra/hrnetv2_w48',
backbone=dict(
extra=dict(
stage2=dict(num_channels=(48, 96)),
stage3=dict(num_channels=(48, 96, 192)),
stage4=dict(num_channels=(48, 96, 192, 384)))),
decode_head=[
dict(
type='FCNHead',
in_channels=[48, 96, 192, 384],
channels=sum([48, 96, 192, 384]),
input_transform='resize_concat',
in_index=(0, 1, 2, 3),
kernel_size=1,
num_convs=1,
norm_cfg=norm_cfg,
concat_input=False,
dropout_ratio=-1,
num_classes=19,
align_corners=False,
loss_decode=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
dict(
type='OCRHead',
in_channels=[48, 96, 192, 384],
channels=512,
ocr_channels=256,
input_transform='resize_concat',
in_index=(0, 1, 2, 3),
norm_cfg=norm_cfg,
dropout_ratio=-1,
num_classes=19,
align_corners=False,
loss_decode=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0))
])
| 1,369 | 33.25 | 78 | py |
mmsegmentation | mmsegmentation-master/configs/ocrnet/ocrnet_hr48_512x1024_80k_cityscapes.py | _base_ = './ocrnet_hr18_512x1024_80k_cityscapes.py'
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
pretrained='open-mmlab://msra/hrnetv2_w48',
backbone=dict(
extra=dict(
stage2=dict(num_channels=(48, 96)),
stage3=dict(num_channels=(48, 96, 192)),
stage4=dict(num_channels=(48, 96, 192, 384)))),
decode_head=[
dict(
type='FCNHead',
in_channels=[48, 96, 192, 384],
channels=sum([48, 96, 192, 384]),
input_transform='resize_concat',
in_index=(0, 1, 2, 3),
kernel_size=1,
num_convs=1,
norm_cfg=norm_cfg,
concat_input=False,
dropout_ratio=-1,
num_classes=19,
align_corners=False,
loss_decode=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
dict(
type='OCRHead',
in_channels=[48, 96, 192, 384],
channels=512,
ocr_channels=256,
input_transform='resize_concat',
in_index=(0, 1, 2, 3),
norm_cfg=norm_cfg,
dropout_ratio=-1,
num_classes=19,
align_corners=False,
loss_decode=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0))
])
| 1,369 | 33.25 | 78 | py |
mmsegmentation | mmsegmentation-master/configs/ocrnet/ocrnet_hr48_512x512_160k_ade20k.py | _base_ = './ocrnet_hr18_512x512_160k_ade20k.py'
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
pretrained='open-mmlab://msra/hrnetv2_w48',
backbone=dict(
extra=dict(
stage2=dict(num_channels=(48, 96)),
stage3=dict(num_channels=(48, 96, 192)),
stage4=dict(num_channels=(48, 96, 192, 384)))),
decode_head=[
dict(
type='FCNHead',
in_channels=[48, 96, 192, 384],
channels=sum([48, 96, 192, 384]),
input_transform='resize_concat',
in_index=(0, 1, 2, 3),
kernel_size=1,
num_convs=1,
norm_cfg=norm_cfg,
concat_input=False,
dropout_ratio=-1,
num_classes=150,
align_corners=False,
loss_decode=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
dict(
type='OCRHead',
in_channels=[48, 96, 192, 384],
channels=512,
ocr_channels=256,
input_transform='resize_concat',
in_index=(0, 1, 2, 3),
norm_cfg=norm_cfg,
dropout_ratio=-1,
num_classes=150,
align_corners=False,
loss_decode=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0))
])
| 1,367 | 33.2 | 78 | py |
mmsegmentation | mmsegmentation-master/configs/ocrnet/ocrnet_hr48_512x512_20k_voc12aug.py | _base_ = './ocrnet_hr18_512x512_20k_voc12aug.py'
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
pretrained='open-mmlab://msra/hrnetv2_w48',
backbone=dict(
extra=dict(
stage2=dict(num_channels=(48, 96)),
stage3=dict(num_channels=(48, 96, 192)),
stage4=dict(num_channels=(48, 96, 192, 384)))),
decode_head=[
dict(
type='FCNHead',
in_channels=[48, 96, 192, 384],
channels=sum([48, 96, 192, 384]),
input_transform='resize_concat',
in_index=(0, 1, 2, 3),
kernel_size=1,
num_convs=1,
norm_cfg=norm_cfg,
concat_input=False,
dropout_ratio=-1,
num_classes=21,
align_corners=False,
loss_decode=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
dict(
type='OCRHead',
in_channels=[48, 96, 192, 384],
channels=512,
ocr_channels=256,
input_transform='resize_concat',
in_index=(0, 1, 2, 3),
norm_cfg=norm_cfg,
dropout_ratio=-1,
num_classes=21,
align_corners=False,
loss_decode=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0))
])
| 1,366 | 33.175 | 78 | py |
mmsegmentation | mmsegmentation-master/configs/ocrnet/ocrnet_hr48_512x512_40k_voc12aug.py | _base_ = './ocrnet_hr18_512x512_40k_voc12aug.py'
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
pretrained='open-mmlab://msra/hrnetv2_w48',
backbone=dict(
extra=dict(
stage2=dict(num_channels=(48, 96)),
stage3=dict(num_channels=(48, 96, 192)),
stage4=dict(num_channels=(48, 96, 192, 384)))),
decode_head=[
dict(
type='FCNHead',
in_channels=[48, 96, 192, 384],
channels=sum([48, 96, 192, 384]),
input_transform='resize_concat',
in_index=(0, 1, 2, 3),
kernel_size=1,
num_convs=1,
norm_cfg=norm_cfg,
concat_input=False,
dropout_ratio=-1,
num_classes=21,
align_corners=False,
loss_decode=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
dict(
type='OCRHead',
in_channels=[48, 96, 192, 384],
channels=512,
ocr_channels=256,
input_transform='resize_concat',
in_index=(0, 1, 2, 3),
norm_cfg=norm_cfg,
dropout_ratio=-1,
num_classes=21,
align_corners=False,
loss_decode=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0))
])
| 1,366 | 33.175 | 78 | py |
mmsegmentation | mmsegmentation-master/configs/ocrnet/ocrnet_hr48_512x512_80k_ade20k.py | _base_ = './ocrnet_hr18_512x512_80k_ade20k.py'
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
pretrained='open-mmlab://msra/hrnetv2_w48',
backbone=dict(
extra=dict(
stage2=dict(num_channels=(48, 96)),
stage3=dict(num_channels=(48, 96, 192)),
stage4=dict(num_channels=(48, 96, 192, 384)))),
decode_head=[
dict(
type='FCNHead',
in_channels=[48, 96, 192, 384],
channels=sum([48, 96, 192, 384]),
input_transform='resize_concat',
in_index=(0, 1, 2, 3),
kernel_size=1,
num_convs=1,
norm_cfg=norm_cfg,
concat_input=False,
dropout_ratio=-1,
num_classes=150,
align_corners=False,
loss_decode=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
dict(
type='OCRHead',
in_channels=[48, 96, 192, 384],
channels=512,
ocr_channels=256,
input_transform='resize_concat',
in_index=(0, 1, 2, 3),
norm_cfg=norm_cfg,
dropout_ratio=-1,
num_classes=150,
align_corners=False,
loss_decode=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0))
])
| 1,366 | 33.175 | 78 | py |
mmsegmentation | mmsegmentation-master/configs/ocrnet/ocrnet_r101-d8_512x1024_40k_b16_cityscapes.py | _base_ = [
'../_base_/models/ocrnet_r50-d8.py', '../_base_/datasets/cityscapes.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py'
]
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
optimizer = dict(lr=0.02)
lr_config = dict(min_lr=2e-4)
| 300 | 36.625 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/ocrnet/ocrnet_r101-d8_512x1024_40k_b8_cityscapes.py | _base_ = [
'../_base_/models/ocrnet_r50-d8.py', '../_base_/datasets/cityscapes.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py'
]
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
| 244 | 39.833333 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/ocrnet/ocrnet_r101-d8_512x1024_80k_b16_cityscapes.py | _base_ = [
'../_base_/models/ocrnet_r50-d8.py', '../_base_/datasets/cityscapes.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
]
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
optimizer = dict(lr=0.02)
lr_config = dict(min_lr=2e-4)
| 300 | 36.625 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/point_rend/README.md | # PointRend
[PointRend: Image Segmentation as Rendering](https://arxiv.org/abs/1912.08193)
## Introduction
<!-- [ALGORITHM] -->
<a href="https://github.com/facebookresearch/detectron2/tree/master/projects/PointRend">Official Repo</a>
<a href="https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/point_head.py#L36">Code Snippet</a>
## Abstract
<!-- [ABSTRACT] -->
We present a new method for efficient high-quality image segmentation of objects and scenes. By analogizing classical computer graphics methods for efficient rendering with over- and undersampling challenges faced in pixel labeling tasks, we develop a unique perspective of image segmentation as a rendering problem. From this vantage, we present the PointRend (Point-based Rendering) neural network module: a module that performs point-based segmentation predictions at adaptively selected locations based on an iterative subdivision algorithm. PointRend can be flexibly applied to both instance and semantic segmentation tasks by building on top of existing state-of-the-art models. While many concrete implementations of the general idea are possible, we show that a simple design already achieves excellent results. Qualitatively, PointRend outputs crisp object boundaries in regions that are over-smoothed by previous methods. Quantitatively, PointRend yields significant gains on COCO and Cityscapes, for both instance and semantic segmentation. PointRend's efficiency enables output resolutions that are otherwise impractical in terms of memory or computation compared to existing approaches. Code has been made available at [this https URL](https://github.com/facebookresearch/detectron2/tree/main/projects/PointRend).
<!-- [IMAGE] -->
<div align=center>
<img src="https://user-images.githubusercontent.com/24582831/142902293-5db49cdd-4b1b-4940-9067-2acd6196c700.png" width="60%"/>
</div>
## Citation
```bibtex
@inproceedings{kirillov2020pointrend,
title={Pointrend: Image segmentation as rendering},
author={Kirillov, Alexander and Wu, Yuxin and He, Kaiming and Girshick, Ross},
booktitle={Proceedings of the IEEE/CVF conference on computer vision and pattern recognition},
pages={9799--9808},
year={2020}
}
```
## Results and models
### Cityscapes
| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download |
| --------- | -------- | --------- | ------: | -------: | -------------- | ----: | ------------- | ------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| PointRend | R-50 | 512x1024 | 80000 | 3.1 | 8.48 | 76.47 | 78.13 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/point_rend/pointrend_r50_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/point_rend/pointrend_r50_512x1024_80k_cityscapes/pointrend_r50_512x1024_80k_cityscapes_20200711_015821-bb1ff523.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/point_rend/pointrend_r50_512x1024_80k_cityscapes/pointrend_r50_512x1024_80k_cityscapes-20200715_214714.log.json) |
| PointRend | R-101 | 512x1024 | 80000 | 4.2 | 7.00 | 78.30 | 79.97 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/point_rend/pointrend_r101_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/point_rend/pointrend_r101_512x1024_80k_cityscapes/pointrend_r101_512x1024_80k_cityscapes_20200711_170850-d0ca84be.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/point_rend/pointrend_r101_512x1024_80k_cityscapes/pointrend_r101_512x1024_80k_cityscapes-20200715_214824.log.json) |
### ADE20K
| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download |
| --------- | -------- | --------- | ------: | -------: | -------------- | ----: | ------------- | --------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| PointRend | R-50 | 512x512 | 160000 | 5.1 | 17.31 | 37.64 | 39.17 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/point_rend/pointrend_r50_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/point_rend/pointrend_r50_512x512_160k_ade20k/pointrend_r50_512x512_160k_ade20k_20200807_232644-ac3febf2.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/point_rend/pointrend_r50_512x512_160k_ade20k/pointrend_r50_512x512_160k_ade20k-20200807_232644.log.json) |
| PointRend | R-101 | 512x512 | 160000 | 6.1 | 15.50 | 40.02 | 41.60 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/point_rend/pointrend_r101_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/point_rend/pointrend_r101_512x512_160k_ade20k/pointrend_r101_512x512_160k_ade20k_20200808_030852-8834902a.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/point_rend/pointrend_r101_512x512_160k_ade20k/pointrend_r101_512x512_160k_ade20k-20200808_030852.log.json) |
| 6,883 | 131.384615 | 1,326 | md |
mmsegmentation | mmsegmentation-master/configs/point_rend/point_rend.yml | Collections:
- Name: PointRend
Metadata:
Training Data:
- Cityscapes
- ADE20K
Paper:
URL: https://arxiv.org/abs/1912.08193
Title: 'PointRend: Image Segmentation as Rendering'
README: configs/point_rend/README.md
Code:
URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/point_head.py#L36
Version: v0.17.0
Converted From:
Code: https://github.com/facebookresearch/detectron2/tree/master/projects/PointRend
Models:
- Name: pointrend_r50_512x1024_80k_cityscapes
In Collection: PointRend
Metadata:
backbone: R-50
crop size: (512,1024)
lr schd: 80000
inference time (ms/im):
- value: 117.92
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,1024)
Training Memory (GB): 3.1
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 76.47
mIoU(ms+flip): 78.13
Config: configs/point_rend/pointrend_r50_512x1024_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/point_rend/pointrend_r50_512x1024_80k_cityscapes/pointrend_r50_512x1024_80k_cityscapes_20200711_015821-bb1ff523.pth
- Name: pointrend_r101_512x1024_80k_cityscapes
In Collection: PointRend
Metadata:
backbone: R-101
crop size: (512,1024)
lr schd: 80000
inference time (ms/im):
- value: 142.86
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,1024)
Training Memory (GB): 4.2
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 78.3
mIoU(ms+flip): 79.97
Config: configs/point_rend/pointrend_r101_512x1024_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/point_rend/pointrend_r101_512x1024_80k_cityscapes/pointrend_r101_512x1024_80k_cityscapes_20200711_170850-d0ca84be.pth
- Name: pointrend_r50_512x512_160k_ade20k
In Collection: PointRend
Metadata:
backbone: R-50
crop size: (512,512)
lr schd: 160000
inference time (ms/im):
- value: 57.77
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 5.1
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 37.64
mIoU(ms+flip): 39.17
Config: configs/point_rend/pointrend_r50_512x512_160k_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/point_rend/pointrend_r50_512x512_160k_ade20k/pointrend_r50_512x512_160k_ade20k_20200807_232644-ac3febf2.pth
- Name: pointrend_r101_512x512_160k_ade20k
In Collection: PointRend
Metadata:
backbone: R-101
crop size: (512,512)
lr schd: 160000
inference time (ms/im):
- value: 64.52
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 6.1
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 40.02
mIoU(ms+flip): 41.6
Config: configs/point_rend/pointrend_r101_512x512_160k_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/point_rend/pointrend_r101_512x512_160k_ade20k/pointrend_r101_512x512_160k_ade20k_20200808_030852-8834902a.pth
| 3,296 | 30.4 | 179 | yml |
mmsegmentation | mmsegmentation-master/configs/point_rend/pointrend_r101_512x1024_80k_cityscapes.py | _base_ = './pointrend_r50_512x1024_80k_cityscapes.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
| 134 | 44 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/point_rend/pointrend_r101_512x512_160k_ade20k.py | _base_ = './pointrend_r50_512x512_160k_ade20k.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
| 130 | 42.666667 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/point_rend/pointrend_r50_512x1024_80k_cityscapes.py | _base_ = [
'../_base_/models/pointrend_r50.py', '../_base_/datasets/cityscapes.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
]
lr_config = dict(warmup='linear', warmup_iters=200)
| 216 | 35.166667 | 76 | py |
mmsegmentation | mmsegmentation-master/configs/point_rend/pointrend_r50_512x512_160k_ade20k.py | _base_ = [
'../_base_/models/pointrend_r50.py', '../_base_/datasets/ade20k.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py'
]
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(decode_head=[
dict(
type='FPNHead',
in_channels=[256, 256, 256, 256],
in_index=[0, 1, 2, 3],
feature_strides=[4, 8, 16, 32],
channels=128,
dropout_ratio=-1,
num_classes=150,
norm_cfg=norm_cfg,
align_corners=False,
loss_decode=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),
dict(
type='PointHead',
in_channels=[256],
in_index=[0],
channels=256,
num_fcs=3,
coarse_pred_each_layer=True,
dropout_ratio=-1,
num_classes=150,
align_corners=False,
loss_decode=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0))
])
lr_config = dict(warmup='linear', warmup_iters=200)
| 1,014 | 29.757576 | 74 | py |
mmsegmentation | mmsegmentation-master/configs/poolformer/README.md | # PoolFormer
[MetaFormer is Actually What You Need for Vision](https://arxiv.org/abs/2111.11418)
## Introduction
<!-- [BACKBONE] -->
<a href="https://github.com/sail-sg/poolformer/tree/main/segmentation">Official Repo</a>
<a href="https://github.com/open-mmlab/mmclassification/blob/v0.23.0/mmcls/models/backbones/poolformer.py#L198">Code Snippet</a>
## Abstract
<!-- [ABSTRACT] -->
Transformers have shown great potential in computer vision tasks. A common belief is their attention-based token mixer module contributes most to their competence. However, recent works show the attention-based module in transformers can be replaced by spatial MLPs and the resulted models still perform quite well. Based on this observation, we hypothesize that the general architecture of the transformers, instead of the specific token mixer module, is more essential to the model's performance. To verify this, we deliberately replace the attention module in transformers with an embarrassingly simple spatial pooling operator to conduct only the most basic token mixing. Surprisingly, we observe that the derived model, termed as PoolFormer, achieves competitive performance on multiple computer vision tasks. For example, on ImageNet-1K, PoolFormer achieves 82.1% top-1 accuracy, surpassing well-tuned vision transformer/MLP-like baselines DeiT-B/ResMLP-B24 by 0.3%/1.1% accuracy with 35%/52% fewer parameters and 48%/60% fewer MACs. The effectiveness of PoolFormer verifies our hypothesis and urges us to initiate the concept of "MetaFormer", a general architecture abstracted from transformers without specifying the token mixer. Based on the extensive experiments, we argue that MetaFormer is the key player in achieving superior results for recent transformer and MLP-like models on vision tasks. This work calls for more future research dedicated to improving MetaFormer instead of focusing on the token mixer modules. Additionally, our proposed PoolFormer could serve as a starting baseline for future MetaFormer architecture design. Code is available at [this https URL](https://github.com/sail-sg/poolformer)
<!-- [IMAGE] -->
<div align=center>
<img src="https://user-images.githubusercontent.com/15921929/144710761-1635f59a-abde-4946-984c-a2c3f22a19d2.png" width="70%"/>
</div>
## Citation
```bibtex
@inproceedings{yu2022metaformer,
title={Metaformer is actually what you need for vision},
author={Yu, Weihao and Luo, Mi and Zhou, Pan and Si, Chenyang and Zhou, Yichen and Wang, Xinchao and Feng, Jiashi and Yan, Shuicheng},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages={10819--10829},
year={2022}
}
```
### Usage
- PoolFormer backbone needs to install [MMClassification](https://github.com/open-mmlab/mmclassification) first, which has abundant backbones for downstream tasks.
```shell
pip install mmcls>=0.23.0
```
- The pretrained models could also be downloaded from [PoolFormer config of MMClassification](https://github.com/open-mmlab/mmclassification/tree/master/configs/poolformer).
## Results and models
### ADE20K
| Method | Backbone | Crop Size | pretrain | Batch Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | mIoU\* | mIoU\*(ms+flip) | config | download |
| ------ | -------------- | --------- | ----------- | ---------- | ------- | -------- | -------------- | ----- | ------------: | ------ | --------------: | ---------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| FPN | PoolFormer-S12 | 512x512 | ImageNet-1K | 32 | 40000 | 4.17 | 23.48 | 36.68 | 38.22 | 37.07 | 38.44 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/poolformer/fpn_poolformer_s12_8x4_512x512_40k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/poolformer/fpn_poolformer_s12_8x4_512x512_40k_ade20k/fpn_poolformer_s12_8x4_512x512_40k_ade20k_20220501_115154-b5aa2f49.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/poolformer/fpn_poolformer_s12_8x4_512x512_40k_ade20k/fpn_poolformer_s12_8x4_512x512_40k_ade20k_20220501_115154.log.json) |
| FPN | PoolFormer-S24 | 512x512 | ImageNet-1K | 32 | 40000 | 5.47 | 15.74 | 40.12 | 40.97 | 40.36 | 41.08 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/poolformer/fpn_poolformer_s24_8x4_512x512_40k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/poolformer/fpn_poolformer_s24_8x4_512x512_40k_ade20k/fpn_poolformer_s24_8x4_512x512_40k_ade20k_20220503_222049-394a7cf7.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/poolformer/fpn_poolformer_s24_8x4_512x512_40k_ade20k/fpn_poolformer_s24_8x4_512x512_40k_ade20k_20220503_222049.log.json) |
| FPN | PoolFormer-S36 | 512x512 | ImageNet-1K | 32 | 40000 | 6.77 | 11.34 | 41.61 | 42.61 | 41.81 | 42.72 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/poolformer/fpn_poolformer_s36_8x4_512x512_40k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/poolformer/fpn_poolformer_s36_8x4_512x512_40k_ade20k/fpn_poolformer_s36_8x4_512x512_40k_ade20k_20220501_151122-b47e607d.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/poolformer/fpn_poolformer_s36_8x4_512x512_40k_ade20k/fpn_poolformer_s36_8x4_512x512_40k_ade20k_20220501_151122.log.json) |
| FPN | PoolFormer-M36 | 512x512 | ImageNet-1K | 32 | 40000 | 8.59 | 8.97 | 41.95 | 43.24 | 42.35 | 43.34 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/poolformer/fpn_poolformer_m36_8x4_512x512_40k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/poolformer/fpn_poolformer_m36_8x4_512x512_40k_ade20k/fpn_poolformer_m36_8x4_512x512_40k_ade20k_20220501_164230-3dc83921.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/poolformer/fpn_poolformer_m36_8x4_512x512_40k_ade20k/fpn_poolformer_m36_8x4_512x512_40k_ade20k_20220501_164230.log.json) |
| FPN | PoolFormer-M48 | 512x512 | ImageNet-1K | 32 | 40000 | 10.48 | 6.69 | 42.43 | 43.60 | 42.76 | 43.57 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/poolformer/fpn_poolformer_m48_8x4_512x512_40k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/poolformer/fpn_poolformer_m48_8x4_512x512_40k_ade20k/fpn_poolformer_m48_8x4_512x512_40k_ade20k_20220504_003923-64168d3b.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/poolformer/fpn_poolformer_m48_8x4_512x512_40k_ade20k/fpn_poolformer_m48_8x4_512x512_40k_ade20k_20220504_003923.log.json) |
Note:
- We replace `AlignedResize` in original PoolFormer implementation to `Resize + ResizeToMultiple`.
- `mIoU` with * is collected when `Resize + ResizeToMultiple` is adopted in `test_pipeline`, so do `mIoU` in logs.
| 7,922 | 122.796875 | 1,722 | md |
mmsegmentation | mmsegmentation-master/configs/poolformer/fpn_poolformer_m36_8x4_512x512_40k_ade20k.py | _base_ = './fpn_poolformer_s12_8x4_512x512_40k_ade20k.py'
checkpoint_file = 'https://download.openmmlab.com/mmclassification/v0/poolformer/poolformer-m36_3rdparty_32xb128_in1k_20220414-c55e0949.pth' # noqa
# model settings
model = dict(
backbone=dict(
arch='m36',
init_cfg=dict(
type='Pretrained', checkpoint=checkpoint_file,
prefix='backbone.')),
neck=dict(in_channels=[96, 192, 384, 768]))
| 442 | 35.916667 | 148 | py |
mmsegmentation | mmsegmentation-master/configs/poolformer/fpn_poolformer_m48_8x4_512x512_40k_ade20k.py | _base_ = './fpn_poolformer_s12_8x4_512x512_40k_ade20k.py'
checkpoint_file = 'https://download.openmmlab.com/mmclassification/v0/poolformer/poolformer-m48_3rdparty_32xb128_in1k_20220414-9378f3eb.pth' # noqa
# model settings
model = dict(
backbone=dict(
arch='m48',
init_cfg=dict(
type='Pretrained', checkpoint=checkpoint_file,
prefix='backbone.')),
neck=dict(in_channels=[96, 192, 384, 768]))
| 442 | 35.916667 | 148 | py |
mmsegmentation | mmsegmentation-master/configs/poolformer/fpn_poolformer_s12_8x4_512x512_40k_ade20k.py | _base_ = [
'../_base_/models/fpn_poolformer_s12.py', '../_base_/default_runtime.py',
'../_base_/schedules/schedule_40k.py'
]
# model settings
model = dict(
neck=dict(in_channels=[64, 128, 320, 512]),
decode_head=dict(num_classes=150))
# optimizer
optimizer = dict(_delete_=True, type='AdamW', lr=0.0002, weight_decay=0.0001)
optimizer_config = dict()
# learning policy
lr_config = dict(policy='poly', power=0.9, min_lr=0.0, by_epoch=False)
# dataset settings
dataset_type = 'ADE20KDataset'
data_root = 'data/ade/ADEChallengeData2016'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
crop_size = (512, 512)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', reduce_zero_label=True),
dict(type='Resize', img_scale=(2048, 512), ratio_range=(0.5, 2.0)),
dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
dict(type='RandomFlip', prob=0.5),
dict(type='PhotoMetricDistortion'),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size=crop_size, pad_val=0, seg_pad_val=255),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_semantic_seg']),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(2048, 512),
# img_ratios=[0.5, 0.75, 1.0, 1.25, 1.5, 1.75],
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='ResizeToMultiple', size_divisor=32),
dict(type='RandomFlip'),
dict(type='Normalize', **img_norm_cfg),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img']),
])
]
data = dict(
samples_per_gpu=4,
workers_per_gpu=4,
train=dict(
type='RepeatDataset',
times=50,
dataset=dict(
type=dataset_type,
data_root=data_root,
img_dir='images/training',
ann_dir='annotations/training',
pipeline=train_pipeline)),
val=dict(
type=dataset_type,
data_root=data_root,
img_dir='images/validation',
ann_dir='annotations/validation',
pipeline=test_pipeline),
test=dict(
type=dataset_type,
data_root=data_root,
img_dir='images/validation',
ann_dir='annotations/validation',
pipeline=test_pipeline))
| 2,454 | 31.733333 | 77 | py |
mmsegmentation | mmsegmentation-master/configs/poolformer/fpn_poolformer_s24_8x4_512x512_40k_ade20k.py | _base_ = './fpn_poolformer_s12_8x4_512x512_40k_ade20k.py'
checkpoint_file = 'https://download.openmmlab.com/mmclassification/v0/poolformer/poolformer-s24_3rdparty_32xb128_in1k_20220414-d7055904.pth' # noqa
# model settings
model = dict(
backbone=dict(
arch='s24',
init_cfg=dict(
type='Pretrained', checkpoint=checkpoint_file,
prefix='backbone.')))
| 393 | 38.4 | 148 | py |
mmsegmentation | mmsegmentation-master/configs/poolformer/fpn_poolformer_s36_8x4_512x512_40k_ade20k.py | _base_ = './fpn_poolformer_s12_8x4_512x512_40k_ade20k.py'
checkpoint_file = 'https://download.openmmlab.com/mmclassification/v0/poolformer/poolformer-s36_3rdparty_32xb128_in1k_20220414-d78ff3e8.pth' # noqa
# model settings
model = dict(
backbone=dict(
arch='s36',
init_cfg=dict(
type='Pretrained', checkpoint=checkpoint_file,
prefix='backbone.')))
| 394 | 34.909091 | 148 | py |
mmsegmentation | mmsegmentation-master/configs/poolformer/poolformer.yml | Models:
- Name: fpn_poolformer_s12_8x4_512x512_40k_ade20k
In Collection: FPN
Metadata:
backbone: PoolFormer-S12
crop size: (512,512)
lr schd: 40000
inference time (ms/im):
- value: 42.59
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 4.17
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 36.68
mIoU(ms+flip): 38.22
Config: configs/poolformer/fpn_poolformer_s12_8x4_512x512_40k_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/poolformer/fpn_poolformer_s12_8x4_512x512_40k_ade20k/fpn_poolformer_s12_8x4_512x512_40k_ade20k_20220501_115154-b5aa2f49.pth
- Name: fpn_poolformer_s24_8x4_512x512_40k_ade20k
In Collection: FPN
Metadata:
backbone: PoolFormer-S24
crop size: (512,512)
lr schd: 40000
inference time (ms/im):
- value: 63.53
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 5.47
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 40.12
mIoU(ms+flip): 40.97
Config: configs/poolformer/fpn_poolformer_s24_8x4_512x512_40k_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/poolformer/fpn_poolformer_s24_8x4_512x512_40k_ade20k/fpn_poolformer_s24_8x4_512x512_40k_ade20k_20220503_222049-394a7cf7.pth
- Name: fpn_poolformer_s36_8x4_512x512_40k_ade20k
In Collection: FPN
Metadata:
backbone: PoolFormer-S36
crop size: (512,512)
lr schd: 40000
inference time (ms/im):
- value: 88.18
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 6.77
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 41.61
mIoU(ms+flip): 42.61
Config: configs/poolformer/fpn_poolformer_s36_8x4_512x512_40k_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/poolformer/fpn_poolformer_s36_8x4_512x512_40k_ade20k/fpn_poolformer_s36_8x4_512x512_40k_ade20k_20220501_151122-b47e607d.pth
- Name: fpn_poolformer_m36_8x4_512x512_40k_ade20k
In Collection: FPN
Metadata:
backbone: PoolFormer-M36
crop size: (512,512)
lr schd: 40000
inference time (ms/im):
- value: 111.48
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 8.59
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 41.95
mIoU(ms+flip): 43.24
Config: configs/poolformer/fpn_poolformer_m36_8x4_512x512_40k_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/poolformer/fpn_poolformer_m36_8x4_512x512_40k_ade20k/fpn_poolformer_m36_8x4_512x512_40k_ade20k_20220501_164230-3dc83921.pth
- Name: fpn_poolformer_m48_8x4_512x512_40k_ade20k
In Collection: FPN
Metadata:
backbone: PoolFormer-M48
crop size: (512,512)
lr schd: 40000
inference time (ms/im):
- value: 149.48
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 10.48
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 42.43
mIoU(ms+flip): 43.6
Config: configs/poolformer/fpn_poolformer_m48_8x4_512x512_40k_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/poolformer/fpn_poolformer_m48_8x4_512x512_40k_ade20k/fpn_poolformer_m48_8x4_512x512_40k_ade20k_20220504_003923-64168d3b.pth
| 3,630 | 31.419643 | 185 | yml |
mmsegmentation | mmsegmentation-master/configs/psanet/README.md | # PSANet
[PSANet: Point-wise Spatial Attention Network for Scene Parsing](https://openaccess.thecvf.com/content_ECCV_2018/papers/Hengshuang_Zhao_PSANet_Point-wise_Spatial_ECCV_2018_paper.pdf)
## Introduction
<!-- [ALGORITHM] -->
<a href="https://github.com/hszhao/PSANet">Official Repo</a>
<a href="https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/psa_head.py#L18">Code Snippet</a>
## Abstract
<!-- [ABSTRACT] -->
We notice information flow in convolutional neural networksis restricted inside local neighborhood regions due to the physical de-sign of convolutional filters, which limits the overall understanding ofcomplex scenes. In this paper, we propose thepoint-wise spatial atten-tion network(PSANet) to relax the local neighborhood constraint. Eachposition on the feature map is connected to all the other ones througha self-adaptively learned attention mask. Moreover, information propa-gation in bi-direction for scene parsing is enabled. Information at otherpositions can be collected to help the prediction of the current positionand vice versa, information at the current position can be distributedto assist the prediction of other ones. Our proposed approach achievestop performance on various competitive scene parsing datasets, includ-ing ADE20K, PASCAL VOC 2012 and Cityscapes, demonstrating itseffectiveness and generality.
<!-- [IMAGE] -->
<div align=center>
<img src="https://user-images.githubusercontent.com/24582831/142902367-0f29e8cb-5ac0-434b-98c4-b2af7c9c2e58.png" width="70%"/>
</div>
## Citation
```bibtex
@inproceedings{zhao2018psanet,
title={Psanet: Point-wise spatial attention network for scene parsing},
author={Zhao, Hengshuang and Zhang, Yi and Liu, Shu and Shi, Jianping and Change Loy, Chen and Lin, Dahua and Jia, Jiaya},
booktitle={Proceedings of the European Conference on Computer Vision (ECCV)},
pages={267--283},
year={2018}
}
```
## Results and models
### Cityscapes
| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download |
| ------ | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | --------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| PSANet | R-50-D8 | 512x1024 | 40000 | 7 | 3.17 | 77.63 | 79.04 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/psanet/psanet_r50-d8_512x1024_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x1024_40k_cityscapes/psanet_r50-d8_512x1024_40k_cityscapes_20200606_103117-99fac37c.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x1024_40k_cityscapes/psanet_r50-d8_512x1024_40k_cityscapes_20200606_103117.log.json) |
| PSANet | R-101-D8 | 512x1024 | 40000 | 10.5 | 2.20 | 79.14 | 80.19 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/psanet/psanet_r101-d8_512x1024_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x1024_40k_cityscapes/psanet_r101-d8_512x1024_40k_cityscapes_20200606_001418-27b9cfa7.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x1024_40k_cityscapes/psanet_r101-d8_512x1024_40k_cityscapes_20200606_001418.log.json) |
| PSANet | R-50-D8 | 769x769 | 40000 | 7.9 | 1.40 | 77.99 | 79.64 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/psanet/psanet_r50-d8_769x769_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_769x769_40k_cityscapes/psanet_r50-d8_769x769_40k_cityscapes_20200530_033717-d5365506.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_769x769_40k_cityscapes/psanet_r50-d8_769x769_40k_cityscapes_20200530_033717.log.json) |
| PSANet | R-101-D8 | 769x769 | 40000 | 11.9 | 0.98 | 78.43 | 80.26 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/psanet/psanet_r101-d8_769x769_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_769x769_40k_cityscapes/psanet_r101-d8_769x769_40k_cityscapes_20200530_035107-997da1e6.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_769x769_40k_cityscapes/psanet_r101-d8_769x769_40k_cityscapes_20200530_035107.log.json) |
| PSANet | R-50-D8 | 512x1024 | 80000 | - | - | 77.24 | 78.69 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/psanet/psanet_r50-d8_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x1024_80k_cityscapes/psanet_r50-d8_512x1024_80k_cityscapes_20200606_161842-ab60a24f.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x1024_80k_cityscapes/psanet_r50-d8_512x1024_80k_cityscapes_20200606_161842.log.json) |
| PSANet | R-101-D8 | 512x1024 | 80000 | - | - | 79.31 | 80.53 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/psanet/psanet_r101-d8_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x1024_80k_cityscapes/psanet_r101-d8_512x1024_80k_cityscapes_20200606_161823-0f73a169.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x1024_80k_cityscapes/psanet_r101-d8_512x1024_80k_cityscapes_20200606_161823.log.json) |
| PSANet | R-50-D8 | 769x769 | 80000 | - | - | 79.31 | 80.91 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/psanet/psanet_r50-d8_769x769_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_769x769_80k_cityscapes/psanet_r50-d8_769x769_80k_cityscapes_20200606_225134-fe42f49e.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_769x769_80k_cityscapes/psanet_r50-d8_769x769_80k_cityscapes_20200606_225134.log.json) |
| PSANet | R-101-D8 | 769x769 | 80000 | - | - | 79.69 | 80.89 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/psanet/psanet_r101-d8_769x769_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_769x769_80k_cityscapes/psanet_r101-d8_769x769_80k_cityscapes_20200606_214550-7665827b.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_769x769_80k_cityscapes/psanet_r101-d8_769x769_80k_cityscapes_20200606_214550.log.json) |
### ADE20K
| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download |
| ------ | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ----------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| PSANet | R-50-D8 | 512x512 | 80000 | 9 | 18.91 | 41.14 | 41.91 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/psanet/psanet_r50-d8_512x512_80k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x512_80k_ade20k/psanet_r50-d8_512x512_80k_ade20k_20200614_144141-835e4b97.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x512_80k_ade20k/psanet_r50-d8_512x512_80k_ade20k_20200614_144141.log.json) |
| PSANet | R-101-D8 | 512x512 | 80000 | 12.5 | 13.13 | 43.80 | 44.75 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/psanet/psanet_r101-d8_512x512_80k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x512_80k_ade20k/psanet_r101-d8_512x512_80k_ade20k_20200614_185117-1fab60d4.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x512_80k_ade20k/psanet_r101-d8_512x512_80k_ade20k_20200614_185117.log.json) |
| PSANet | R-50-D8 | 512x512 | 160000 | - | - | 41.67 | 42.95 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/psanet/psanet_r50-d8_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x512_160k_ade20k/psanet_r50-d8_512x512_160k_ade20k_20200615_161258-148077dd.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x512_160k_ade20k/psanet_r50-d8_512x512_160k_ade20k_20200615_161258.log.json) |
| PSANet | R-101-D8 | 512x512 | 160000 | - | - | 43.74 | 45.38 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/psanet/psanet_r101-d8_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x512_160k_ade20k/psanet_r101-d8_512x512_160k_ade20k_20200615_161537-dbfa564c.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x512_160k_ade20k/psanet_r101-d8_512x512_160k_ade20k_20200615_161537.log.json) |
### Pascal VOC 2012 + Aug
| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download |
| ------ | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ------------------------------------------------------------------------------------------------------------------------ | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| PSANet | R-50-D8 | 512x512 | 20000 | 6.9 | 18.24 | 76.39 | 77.34 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/psanet/psanet_r50-d8_512x512_20k_voc12aug.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x512_20k_voc12aug/psanet_r50-d8_512x512_20k_voc12aug_20200617_102413-2f1bbaa1.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x512_20k_voc12aug/psanet_r50-d8_512x512_20k_voc12aug_20200617_102413.log.json) |
| PSANet | R-101-D8 | 512x512 | 20000 | 10.4 | 12.63 | 77.91 | 79.30 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/psanet/psanet_r101-d8_512x512_20k_voc12aug.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x512_20k_voc12aug/psanet_r101-d8_512x512_20k_voc12aug_20200617_110624-946fef11.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x512_20k_voc12aug/psanet_r101-d8_512x512_20k_voc12aug_20200617_110624.log.json) |
| PSANet | R-50-D8 | 512x512 | 40000 | - | - | 76.30 | 77.35 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/psanet/psanet_r50-d8_512x512_40k_voc12aug.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x512_40k_voc12aug/psanet_r50-d8_512x512_40k_voc12aug_20200613_161946-f596afb5.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x512_40k_voc12aug/psanet_r50-d8_512x512_40k_voc12aug_20200613_161946.log.json) |
| PSANet | R-101-D8 | 512x512 | 40000 | - | - | 77.73 | 79.05 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/psanet/psanet_r101-d8_512x512_40k_voc12aug.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x512_40k_voc12aug/psanet_r101-d8_512x512_40k_voc12aug_20200613_161946-1f560f9e.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x512_40k_voc12aug/psanet_r101-d8_512x512_40k_voc12aug_20200613_161946.log.json) |
| 14,359 | 207.115942 | 966 | md |
mmsegmentation | mmsegmentation-master/configs/psanet/psanet.yml | Collections:
- Name: PSANet
Metadata:
Training Data:
- Cityscapes
- ADE20K
- Pascal VOC 2012 + Aug
Paper:
URL: https://openaccess.thecvf.com/content_ECCV_2018/papers/Hengshuang_Zhao_PSANet_Point-wise_Spatial_ECCV_2018_paper.pdf
Title: 'PSANet: Point-wise Spatial Attention Network for Scene Parsing'
README: configs/psanet/README.md
Code:
URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/psa_head.py#L18
Version: v0.17.0
Converted From:
Code: https://github.com/hszhao/PSANet
Models:
- Name: psanet_r50-d8_512x1024_40k_cityscapes
In Collection: PSANet
Metadata:
backbone: R-50-D8
crop size: (512,1024)
lr schd: 40000
inference time (ms/im):
- value: 315.46
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,1024)
Training Memory (GB): 7.0
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 77.63
mIoU(ms+flip): 79.04
Config: configs/psanet/psanet_r50-d8_512x1024_40k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x1024_40k_cityscapes/psanet_r50-d8_512x1024_40k_cityscapes_20200606_103117-99fac37c.pth
- Name: psanet_r101-d8_512x1024_40k_cityscapes
In Collection: PSANet
Metadata:
backbone: R-101-D8
crop size: (512,1024)
lr schd: 40000
inference time (ms/im):
- value: 454.55
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,1024)
Training Memory (GB): 10.5
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.14
mIoU(ms+flip): 80.19
Config: configs/psanet/psanet_r101-d8_512x1024_40k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x1024_40k_cityscapes/psanet_r101-d8_512x1024_40k_cityscapes_20200606_001418-27b9cfa7.pth
- Name: psanet_r50-d8_769x769_40k_cityscapes
In Collection: PSANet
Metadata:
backbone: R-50-D8
crop size: (769,769)
lr schd: 40000
inference time (ms/im):
- value: 714.29
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (769,769)
Training Memory (GB): 7.9
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 77.99
mIoU(ms+flip): 79.64
Config: configs/psanet/psanet_r50-d8_769x769_40k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_769x769_40k_cityscapes/psanet_r50-d8_769x769_40k_cityscapes_20200530_033717-d5365506.pth
- Name: psanet_r101-d8_769x769_40k_cityscapes
In Collection: PSANet
Metadata:
backbone: R-101-D8
crop size: (769,769)
lr schd: 40000
inference time (ms/im):
- value: 1020.41
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (769,769)
Training Memory (GB): 11.9
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 78.43
mIoU(ms+flip): 80.26
Config: configs/psanet/psanet_r101-d8_769x769_40k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_769x769_40k_cityscapes/psanet_r101-d8_769x769_40k_cityscapes_20200530_035107-997da1e6.pth
- Name: psanet_r50-d8_512x1024_80k_cityscapes
In Collection: PSANet
Metadata:
backbone: R-50-D8
crop size: (512,1024)
lr schd: 80000
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 77.24
mIoU(ms+flip): 78.69
Config: configs/psanet/psanet_r50-d8_512x1024_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x1024_80k_cityscapes/psanet_r50-d8_512x1024_80k_cityscapes_20200606_161842-ab60a24f.pth
- Name: psanet_r101-d8_512x1024_80k_cityscapes
In Collection: PSANet
Metadata:
backbone: R-101-D8
crop size: (512,1024)
lr schd: 80000
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.31
mIoU(ms+flip): 80.53
Config: configs/psanet/psanet_r101-d8_512x1024_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x1024_80k_cityscapes/psanet_r101-d8_512x1024_80k_cityscapes_20200606_161823-0f73a169.pth
- Name: psanet_r50-d8_769x769_80k_cityscapes
In Collection: PSANet
Metadata:
backbone: R-50-D8
crop size: (769,769)
lr schd: 80000
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.31
mIoU(ms+flip): 80.91
Config: configs/psanet/psanet_r50-d8_769x769_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_769x769_80k_cityscapes/psanet_r50-d8_769x769_80k_cityscapes_20200606_225134-fe42f49e.pth
- Name: psanet_r101-d8_769x769_80k_cityscapes
In Collection: PSANet
Metadata:
backbone: R-101-D8
crop size: (769,769)
lr schd: 80000
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.69
mIoU(ms+flip): 80.89
Config: configs/psanet/psanet_r101-d8_769x769_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_769x769_80k_cityscapes/psanet_r101-d8_769x769_80k_cityscapes_20200606_214550-7665827b.pth
- Name: psanet_r50-d8_512x512_80k_ade20k
In Collection: PSANet
Metadata:
backbone: R-50-D8
crop size: (512,512)
lr schd: 80000
inference time (ms/im):
- value: 52.88
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 9.0
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 41.14
mIoU(ms+flip): 41.91
Config: configs/psanet/psanet_r50-d8_512x512_80k_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x512_80k_ade20k/psanet_r50-d8_512x512_80k_ade20k_20200614_144141-835e4b97.pth
- Name: psanet_r101-d8_512x512_80k_ade20k
In Collection: PSANet
Metadata:
backbone: R-101-D8
crop size: (512,512)
lr schd: 80000
inference time (ms/im):
- value: 76.16
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 12.5
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 43.8
mIoU(ms+flip): 44.75
Config: configs/psanet/psanet_r101-d8_512x512_80k_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x512_80k_ade20k/psanet_r101-d8_512x512_80k_ade20k_20200614_185117-1fab60d4.pth
- Name: psanet_r50-d8_512x512_160k_ade20k
In Collection: PSANet
Metadata:
backbone: R-50-D8
crop size: (512,512)
lr schd: 160000
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 41.67
mIoU(ms+flip): 42.95
Config: configs/psanet/psanet_r50-d8_512x512_160k_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x512_160k_ade20k/psanet_r50-d8_512x512_160k_ade20k_20200615_161258-148077dd.pth
- Name: psanet_r101-d8_512x512_160k_ade20k
In Collection: PSANet
Metadata:
backbone: R-101-D8
crop size: (512,512)
lr schd: 160000
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 43.74
mIoU(ms+flip): 45.38
Config: configs/psanet/psanet_r101-d8_512x512_160k_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x512_160k_ade20k/psanet_r101-d8_512x512_160k_ade20k_20200615_161537-dbfa564c.pth
- Name: psanet_r50-d8_512x512_20k_voc12aug
In Collection: PSANet
Metadata:
backbone: R-50-D8
crop size: (512,512)
lr schd: 20000
inference time (ms/im):
- value: 54.82
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 6.9
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 76.39
mIoU(ms+flip): 77.34
Config: configs/psanet/psanet_r50-d8_512x512_20k_voc12aug.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x512_20k_voc12aug/psanet_r50-d8_512x512_20k_voc12aug_20200617_102413-2f1bbaa1.pth
- Name: psanet_r101-d8_512x512_20k_voc12aug
In Collection: PSANet
Metadata:
backbone: R-101-D8
crop size: (512,512)
lr schd: 20000
inference time (ms/im):
- value: 79.18
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 10.4
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 77.91
mIoU(ms+flip): 79.3
Config: configs/psanet/psanet_r101-d8_512x512_20k_voc12aug.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x512_20k_voc12aug/psanet_r101-d8_512x512_20k_voc12aug_20200617_110624-946fef11.pth
- Name: psanet_r50-d8_512x512_40k_voc12aug
In Collection: PSANet
Metadata:
backbone: R-50-D8
crop size: (512,512)
lr schd: 40000
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 76.3
mIoU(ms+flip): 77.35
Config: configs/psanet/psanet_r50-d8_512x512_40k_voc12aug.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x512_40k_voc12aug/psanet_r50-d8_512x512_40k_voc12aug_20200613_161946-f596afb5.pth
- Name: psanet_r101-d8_512x512_40k_voc12aug
In Collection: PSANet
Metadata:
backbone: R-101-D8
crop size: (512,512)
lr schd: 40000
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 77.73
mIoU(ms+flip): 79.05
Config: configs/psanet/psanet_r101-d8_512x512_40k_voc12aug.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x512_40k_voc12aug/psanet_r101-d8_512x512_40k_voc12aug_20200613_161946-1f560f9e.pth
| 10,212 | 32.375817 | 175 | yml |
mmsegmentation | mmsegmentation-master/configs/psanet/psanet_r101-d8_512x1024_40k_cityscapes.py | _base_ = './psanet_r50-d8_512x1024_40k_cityscapes.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
| 134 | 44 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/psanet/psanet_r101-d8_512x1024_80k_cityscapes.py | _base_ = './psanet_r50-d8_512x1024_80k_cityscapes.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
| 134 | 44 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/psanet/psanet_r101-d8_512x512_160k_ade20k.py | _base_ = './psanet_r50-d8_512x512_160k_ade20k.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
| 130 | 42.666667 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/psanet/psanet_r101-d8_512x512_20k_voc12aug.py | _base_ = './psanet_r50-d8_512x512_20k_voc12aug.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
| 131 | 43 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/psanet/psanet_r101-d8_512x512_40k_voc12aug.py | _base_ = './psanet_r50-d8_512x512_40k_voc12aug.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
| 131 | 43 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/psanet/psanet_r101-d8_512x512_80k_ade20k.py | _base_ = './psanet_r50-d8_512x512_80k_ade20k.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
| 129 | 42.333333 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/psanet/psanet_r101-d8_769x769_40k_cityscapes.py | _base_ = './psanet_r50-d8_769x769_40k_cityscapes.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
| 133 | 43.666667 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/psanet/psanet_r101-d8_769x769_80k_cityscapes.py | _base_ = './psanet_r50-d8_769x769_80k_cityscapes.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
| 133 | 43.666667 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/psanet/psanet_r50-d8_512x1024_40k_cityscapes.py | _base_ = [
'../_base_/models/psanet_r50-d8.py', '../_base_/datasets/cityscapes.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py'
]
| 164 | 32 | 76 | py |
mmsegmentation | mmsegmentation-master/configs/psanet/psanet_r50-d8_512x1024_80k_cityscapes.py | _base_ = [
'../_base_/models/psanet_r50-d8.py', '../_base_/datasets/cityscapes.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
]
| 164 | 32 | 76 | py |
mmsegmentation | mmsegmentation-master/configs/psanet/psanet_r50-d8_512x512_160k_ade20k.py | _base_ = [
'../_base_/models/psanet_r50-d8.py', '../_base_/datasets/ade20k.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py'
]
model = dict(
decode_head=dict(mask_size=(66, 66), num_classes=150),
auxiliary_head=dict(num_classes=150))
| 276 | 33.625 | 74 | py |
mmsegmentation | mmsegmentation-master/configs/psanet/psanet_r50-d8_512x512_20k_voc12aug.py | _base_ = [
'../_base_/models/psanet_r50-d8.py',
'../_base_/datasets/pascal_voc12_aug.py', '../_base_/default_runtime.py',
'../_base_/schedules/schedule_20k.py'
]
model = dict(
decode_head=dict(num_classes=21), auxiliary_head=dict(num_classes=21))
| 263 | 32 | 77 | py |
mmsegmentation | mmsegmentation-master/configs/psanet/psanet_r50-d8_512x512_40k_voc12aug.py | _base_ = [
'../_base_/models/psanet_r50-d8.py',
'../_base_/datasets/pascal_voc12_aug.py', '../_base_/default_runtime.py',
'../_base_/schedules/schedule_40k.py'
]
model = dict(
decode_head=dict(num_classes=21), auxiliary_head=dict(num_classes=21))
| 263 | 32 | 77 | py |
mmsegmentation | mmsegmentation-master/configs/psanet/psanet_r50-d8_512x512_80k_ade20k.py | _base_ = [
'../_base_/models/psanet_r50-d8.py', '../_base_/datasets/ade20k.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
]
model = dict(
decode_head=dict(mask_size=(66, 66), num_classes=150),
auxiliary_head=dict(num_classes=150))
| 275 | 33.5 | 73 | py |
mmsegmentation | mmsegmentation-master/configs/psanet/psanet_r50-d8_769x769_40k_cityscapes.py | _base_ = [
'../_base_/models/psanet_r50-d8.py',
'../_base_/datasets/cityscapes_769x769.py', '../_base_/default_runtime.py',
'../_base_/schedules/schedule_40k.py'
]
model = dict(
decode_head=dict(align_corners=True),
auxiliary_head=dict(align_corners=True),
test_cfg=dict(mode='slide', crop_size=(769, 769), stride=(513, 513)))
| 351 | 34.2 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/psanet/psanet_r50-d8_769x769_80k_cityscapes.py | _base_ = [
'../_base_/models/psanet_r50-d8.py',
'../_base_/datasets/cityscapes_769x769.py', '../_base_/default_runtime.py',
'../_base_/schedules/schedule_80k.py'
]
model = dict(
decode_head=dict(align_corners=True),
auxiliary_head=dict(align_corners=True),
test_cfg=dict(mode='slide', crop_size=(769, 769), stride=(513, 513)))
| 351 | 34.2 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/pspnet/README.md | # PSPNet
[Pyramid Scene Parsing Network](https://arxiv.org/abs/1612.01105)
## Introduction
<!-- [ALGORITHM] -->
<a href="https://github.com/hszhao/PSPNet">Official Repo</a>
<a href="https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/psp_head.py#L63">Code Snippet</a>
## Abstract
<!-- [ABSTRACT] -->
Scene parsing is challenging for unrestricted open vocabulary and diverse scenes. In this paper, we exploit the capability of global context information by different-region-based context aggregation through our pyramid pooling module together with the proposed pyramid scene parsing network (PSPNet). Our global prior representation is effective to produce good quality results on the scene parsing task, while PSPNet provides a superior framework for pixel-level prediction tasks. The proposed approach achieves state-of-the-art performance on various datasets. It came first in ImageNet scene parsing challenge 2016, PASCAL VOC 2012 benchmark and Cityscapes benchmark. A single PSPNet yields new record of mIoU accuracy 85.4% on PASCAL VOC 2012 and accuracy 80.2% on Cityscapes.
<!-- [IMAGE] -->
<div align=center>
<img src="https://user-images.githubusercontent.com/24582831/142902444-9f93b99e-9261-443b-a0a4-17e78eefb525.png" width="70%"/>
</div>
## Citation
```bibtex
@inproceedings{zhao2017pspnet,
title={Pyramid Scene Parsing Network},
author={Zhao, Hengshuang and Shi, Jianping and Qi, Xiaojuan and Wang, Xiaogang and Jia, Jiaya},
booktitle={CVPR},
year={2017}
}
```
```bibtex
@article{wightman2021resnet,
title={Resnet strikes back: An improved training procedure in timm},
author={Wightman, Ross and Touvron, Hugo and J{\'e}gou, Herv{\'e}},
journal={arXiv preprint arXiv:2110.00476},
year={2021}
}
```
## Results and models
### Cityscapes
| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download |
| ------------- | ------------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ---------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| PSPNet | R-50-D8 | 512x1024 | 40000 | 6.1 | 4.07 | 77.85 | 79.18 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet/pspnet_r50-d8_512x1024_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x1024_40k_cityscapes/pspnet_r50-d8_512x1024_40k_cityscapes_20200605_003338-2966598c.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x1024_40k_cityscapes/pspnet_r50-d8_512x1024_40k_cityscapes_20200605_003338.log.json) |
| PSPNet | R-101-D8 | 512x1024 | 40000 | 9.6 | 2.68 | 78.34 | 79.74 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet/pspnet_r101-d8_512x1024_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x1024_40k_cityscapes/pspnet_r101-d8_512x1024_40k_cityscapes_20200604_232751-467e7cf4.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x1024_40k_cityscapes/pspnet_r101-d8_512x1024_40k_cityscapes_20200604_232751.log.json) |
| PSPNet | R-50-D8 | 769x769 | 40000 | 6.9 | 1.76 | 78.26 | 79.88 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet/pspnet_r50-d8_769x769_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_769x769_40k_cityscapes/pspnet_r50-d8_769x769_40k_cityscapes_20200606_112725-86638686.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_769x769_40k_cityscapes/pspnet_r50-d8_769x769_40k_cityscapes_20200606_112725.log.json) |
| PSPNet | R-101-D8 | 769x769 | 40000 | 10.9 | 1.15 | 79.08 | 80.28 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet/pspnet_r101-d8_769x769_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_769x769_40k_cityscapes/pspnet_r101-d8_769x769_40k_cityscapes_20200606_112753-61c6f5be.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_769x769_40k_cityscapes/pspnet_r101-d8_769x769_40k_cityscapes_20200606_112753.log.json) |
| PSPNet | R-18-D8 | 512x1024 | 80000 | 1.7 | 15.71 | 74.87 | 76.04 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet/pspnet_r18-d8_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18-d8_512x1024_80k_cityscapes/pspnet_r18-d8_512x1024_80k_cityscapes_20201225_021458-09ffa746.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18-d8_512x1024_80k_cityscapes/pspnet_r18-d8_512x1024_80k_cityscapes-20201225_021458.log.json) |
| PSPNet | R-50-D8 | 512x1024 | 80000 | - | - | 78.55 | 79.79 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet/pspnet_r50-d8_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x1024_80k_cityscapes/pspnet_r50-d8_512x1024_80k_cityscapes_20200606_112131-2376f12b.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x1024_80k_cityscapes/pspnet_r50-d8_512x1024_80k_cityscapes_20200606_112131.log.json) |
| PSPNet | R-50b-D8 rsb | 512x1024 | 80000 | 6.2 | 3.82 | 78.47 | 79.45 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet/pspnet_r50-d8_rsb-pretrain_512x1024_adamw_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_rsb-pretrain_512x1024_adamw_80k_cityscapes/pspnet_r50-d8_rsb-pretrain_512x1024_adamw_80k_cityscapes_20220315_123238-588c30be.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_rsb-pretrain_512x1024_adamw_80k_cityscapes/pspnet_r50-d8_rsb-pretrain_512x1024_adamw_80k_cityscapes_20220315_123238.log.json) |
| PSPNet | R-101-D8 | 512x1024 | 80000 | - | - | 79.76 | 81.01 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet/pspnet_r101-d8_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x1024_80k_cityscapes/pspnet_r101-d8_512x1024_80k_cityscapes_20200606_112211-e1e1100f.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x1024_80k_cityscapes/pspnet_r101-d8_512x1024_80k_cityscapes_20200606_112211.log.json) |
| PSPNet (FP16) | R-101-D8 | 512x1024 | 80000 | 5.34 | 8.77 | 79.46 | - | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet/pspnet_r101-d8_fp16_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_fp16_512x1024_80k_cityscapes/pspnet_r101-d8_fp16_512x1024_80k_cityscapes_20200717_230919-a0875e5c.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_fp16_512x1024_80k_cityscapes/pspnet_r101-d8_fp16_512x1024_80k_cityscapes_20200717_230919.log.json) |
| PSPNet | R-18-D8 | 769x769 | 80000 | 1.9 | 6.20 | 75.90 | 77.86 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet/pspnet_r18-d8_769x769_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18-d8_769x769_80k_cityscapes/pspnet_r18-d8_769x769_80k_cityscapes_20201225_021458-3deefc62.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18-d8_769x769_80k_cityscapes/pspnet_r18-d8_769x769_80k_cityscapes-20201225_021458.log.json) |
| PSPNet | R-50-D8 | 769x769 | 80000 | - | - | 79.59 | 80.69 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet/pspnet_r50-d8_769x769_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_769x769_80k_cityscapes/pspnet_r50-d8_769x769_80k_cityscapes_20200606_210121-5ccf03dd.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_769x769_80k_cityscapes/pspnet_r50-d8_769x769_80k_cityscapes_20200606_210121.log.json) |
| PSPNet | R-101-D8 | 769x769 | 80000 | - | - | 79.77 | 81.06 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet/pspnet_r101-d8_769x769_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_769x769_80k_cityscapes/pspnet_r101-d8_769x769_80k_cityscapes_20200606_225055-dba412fa.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_769x769_80k_cityscapes/pspnet_r101-d8_769x769_80k_cityscapes_20200606_225055.log.json) |
| PSPNet | R-18b-D8 | 512x1024 | 80000 | 1.5 | 16.28 | 74.23 | 75.79 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet/pspnet_r18b-d8_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18b-d8_512x1024_80k_cityscapes/pspnet_r18b-d8_512x1024_80k_cityscapes_20201226_063116-26928a60.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18b-d8_512x1024_80k_cityscapes/pspnet_r18b-d8_512x1024_80k_cityscapes-20201226_063116.log.json) |
| PSPNet | R-50b-D8 | 512x1024 | 80000 | 6.0 | 4.30 | 78.22 | 79.46 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet/pspnet_r50b-d8_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50b-d8_512x1024_80k_cityscapes/pspnet_r50b-d8_512x1024_80k_cityscapes_20201225_094315-6344287a.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50b-d8_512x1024_80k_cityscapes/pspnet_r50b-d8_512x1024_80k_cityscapes-20201225_094315.log.json) |
| PSPNet | R-101b-D8 | 512x1024 | 80000 | 9.5 | 2.76 | 79.69 | 80.79 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet/pspnet_r101b-d8_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101b-d8_512x1024_80k_cityscapes/pspnet_r101b-d8_512x1024_80k_cityscapes_20201226_170012-3a4d38ab.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101b-d8_512x1024_80k_cityscapes/pspnet_r101b-d8_512x1024_80k_cityscapes-20201226_170012.log.json) |
| PSPNet | R-18b-D8 | 769x769 | 80000 | 1.7 | 6.41 | 74.92 | 76.90 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet/pspnet_r18b-d8_769x769_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18b-d8_769x769_80k_cityscapes/pspnet_r18b-d8_769x769_80k_cityscapes_20201226_080942-bf98d186.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18b-d8_769x769_80k_cityscapes/pspnet_r18b-d8_769x769_80k_cityscapes-20201226_080942.log.json) |
| PSPNet | R-50b-D8 | 769x769 | 80000 | 6.8 | 1.88 | 78.50 | 79.96 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet/pspnet_r50b-d8_769x769_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50b-d8_769x769_80k_cityscapes/pspnet_r50b-d8_769x769_80k_cityscapes_20201225_094316-4c643cf6.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50b-d8_769x769_80k_cityscapes/pspnet_r50b-d8_769x769_80k_cityscapes-20201225_094316.log.json) |
| PSPNet | R-101b-D8 | 769x769 | 80000 | 10.8 | 1.17 | 78.87 | 80.04 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet/pspnet_r101b-d8_769x769_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101b-d8_769x769_80k_cityscapes/pspnet_r101b-d8_769x769_80k_cityscapes_20201226_171823-f0e7c293.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101b-d8_769x769_80k_cityscapes/pspnet_r101b-d8_769x769_80k_cityscapes-20201226_171823.log.json) |
| PSPNet | R-50-D32 | 512x1024 | 80000 | 3.0 | 15.21 | 73.88 | 76.85 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet/pspnet_r50-d32_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d32_512x1024_80k_cityscapes/pspnet_r50-d32_512x1024_80k_cityscapes_20220316_224840-9092b254.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d32_512x1024_80k_cityscapes/pspnet_r50-d32_512x1024_80k_cityscapes_20220316_224840.log.json) |
| PSPNet | R-50b-D32 rsb | 512x1024 | 80000 | 3.1 | 16.08 | 74.09 | 77.18 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet/pspnet_r50-d32_rsb-pretrain_512x1024_adamw_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d32_rsb-pretrain_512x1024_adamw_80k_cityscapes/pspnet_r50-d32_rsb-pretrain_512x1024_adamw_80k_cityscapes_20220316_141229-dd9c9610.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d32_rsb-pretrain_512x1024_adamw_80k_cityscapes/pspnet_r50-d32_rsb-pretrain_512x1024_adamw_80k_cityscapes_20220316_141229.log.json) |
| PSPNet | R-50b-D32 | 512x1024 | 80000 | 2.9 | 15.41 | 72.61 | 75.51 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet/pspnet_r50b-d32_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50b-d32_512x1024_80k_cityscapes/pspnet_r50b-d32_512x1024_80k_cityscapes_20220311_152152-23bcaf8c.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50b-d32_512x1024_80k_cityscapes/pspnet_r50b-d32_512x1024_80k_cityscapes_20220311_152152.log.json) |
### ADE20K
| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download |
| ------ | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ----------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| PSPNet | R-50-D8 | 512x512 | 80000 | 8.5 | 23.53 | 41.13 | 41.94 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet/pspnet_r50-d8_512x512_80k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_80k_ade20k/pspnet_r50-d8_512x512_80k_ade20k_20200615_014128-15a8b914.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_80k_ade20k/pspnet_r50-d8_512x512_80k_ade20k_20200615_014128.log.json) |
| PSPNet | R-101-D8 | 512x512 | 80000 | 12 | 15.30 | 43.57 | 44.35 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet/pspnet_r101-d8_512x512_80k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_80k_ade20k/pspnet_r101-d8_512x512_80k_ade20k_20200614_031423-b6e782f0.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_80k_ade20k/pspnet_r101-d8_512x512_80k_ade20k_20200614_031423.log.json) |
| PSPNet | R-50-D8 | 512x512 | 160000 | - | - | 42.48 | 43.44 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet/pspnet_r50-d8_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_160k_ade20k/pspnet_r50-d8_512x512_160k_ade20k_20200615_184358-1890b0bd.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_160k_ade20k/pspnet_r50-d8_512x512_160k_ade20k_20200615_184358.log.json) |
| PSPNet | R-101-D8 | 512x512 | 160000 | - | - | 44.39 | 45.35 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet/pspnet_r101-d8_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_160k_ade20k/pspnet_r101-d8_512x512_160k_ade20k_20200615_100650-967c316f.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_160k_ade20k/pspnet_r101-d8_512x512_160k_ade20k_20200615_100650.log.json) |
### Pascal VOC 2012 + Aug
| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download |
| ------ | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ------------------------------------------------------------------------------------------------------------------------ | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| PSPNet | R-50-D8 | 512x512 | 20000 | 6.1 | 23.59 | 76.78 | 77.61 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet/pspnet_r50-d8_512x512_20k_voc12aug.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_20k_voc12aug/pspnet_r50-d8_512x512_20k_voc12aug_20200617_101958-ed5dfbd9.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_20k_voc12aug/pspnet_r50-d8_512x512_20k_voc12aug_20200617_101958.log.json) |
| PSPNet | R-101-D8 | 512x512 | 20000 | 9.6 | 15.02 | 78.47 | 79.25 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet/pspnet_r101-d8_512x512_20k_voc12aug.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_20k_voc12aug/pspnet_r101-d8_512x512_20k_voc12aug_20200617_102003-4aef3c9a.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_20k_voc12aug/pspnet_r101-d8_512x512_20k_voc12aug_20200617_102003.log.json) |
| PSPNet | R-50-D8 | 512x512 | 40000 | - | - | 77.29 | 78.48 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet/pspnet_r50-d8_512x512_40k_voc12aug.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_40k_voc12aug/pspnet_r50-d8_512x512_40k_voc12aug_20200613_161222-ae9c1b8c.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_40k_voc12aug/pspnet_r50-d8_512x512_40k_voc12aug_20200613_161222.log.json) |
| PSPNet | R-101-D8 | 512x512 | 40000 | - | - | 78.52 | 79.57 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet/pspnet_r101-d8_512x512_40k_voc12aug.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_40k_voc12aug/pspnet_r101-d8_512x512_40k_voc12aug_20200613_161222-bc933b18.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_40k_voc12aug/pspnet_r101-d8_512x512_40k_voc12aug_20200613_161222.log.json) |
### Pascal Context
| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download |
| ------ | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ------------------------------------------------------------------------------------------------------------------------------ | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| PSPNet | R-101-D8 | 480x480 | 40000 | 8.8 | 9.68 | 46.60 | 47.78 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet/pspnet_r101-d8_480x480_40k_pascal_context.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_480x480_40k_pascal_context/pspnet_r101-d8_480x480_40k_pascal_context_20200911_211210-bf0f5d7c.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_480x480_40k_pascal_context/pspnet_r101-d8_480x480_40k_pascal_context-20200911_211210.log.json) |
| PSPNet | R-101-D8 | 480x480 | 80000 | - | - | 46.03 | 47.15 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet/pspnet_r101-d8_480x480_80k_pascal_context.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_480x480_80k_pascal_context/pspnet_r101-d8_480x480_80k_pascal_context_20200911_190530-c86d6233.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_480x480_80k_pascal_context/pspnet_r101-d8_480x480_80k_pascal_context-20200911_190530.log.json) |
### Pascal Context 59
| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download |
| ------ | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | --------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| PSPNet | R-101-D8 | 480x480 | 40000 | - | - | 52.02 | 53.54 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet/pspnet_r101-d8_480x480_40k_pascal_context_59.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_480x480_40k_pascal_context_59/pspnet_r101-d8_480x480_40k_pascal_context_59_20210416_114524-86d44cd4.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_480x480_40k_pascal_context_59/pspnet_r101-d8_480x480_40k_pascal_context_59-20210416_114524.log.json) |
| PSPNet | R-101-D8 | 480x480 | 80000 | - | - | 52.47 | 53.99 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet/pspnet_r101-d8_480x480_80k_pascal_context_59.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_480x480_80k_pascal_context_59/pspnet_r101-d8_480x480_80k_pascal_context_59_20210416_114418-fa6caaa2.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_480x480_80k_pascal_context_59/pspnet_r101-d8_480x480_80k_pascal_context_59-20210416_114418.log.json) |
### Dark Zurich and Nighttime Driving
We support evaluation results on these two datasets using models above trained on Cityscapes training set.
| Method | Backbone | Training Dataset | Test Dataset | mIoU | config | evaluation checkpoint |
| ------ | --------- | ----------------------- | ------------------------- | ----- | ------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| PSPNet | R-50-D8 | Cityscapes Training set | Dark Zurich | 10.91 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet/pspnet_r50-d8_512x1024_40k_dark.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x1024_40k_cityscapes/pspnet_r50-d8_512x1024_40k_cityscapes_20200605_003338-2966598c.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x1024_40k_cityscapes/pspnet_r50-d8_512x1024_40k_cityscapes_20200605_003338.log.json) |
| PSPNet | R-50-D8 | Cityscapes Training set | Nighttime Driving | 23.02 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet/pspnet_r50-d8_512x1024_40k_night_driving.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x1024_40k_cityscapes/pspnet_r50-d8_512x1024_40k_cityscapes_20200605_003338-2966598c.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x1024_40k_cityscapes/pspnet_r50-d8_512x1024_40k_cityscapes_20200605_003338.log.json) |
| PSPNet | R-50-D8 | Cityscapes Training set | Cityscapes Validation set | 77.85 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet/pspnet_r50-d8_512x1024_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x1024_40k_cityscapes/pspnet_r50-d8_512x1024_40k_cityscapes_20200605_003338-2966598c.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x1024_40k_cityscapes/pspnet_r50-d8_512x1024_40k_cityscapes_20200605_003338.log.json) |
| PSPNet | R-101-D8 | Cityscapes Training set | Dark Zurich | 10.16 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet/pspnet_r101-d8_512x1024_40k_dark.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x1024_40k_cityscapes/pspnet_r101-d8_512x1024_40k_cityscapes_20200604_232751-467e7cf4.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x1024_40k_cityscapes/pspnet_r101-d8_512x1024_40k_cityscapes_20200604_232751.log.json) |
| PSPNet | R-101-D8 | Cityscapes Training set | Nighttime Driving | 20.25 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet/pspnet_r101-d8_512x1024_40k_night_driving.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x1024_40k_cityscapes/pspnet_r101-d8_512x1024_40k_cityscapes_20200604_232751-467e7cf4.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x1024_40k_cityscapes/pspnet_r101-d8_512x1024_40k_cityscapes_20200604_232751.log.json) |
| PSPNet | R-101-D8 | Cityscapes Training set | Cityscapes Validation set | 78.34 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet/pspnet_r101-d8_512x1024_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x1024_40k_cityscapes/pspnet_r101-d8_512x1024_40k_cityscapes_20200604_232751-467e7cf4.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x1024_40k_cityscapes/pspnet_r101-d8_512x1024_40k_cityscapes_20200604_232751.log.json) |
| PSPNet | R-101b-D8 | Cityscapes Training set | Dark Zurich | 15.54 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet/pspnet_r101b-d8_512x1024_80k_dark.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101b-d8_512x1024_80k_cityscapes/pspnet_r101b-d8_512x1024_80k_cityscapes_20201226_170012-3a4d38ab.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101b-d8_512x1024_80k_cityscapes/pspnet_r101b-d8_512x1024_80k_cityscapes-20201226_170012.log.json) |
| PSPNet | R-101b-D8 | Cityscapes Training set | Nighttime Driving | 22.25 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet/pspnet_r101b-d8_512x1024_80k_night_driving.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101b-d8_512x1024_80k_cityscapes/pspnet_r101b-d8_512x1024_80k_cityscapes_20201226_170012-3a4d38ab.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101b-d8_512x1024_80k_cityscapes/pspnet_r101b-d8_512x1024_80k_cityscapes-20201226_170012.log.json) |
| PSPNet | R-101b-D8 | Cityscapes Training set | Cityscapes Validation set | 79.69 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet/pspnet_r101b-d8_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101b-d8_512x1024_80k_cityscapes/pspnet_r101b-d8_512x1024_80k_cityscapes_20201226_170012-3a4d38ab.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101b-d8_512x1024_80k_cityscapes/pspnet_r101b-d8_512x1024_80k_cityscapes-20201226_170012.log.json) |
### COCO-Stuff 10k
| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download |
| ------ | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | --------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| PSPNet | R-50-D8 | 512x512 | 20000 | 9.6 | 20.5 | 35.69 | 36.62 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet/pspnet_r50-d8_512x512_4x4_20k_coco-stuff10k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_4x4_20k_coco-stuff10k/pspnet_r50-d8_512x512_4x4_20k_coco-stuff10k_20210820_203258-b88df27f.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_4x4_20k_coco-stuff10k/pspnet_r50-d8_512x512_4x4_20k_coco-stuff10k_20210820_203258.log.json) |
| PSPNet | R-101-D8 | 512x512 | 20000 | 13.2 | 11.1 | 37.26 | 38.52 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet/pspnet_r101-d8_512x512_4x4_20k_coco-stuff10k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_4x4_20k_coco-stuff10k/pspnet_r101-d8_512x512_4x4_20k_coco-stuff10k_20210820_232135-76aae482.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_4x4_20k_coco-stuff10k/pspnet_r101-d8_512x512_4x4_20k_coco-stuff10k_20210820_232135.log.json) |
| PSPNet | R-50-D8 | 512x512 | 40000 | - | - | 36.33 | 37.24 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet/pspnet_r50-d8_512x512_4x4_40k_coco-stuff10k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_4x4_40k_coco-stuff10k/pspnet_r50-d8_512x512_4x4_40k_coco-stuff10k_20210821_030857-92e2902b.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_4x4_40k_coco-stuff10k/pspnet_r50-d8_512x512_4x4_40k_coco-stuff10k_20210821_030857.log.json) |
| PSPNet | R-101-D8 | 512x512 | 40000 | - | - | 37.76 | 38.86 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet/pspnet_r101-d8_512x512_4x4_40k_coco-stuff10k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_4x4_40k_coco-stuff10k/pspnet_r101-d8_512x512_4x4_40k_coco-stuff10k_20210821_014022-831aec95.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_4x4_40k_coco-stuff10k/pspnet_r101-d8_512x512_4x4_40k_coco-stuff10k_20210821_014022.log.json) |
### COCO-Stuff 164k
| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download |
| ------ | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ----------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| PSPNet | R-50-D8 | 512x512 | 80000 | 9.6 | 20.5 | 38.80 | 39.19 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet/pspnet_r50-d8_512x512_4x4_80k_coco-stuff164k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_4x4_80k_coco-stuff164k/pspnet_r50-d8_512x512_4x4_80k_coco-stuff164k_20210707_152034-0e41b2db.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_4x4_80k_coco-stuff164k/pspnet_r50-d8_512x512_4x4_80k_coco-stuff164k_20210707_152034.log.json) |
| PSPNet | R-101-D8 | 512x512 | 80000 | 13.2 | 11.1 | 40.34 | 40.79 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet/pspnet_r101-d8_512x512_4x4_80k_coco-stuff164k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_4x4_80k_coco-stuff164k/pspnet_r101-d8_512x512_4x4_80k_coco-stuff164k_20210707_152034-7eb41789.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_4x4_80k_coco-stuff164k/pspnet_r101-d8_512x512_4x4_80k_coco-stuff164k_20210707_152034.log.json) |
| PSPNet | R-50-D8 | 512x512 | 160000 | - | - | 39.64 | 39.97 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet/pspnet_r50-d8_512x512_4x4_160k_coco-stuff164k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_4x4_160k_coco-stuff164k/pspnet_r50-d8_512x512_4x4_160k_coco-stuff164k_20210707_152004-51276a57.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_4x4_160k_coco-stuff164k/pspnet_r50-d8_512x512_4x4_160k_coco-stuff164k_20210707_152004.log.json) |
| PSPNet | R-101-D8 | 512x512 | 160000 | - | - | 41.28 | 41.66 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet/pspnet_r101-d8_512x512_4x4_160k_coco-stuff164k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_4x4_160k_coco-stuff164k/pspnet_r101-d8_512x512_4x4_160k_coco-stuff164k_20210707_152004-4af9621b.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_4x4_160k_coco-stuff164k/pspnet_r101-d8_512x512_4x4_160k_coco-stuff164k_20210707_152004.log.json) |
| PSPNet | R-50-D8 | 512x512 | 320000 | - | - | 40.53 | 40.75 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet/pspnet_r50-d8_512x512_4x4_320k_coco-stuff164k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_4x4_320k_coco-stuff164k/pspnet_r50-d8_512x512_4x4_320k_coco-stuff164k_20210707_152004-be9610cc.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_4x4_320k_coco-stuff164k/pspnet_r50-d8_512x512_4x4_320k_coco-stuff164k_20210707_152004.log.json) |
| PSPNet | R-101-D8 | 512x512 | 320000 | - | - | 41.95 | 42.42 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet/pspnet_r101-d8_512x512_4x4_320k_coco-stuff164k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_4x4_320k_coco-stuff164k/pspnet_r101-d8_512x512_4x4_320k_coco-stuff164k_20210707_152004-72220c60.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_4x4_320k_coco-stuff164k/pspnet_r101-d8_512x512_4x4_320k_coco-stuff164k_20210707_152004.log.json) |
### LoveDA
| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download |
| ------ | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ---------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| PSPNet | R-18-D8 | 512x512 | 80000 | 1.45 | 26.87 | 48.62 | 47.57 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet/pspnet_r18-d8_512x512_80k_loveda.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18-d8_512x512_80k_loveda/pspnet_r18-d8_512x512_80k_loveda_20211105_052100-b97697f1.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18-d8_512x512_80k_loveda/pspnet_r18-d8_512x512_80k_loveda_20211105_052100.log.json) |
| PSPNet | R-50-D8 | 512x512 | 80000 | 6.14 | 6.60 | 50.46 | 50.19 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet/pspnet_r50-d8_512x512_80k_loveda.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_80k_loveda/pspnet_r50-d8_512x512_80k_loveda_20211104_155728-88610f9f.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_80k_loveda/pspnet_r50-d8_512x512_80k_loveda_20211104_155728.log.json) |
| PSPNet | R-101-D8 | 512x512 | 80000 | 9.61 | 4.58 | 51.86 | 51.34 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet/pspnet_r101-d8_512x512_80k_loveda.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_80k_loveda/pspnet_r101-d8_512x512_80k_loveda_20211104_153212-1c06c6a8.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_80k_loveda/pspnet_r101-d8_512x512_80k_loveda_20211104_153212.log.json) |
### Potsdam
| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download |
| ------ | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | --------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| PSPNet | R-18-D8 | 512x512 | 80000 | 1.50 | 85.12 | 77.09 | 78.30 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet/pspnet_r18-d8_4x4_512x512_80k_potsdam.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18-d8_4x4_512x512_80k_potsdam/pspnet_r18-d8_4x4_512x512_80k_potsdam_20211220_125612-7cd046e1.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18-d8_4x4_512x512_80k_potsdam/pspnet_r18-d8_4x4_512x512_80k_potsdam_20211220_125612.log.json) |
| PSPNet | R-50-D8 | 512x512 | 80000 | 6.14 | 30.21 | 78.12 | 78.98 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet/pspnet_r50-d8_4x4_512x512_80k_potsdam.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_4x4_512x512_80k_potsdam/pspnet_r50-d8_4x4_512x512_80k_potsdam_20211219_043541-2dd5fe67.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_4x4_512x512_80k_potsdam/pspnet_r50-d8_4x4_512x512_80k_potsdam_20211219_043541.log.json) |
| PSPNet | R-101-D8 | 512x512 | 80000 | 9.61 | 19.40 | 78.62 | 79.47 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet/pspnet_r101-d8_4x4_512x512_80k_potsdam.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_4x4_512x512_80k_potsdam/pspnet_r101-d8_4x4_512x512_80k_potsdam_20211220_125612-aed036c4.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_4x4_512x512_80k_potsdam/pspnet_r101-d8_4x4_512x512_80k_potsdam_20211220_125612.log.json) |
### Vaihingen
| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download |
| ------ | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ----------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| PSPNet | R-18-D8 | 512x512 | 80000 | 1.45 | 85.06 | 71.46 | 73.36 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet/pspnet_r18-d8_4x4_512x512_80k_vaihingen.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18-d8_4x4_512x512_80k_vaihingen/pspnet_r18-d8_4x4_512x512_80k_vaihingen_20211228_160355-52a8a6f6.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18-d8_4x4_512x512_80k_vaihingen/pspnet_r18-d8_4x4_512x512_80k_vaihingen_20211228_160355.log.json) |
| PSPNet | R-50-D8 | 512x512 | 80000 | 6.14 | 30.29 | 72.36 | 73.75 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet/pspnet_r50-d8_4x4_512x512_80k_vaihingen.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_4x4_512x512_80k_vaihingen/pspnet_r50-d8_4x4_512x512_80k_vaihingen_20211228_160355-382f8f5b.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_4x4_512x512_80k_vaihingen/pspnet_r50-d8_4x4_512x512_80k_vaihingen_20211228_160355.log.json) |
| PSPNet | R-101-D8 | 512x512 | 80000 | 9.61 | 19.97 | 72.61 | 74.18 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet/pspnet_r101-d8_4x4_512x512_80k_vaihingen.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_4x4_512x512_80k_vaihingen/pspnet_r101-d8_4x4_512x512_80k_vaihingen_20211231_230806-8eba0a09.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_4x4_512x512_80k_vaihingen/pspnet_r101-d8_4x4_512x512_80k_vaihingen_20211231_230806.log.json) |
### iSAID
| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download |
| ------ | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ------------------------------------------------------------------------------------------------------------------------ | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| PSPNet | R-18-D8 | 896x896 | 80000 | 4.52 | 26.91 | 60.22 | 61.25 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet/pspnet_r18-d8_4x4_896x896_80k_isaid.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18-d8_4x4_896x896_80k_isaid/pspnet_r18-d8_4x4_896x896_80k_isaid_20220110_180526-e84c0b6a.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18-d8_4x4_896x896_80k_isaid/pspnet_r18-d8_4x4_896x896_80k_isaid_20220110_180526.log.json) |
| PSPNet | R-50-D8 | 896x896 | 80000 | 16.58 | 8.88 | 65.36 | 66.48 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet/pspnet_r50-d8_4x4_896x896_80k_isaid.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_4x4_896x896_80k_isaid/pspnet_r50-d8_4x4_896x896_80k_isaid_20220110_180629-1f21dc32.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_4x4_896x896_80k_isaid/pspnet_r50-d8_4x4_896x896_80k_isaid_20220110_180629.log.json) |
Note:
- `FP16` means Mixed Precision (FP16) is adopted in training.
- `896x896` is the Crop Size of iSAID dataset, which is followed by the implementation of [PointFlow: Flowing Semantics Through Points for Aerial Image Segmentation](https://arxiv.org/pdf/2103.06564.pdf)
- `rsb` is short for 'Resnet strikes back'.
- The `b` in `R-50b` means ResNetV1b, which is a standard ResNet backbone. In MMSegmentation, default backbone is ResNetV1c, which usually performs better in semantic segmentation task.
| 54,915 | 307.516854 | 780 | md |
mmsegmentation | mmsegmentation-master/configs/pspnet/pspnet.yml | Collections:
- Name: PSPNet
Metadata:
Training Data:
- Cityscapes
- ADE20K
- Pascal VOC 2012 + Aug
- Pascal Context
- Pascal Context 59
- Dark Zurich and Nighttime Driving
- COCO-Stuff 10k
- COCO-Stuff 164k
- LoveDA
- Potsdam
- Vaihingen
- iSAID
Paper:
URL: https://arxiv.org/abs/1612.01105
Title: Pyramid Scene Parsing Network
README: configs/pspnet/README.md
Code:
URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/psp_head.py#L63
Version: v0.17.0
Converted From:
Code: https://github.com/hszhao/PSPNet
Models:
- Name: pspnet_r50-d8_512x1024_40k_cityscapes
In Collection: PSPNet
Metadata:
backbone: R-50-D8
crop size: (512,1024)
lr schd: 40000
inference time (ms/im):
- value: 245.7
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,1024)
Training Memory (GB): 6.1
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 77.85
mIoU(ms+flip): 79.18
Config: configs/pspnet/pspnet_r50-d8_512x1024_40k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x1024_40k_cityscapes/pspnet_r50-d8_512x1024_40k_cityscapes_20200605_003338-2966598c.pth
- Name: pspnet_r101-d8_512x1024_40k_cityscapes
In Collection: PSPNet
Metadata:
backbone: R-101-D8
crop size: (512,1024)
lr schd: 40000
inference time (ms/im):
- value: 373.13
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,1024)
Training Memory (GB): 9.6
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 78.34
mIoU(ms+flip): 79.74
Config: configs/pspnet/pspnet_r101-d8_512x1024_40k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x1024_40k_cityscapes/pspnet_r101-d8_512x1024_40k_cityscapes_20200604_232751-467e7cf4.pth
- Name: pspnet_r50-d8_769x769_40k_cityscapes
In Collection: PSPNet
Metadata:
backbone: R-50-D8
crop size: (769,769)
lr schd: 40000
inference time (ms/im):
- value: 568.18
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (769,769)
Training Memory (GB): 6.9
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 78.26
mIoU(ms+flip): 79.88
Config: configs/pspnet/pspnet_r50-d8_769x769_40k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_769x769_40k_cityscapes/pspnet_r50-d8_769x769_40k_cityscapes_20200606_112725-86638686.pth
- Name: pspnet_r101-d8_769x769_40k_cityscapes
In Collection: PSPNet
Metadata:
backbone: R-101-D8
crop size: (769,769)
lr schd: 40000
inference time (ms/im):
- value: 869.57
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (769,769)
Training Memory (GB): 10.9
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.08
mIoU(ms+flip): 80.28
Config: configs/pspnet/pspnet_r101-d8_769x769_40k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_769x769_40k_cityscapes/pspnet_r101-d8_769x769_40k_cityscapes_20200606_112753-61c6f5be.pth
- Name: pspnet_r18-d8_512x1024_80k_cityscapes
In Collection: PSPNet
Metadata:
backbone: R-18-D8
crop size: (512,1024)
lr schd: 80000
inference time (ms/im):
- value: 63.65
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,1024)
Training Memory (GB): 1.7
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 74.87
mIoU(ms+flip): 76.04
Config: configs/pspnet/pspnet_r18-d8_512x1024_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18-d8_512x1024_80k_cityscapes/pspnet_r18-d8_512x1024_80k_cityscapes_20201225_021458-09ffa746.pth
- Name: pspnet_r50-d8_512x1024_80k_cityscapes
In Collection: PSPNet
Metadata:
backbone: R-50-D8
crop size: (512,1024)
lr schd: 80000
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 78.55
mIoU(ms+flip): 79.79
Config: configs/pspnet/pspnet_r50-d8_512x1024_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x1024_80k_cityscapes/pspnet_r50-d8_512x1024_80k_cityscapes_20200606_112131-2376f12b.pth
- Name: pspnet_r50-d8_rsb-pretrain_512x1024_adamw_80k_cityscapes
In Collection: PSPNet
Metadata:
backbone: R-50b-D8 rsb
crop size: (512,1024)
lr schd: 80000
inference time (ms/im):
- value: 261.78
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,1024)
Training Memory (GB): 6.2
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 78.47
mIoU(ms+flip): 79.45
Config: configs/pspnet/pspnet_r50-d8_rsb-pretrain_512x1024_adamw_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_rsb-pretrain_512x1024_adamw_80k_cityscapes/pspnet_r50-d8_rsb-pretrain_512x1024_adamw_80k_cityscapes_20220315_123238-588c30be.pth
- Name: pspnet_r101-d8_512x1024_80k_cityscapes
In Collection: PSPNet
Metadata:
backbone: R-101-D8
crop size: (512,1024)
lr schd: 80000
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.76
mIoU(ms+flip): 81.01
Config: configs/pspnet/pspnet_r101-d8_512x1024_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x1024_80k_cityscapes/pspnet_r101-d8_512x1024_80k_cityscapes_20200606_112211-e1e1100f.pth
- Name: pspnet_r101-d8_fp16_512x1024_80k_cityscapes
In Collection: PSPNet
Metadata:
backbone: R-101-D8
crop size: (512,1024)
lr schd: 80000
inference time (ms/im):
- value: 114.03
hardware: V100
backend: PyTorch
batch size: 1
mode: FP16
resolution: (512,1024)
Training Memory (GB): 5.34
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.46
Config: configs/pspnet/pspnet_r101-d8_fp16_512x1024_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_fp16_512x1024_80k_cityscapes/pspnet_r101-d8_fp16_512x1024_80k_cityscapes_20200717_230919-a0875e5c.pth
- Name: pspnet_r18-d8_769x769_80k_cityscapes
In Collection: PSPNet
Metadata:
backbone: R-18-D8
crop size: (769,769)
lr schd: 80000
inference time (ms/im):
- value: 161.29
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (769,769)
Training Memory (GB): 1.9
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 75.9
mIoU(ms+flip): 77.86
Config: configs/pspnet/pspnet_r18-d8_769x769_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18-d8_769x769_80k_cityscapes/pspnet_r18-d8_769x769_80k_cityscapes_20201225_021458-3deefc62.pth
- Name: pspnet_r50-d8_769x769_80k_cityscapes
In Collection: PSPNet
Metadata:
backbone: R-50-D8
crop size: (769,769)
lr schd: 80000
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.59
mIoU(ms+flip): 80.69
Config: configs/pspnet/pspnet_r50-d8_769x769_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_769x769_80k_cityscapes/pspnet_r50-d8_769x769_80k_cityscapes_20200606_210121-5ccf03dd.pth
- Name: pspnet_r101-d8_769x769_80k_cityscapes
In Collection: PSPNet
Metadata:
backbone: R-101-D8
crop size: (769,769)
lr schd: 80000
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.77
mIoU(ms+flip): 81.06
Config: configs/pspnet/pspnet_r101-d8_769x769_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_769x769_80k_cityscapes/pspnet_r101-d8_769x769_80k_cityscapes_20200606_225055-dba412fa.pth
- Name: pspnet_r18b-d8_512x1024_80k_cityscapes
In Collection: PSPNet
Metadata:
backbone: R-18b-D8
crop size: (512,1024)
lr schd: 80000
inference time (ms/im):
- value: 61.43
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,1024)
Training Memory (GB): 1.5
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 74.23
mIoU(ms+flip): 75.79
Config: configs/pspnet/pspnet_r18b-d8_512x1024_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18b-d8_512x1024_80k_cityscapes/pspnet_r18b-d8_512x1024_80k_cityscapes_20201226_063116-26928a60.pth
- Name: pspnet_r50b-d8_512x1024_80k_cityscapes
In Collection: PSPNet
Metadata:
backbone: R-50b-D8
crop size: (512,1024)
lr schd: 80000
inference time (ms/im):
- value: 232.56
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,1024)
Training Memory (GB): 6.0
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 78.22
mIoU(ms+flip): 79.46
Config: configs/pspnet/pspnet_r50b-d8_512x1024_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50b-d8_512x1024_80k_cityscapes/pspnet_r50b-d8_512x1024_80k_cityscapes_20201225_094315-6344287a.pth
- Name: pspnet_r101b-d8_512x1024_80k_cityscapes
In Collection: PSPNet
Metadata:
backbone: R-101b-D8
crop size: (512,1024)
lr schd: 80000
inference time (ms/im):
- value: 362.32
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,1024)
Training Memory (GB): 9.5
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.69
mIoU(ms+flip): 80.79
Config: configs/pspnet/pspnet_r101b-d8_512x1024_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101b-d8_512x1024_80k_cityscapes/pspnet_r101b-d8_512x1024_80k_cityscapes_20201226_170012-3a4d38ab.pth
- Name: pspnet_r18b-d8_769x769_80k_cityscapes
In Collection: PSPNet
Metadata:
backbone: R-18b-D8
crop size: (769,769)
lr schd: 80000
inference time (ms/im):
- value: 156.01
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (769,769)
Training Memory (GB): 1.7
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 74.92
mIoU(ms+flip): 76.9
Config: configs/pspnet/pspnet_r18b-d8_769x769_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18b-d8_769x769_80k_cityscapes/pspnet_r18b-d8_769x769_80k_cityscapes_20201226_080942-bf98d186.pth
- Name: pspnet_r50b-d8_769x769_80k_cityscapes
In Collection: PSPNet
Metadata:
backbone: R-50b-D8
crop size: (769,769)
lr schd: 80000
inference time (ms/im):
- value: 531.91
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (769,769)
Training Memory (GB): 6.8
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 78.5
mIoU(ms+flip): 79.96
Config: configs/pspnet/pspnet_r50b-d8_769x769_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50b-d8_769x769_80k_cityscapes/pspnet_r50b-d8_769x769_80k_cityscapes_20201225_094316-4c643cf6.pth
- Name: pspnet_r101b-d8_769x769_80k_cityscapes
In Collection: PSPNet
Metadata:
backbone: R-101b-D8
crop size: (769,769)
lr schd: 80000
inference time (ms/im):
- value: 854.7
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (769,769)
Training Memory (GB): 10.8
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 78.87
mIoU(ms+flip): 80.04
Config: configs/pspnet/pspnet_r101b-d8_769x769_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101b-d8_769x769_80k_cityscapes/pspnet_r101b-d8_769x769_80k_cityscapes_20201226_171823-f0e7c293.pth
- Name: pspnet_r50-d32_512x1024_80k_cityscapes
In Collection: PSPNet
Metadata:
backbone: R-50-D32
crop size: (512,1024)
lr schd: 80000
inference time (ms/im):
- value: 65.75
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,1024)
Training Memory (GB): 3.0
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 73.88
mIoU(ms+flip): 76.85
Config: configs/pspnet/pspnet_r50-d32_512x1024_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d32_512x1024_80k_cityscapes/pspnet_r50-d32_512x1024_80k_cityscapes_20220316_224840-9092b254.pth
- Name: pspnet_r50-d32_rsb-pretrain_512x1024_adamw_80k_cityscapes
In Collection: PSPNet
Metadata:
backbone: R-50b-D32 rsb
crop size: (512,1024)
lr schd: 80000
inference time (ms/im):
- value: 62.19
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,1024)
Training Memory (GB): 3.1
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 74.09
mIoU(ms+flip): 77.18
Config: configs/pspnet/pspnet_r50-d32_rsb-pretrain_512x1024_adamw_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d32_rsb-pretrain_512x1024_adamw_80k_cityscapes/pspnet_r50-d32_rsb-pretrain_512x1024_adamw_80k_cityscapes_20220316_141229-dd9c9610.pth
- Name: pspnet_r50b-d32_512x1024_80k_cityscapes
In Collection: PSPNet
Metadata:
backbone: R-50b-D32
crop size: (512,1024)
lr schd: 80000
inference time (ms/im):
- value: 64.89
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,1024)
Training Memory (GB): 2.9
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 72.61
mIoU(ms+flip): 75.51
Config: configs/pspnet/pspnet_r50b-d32_512x1024_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50b-d32_512x1024_80k_cityscapes/pspnet_r50b-d32_512x1024_80k_cityscapes_20220311_152152-23bcaf8c.pth
- Name: pspnet_r50-d8_512x512_80k_ade20k
In Collection: PSPNet
Metadata:
backbone: R-50-D8
crop size: (512,512)
lr schd: 80000
inference time (ms/im):
- value: 42.5
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 8.5
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 41.13
mIoU(ms+flip): 41.94
Config: configs/pspnet/pspnet_r50-d8_512x512_80k_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_80k_ade20k/pspnet_r50-d8_512x512_80k_ade20k_20200615_014128-15a8b914.pth
- Name: pspnet_r101-d8_512x512_80k_ade20k
In Collection: PSPNet
Metadata:
backbone: R-101-D8
crop size: (512,512)
lr schd: 80000
inference time (ms/im):
- value: 65.36
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 12.0
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 43.57
mIoU(ms+flip): 44.35
Config: configs/pspnet/pspnet_r101-d8_512x512_80k_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_80k_ade20k/pspnet_r101-d8_512x512_80k_ade20k_20200614_031423-b6e782f0.pth
- Name: pspnet_r50-d8_512x512_160k_ade20k
In Collection: PSPNet
Metadata:
backbone: R-50-D8
crop size: (512,512)
lr schd: 160000
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 42.48
mIoU(ms+flip): 43.44
Config: configs/pspnet/pspnet_r50-d8_512x512_160k_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_160k_ade20k/pspnet_r50-d8_512x512_160k_ade20k_20200615_184358-1890b0bd.pth
- Name: pspnet_r101-d8_512x512_160k_ade20k
In Collection: PSPNet
Metadata:
backbone: R-101-D8
crop size: (512,512)
lr schd: 160000
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 44.39
mIoU(ms+flip): 45.35
Config: configs/pspnet/pspnet_r101-d8_512x512_160k_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_160k_ade20k/pspnet_r101-d8_512x512_160k_ade20k_20200615_100650-967c316f.pth
- Name: pspnet_r50-d8_512x512_20k_voc12aug
In Collection: PSPNet
Metadata:
backbone: R-50-D8
crop size: (512,512)
lr schd: 20000
inference time (ms/im):
- value: 42.39
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 6.1
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 76.78
mIoU(ms+flip): 77.61
Config: configs/pspnet/pspnet_r50-d8_512x512_20k_voc12aug.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_20k_voc12aug/pspnet_r50-d8_512x512_20k_voc12aug_20200617_101958-ed5dfbd9.pth
- Name: pspnet_r101-d8_512x512_20k_voc12aug
In Collection: PSPNet
Metadata:
backbone: R-101-D8
crop size: (512,512)
lr schd: 20000
inference time (ms/im):
- value: 66.58
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 9.6
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 78.47
mIoU(ms+flip): 79.25
Config: configs/pspnet/pspnet_r101-d8_512x512_20k_voc12aug.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_20k_voc12aug/pspnet_r101-d8_512x512_20k_voc12aug_20200617_102003-4aef3c9a.pth
- Name: pspnet_r50-d8_512x512_40k_voc12aug
In Collection: PSPNet
Metadata:
backbone: R-50-D8
crop size: (512,512)
lr schd: 40000
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 77.29
mIoU(ms+flip): 78.48
Config: configs/pspnet/pspnet_r50-d8_512x512_40k_voc12aug.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_40k_voc12aug/pspnet_r50-d8_512x512_40k_voc12aug_20200613_161222-ae9c1b8c.pth
- Name: pspnet_r101-d8_512x512_40k_voc12aug
In Collection: PSPNet
Metadata:
backbone: R-101-D8
crop size: (512,512)
lr schd: 40000
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 78.52
mIoU(ms+flip): 79.57
Config: configs/pspnet/pspnet_r101-d8_512x512_40k_voc12aug.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_40k_voc12aug/pspnet_r101-d8_512x512_40k_voc12aug_20200613_161222-bc933b18.pth
- Name: pspnet_r101-d8_480x480_40k_pascal_context
In Collection: PSPNet
Metadata:
backbone: R-101-D8
crop size: (480,480)
lr schd: 40000
inference time (ms/im):
- value: 103.31
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (480,480)
Training Memory (GB): 8.8
Results:
- Task: Semantic Segmentation
Dataset: Pascal Context
Metrics:
mIoU: 46.6
mIoU(ms+flip): 47.78
Config: configs/pspnet/pspnet_r101-d8_480x480_40k_pascal_context.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_480x480_40k_pascal_context/pspnet_r101-d8_480x480_40k_pascal_context_20200911_211210-bf0f5d7c.pth
- Name: pspnet_r101-d8_480x480_80k_pascal_context
In Collection: PSPNet
Metadata:
backbone: R-101-D8
crop size: (480,480)
lr schd: 80000
Results:
- Task: Semantic Segmentation
Dataset: Pascal Context
Metrics:
mIoU: 46.03
mIoU(ms+flip): 47.15
Config: configs/pspnet/pspnet_r101-d8_480x480_80k_pascal_context.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_480x480_80k_pascal_context/pspnet_r101-d8_480x480_80k_pascal_context_20200911_190530-c86d6233.pth
- Name: pspnet_r101-d8_480x480_40k_pascal_context_59
In Collection: PSPNet
Metadata:
backbone: R-101-D8
crop size: (480,480)
lr schd: 40000
Results:
- Task: Semantic Segmentation
Dataset: Pascal Context 59
Metrics:
mIoU: 52.02
mIoU(ms+flip): 53.54
Config: configs/pspnet/pspnet_r101-d8_480x480_40k_pascal_context_59.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_480x480_40k_pascal_context_59/pspnet_r101-d8_480x480_40k_pascal_context_59_20210416_114524-86d44cd4.pth
- Name: pspnet_r101-d8_480x480_80k_pascal_context_59
In Collection: PSPNet
Metadata:
backbone: R-101-D8
crop size: (480,480)
lr schd: 80000
Results:
- Task: Semantic Segmentation
Dataset: Pascal Context 59
Metrics:
mIoU: 52.47
mIoU(ms+flip): 53.99
Config: configs/pspnet/pspnet_r101-d8_480x480_80k_pascal_context_59.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_480x480_80k_pascal_context_59/pspnet_r101-d8_480x480_80k_pascal_context_59_20210416_114418-fa6caaa2.pth
- Name: pspnet_r50-d8_512x512_4x4_20k_coco-stuff10k
In Collection: PSPNet
Metadata:
backbone: R-50-D8
crop size: (512,512)
lr schd: 20000
inference time (ms/im):
- value: 48.78
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 9.6
Results:
- Task: Semantic Segmentation
Dataset: COCO-Stuff 10k
Metrics:
mIoU: 35.69
mIoU(ms+flip): 36.62
Config: configs/pspnet/pspnet_r50-d8_512x512_4x4_20k_coco-stuff10k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_4x4_20k_coco-stuff10k/pspnet_r50-d8_512x512_4x4_20k_coco-stuff10k_20210820_203258-b88df27f.pth
- Name: pspnet_r101-d8_512x512_4x4_20k_coco-stuff10k
In Collection: PSPNet
Metadata:
backbone: R-101-D8
crop size: (512,512)
lr schd: 20000
inference time (ms/im):
- value: 90.09
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 13.2
Results:
- Task: Semantic Segmentation
Dataset: COCO-Stuff 10k
Metrics:
mIoU: 37.26
mIoU(ms+flip): 38.52
Config: configs/pspnet/pspnet_r101-d8_512x512_4x4_20k_coco-stuff10k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_4x4_20k_coco-stuff10k/pspnet_r101-d8_512x512_4x4_20k_coco-stuff10k_20210820_232135-76aae482.pth
- Name: pspnet_r50-d8_512x512_4x4_40k_coco-stuff10k
In Collection: PSPNet
Metadata:
backbone: R-50-D8
crop size: (512,512)
lr schd: 40000
Results:
- Task: Semantic Segmentation
Dataset: COCO-Stuff 10k
Metrics:
mIoU: 36.33
mIoU(ms+flip): 37.24
Config: configs/pspnet/pspnet_r50-d8_512x512_4x4_40k_coco-stuff10k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_4x4_40k_coco-stuff10k/pspnet_r50-d8_512x512_4x4_40k_coco-stuff10k_20210821_030857-92e2902b.pth
- Name: pspnet_r101-d8_512x512_4x4_40k_coco-stuff10k
In Collection: PSPNet
Metadata:
backbone: R-101-D8
crop size: (512,512)
lr schd: 40000
Results:
- Task: Semantic Segmentation
Dataset: COCO-Stuff 10k
Metrics:
mIoU: 37.76
mIoU(ms+flip): 38.86
Config: configs/pspnet/pspnet_r101-d8_512x512_4x4_40k_coco-stuff10k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_4x4_40k_coco-stuff10k/pspnet_r101-d8_512x512_4x4_40k_coco-stuff10k_20210821_014022-831aec95.pth
- Name: pspnet_r50-d8_512x512_4x4_80k_coco-stuff164k
In Collection: PSPNet
Metadata:
backbone: R-50-D8
crop size: (512,512)
lr schd: 80000
inference time (ms/im):
- value: 48.78
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 9.6
Results:
- Task: Semantic Segmentation
Dataset: COCO-Stuff 164k
Metrics:
mIoU: 38.8
mIoU(ms+flip): 39.19
Config: configs/pspnet/pspnet_r50-d8_512x512_4x4_80k_coco-stuff164k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_4x4_80k_coco-stuff164k/pspnet_r50-d8_512x512_4x4_80k_coco-stuff164k_20210707_152034-0e41b2db.pth
- Name: pspnet_r101-d8_512x512_4x4_80k_coco-stuff164k
In Collection: PSPNet
Metadata:
backbone: R-101-D8
crop size: (512,512)
lr schd: 80000
inference time (ms/im):
- value: 90.09
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 13.2
Results:
- Task: Semantic Segmentation
Dataset: COCO-Stuff 164k
Metrics:
mIoU: 40.34
mIoU(ms+flip): 40.79
Config: configs/pspnet/pspnet_r101-d8_512x512_4x4_80k_coco-stuff164k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_4x4_80k_coco-stuff164k/pspnet_r101-d8_512x512_4x4_80k_coco-stuff164k_20210707_152034-7eb41789.pth
- Name: pspnet_r50-d8_512x512_4x4_160k_coco-stuff164k
In Collection: PSPNet
Metadata:
backbone: R-50-D8
crop size: (512,512)
lr schd: 160000
Results:
- Task: Semantic Segmentation
Dataset: COCO-Stuff 164k
Metrics:
mIoU: 39.64
mIoU(ms+flip): 39.97
Config: configs/pspnet/pspnet_r50-d8_512x512_4x4_160k_coco-stuff164k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_4x4_160k_coco-stuff164k/pspnet_r50-d8_512x512_4x4_160k_coco-stuff164k_20210707_152004-51276a57.pth
- Name: pspnet_r101-d8_512x512_4x4_160k_coco-stuff164k
In Collection: PSPNet
Metadata:
backbone: R-101-D8
crop size: (512,512)
lr schd: 160000
Results:
- Task: Semantic Segmentation
Dataset: COCO-Stuff 164k
Metrics:
mIoU: 41.28
mIoU(ms+flip): 41.66
Config: configs/pspnet/pspnet_r101-d8_512x512_4x4_160k_coco-stuff164k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_4x4_160k_coco-stuff164k/pspnet_r101-d8_512x512_4x4_160k_coco-stuff164k_20210707_152004-4af9621b.pth
- Name: pspnet_r50-d8_512x512_4x4_320k_coco-stuff164k
In Collection: PSPNet
Metadata:
backbone: R-50-D8
crop size: (512,512)
lr schd: 320000
Results:
- Task: Semantic Segmentation
Dataset: COCO-Stuff 164k
Metrics:
mIoU: 40.53
mIoU(ms+flip): 40.75
Config: configs/pspnet/pspnet_r50-d8_512x512_4x4_320k_coco-stuff164k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_4x4_320k_coco-stuff164k/pspnet_r50-d8_512x512_4x4_320k_coco-stuff164k_20210707_152004-be9610cc.pth
- Name: pspnet_r101-d8_512x512_4x4_320k_coco-stuff164k
In Collection: PSPNet
Metadata:
backbone: R-101-D8
crop size: (512,512)
lr schd: 320000
Results:
- Task: Semantic Segmentation
Dataset: COCO-Stuff 164k
Metrics:
mIoU: 41.95
mIoU(ms+flip): 42.42
Config: configs/pspnet/pspnet_r101-d8_512x512_4x4_320k_coco-stuff164k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_4x4_320k_coco-stuff164k/pspnet_r101-d8_512x512_4x4_320k_coco-stuff164k_20210707_152004-72220c60.pth
- Name: pspnet_r18-d8_512x512_80k_loveda
In Collection: PSPNet
Metadata:
backbone: R-18-D8
crop size: (512,512)
lr schd: 80000
inference time (ms/im):
- value: 37.22
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 1.45
Results:
- Task: Semantic Segmentation
Dataset: LoveDA
Metrics:
mIoU: 48.62
mIoU(ms+flip): 47.57
Config: configs/pspnet/pspnet_r18-d8_512x512_80k_loveda.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18-d8_512x512_80k_loveda/pspnet_r18-d8_512x512_80k_loveda_20211105_052100-b97697f1.pth
- Name: pspnet_r50-d8_512x512_80k_loveda
In Collection: PSPNet
Metadata:
backbone: R-50-D8
crop size: (512,512)
lr schd: 80000
inference time (ms/im):
- value: 151.52
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 6.14
Results:
- Task: Semantic Segmentation
Dataset: LoveDA
Metrics:
mIoU: 50.46
mIoU(ms+flip): 50.19
Config: configs/pspnet/pspnet_r50-d8_512x512_80k_loveda.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_80k_loveda/pspnet_r50-d8_512x512_80k_loveda_20211104_155728-88610f9f.pth
- Name: pspnet_r101-d8_512x512_80k_loveda
In Collection: PSPNet
Metadata:
backbone: R-101-D8
crop size: (512,512)
lr schd: 80000
inference time (ms/im):
- value: 218.34
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 9.61
Results:
- Task: Semantic Segmentation
Dataset: LoveDA
Metrics:
mIoU: 51.86
mIoU(ms+flip): 51.34
Config: configs/pspnet/pspnet_r101-d8_512x512_80k_loveda.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_80k_loveda/pspnet_r101-d8_512x512_80k_loveda_20211104_153212-1c06c6a8.pth
- Name: pspnet_r18-d8_4x4_512x512_80k_potsdam
In Collection: PSPNet
Metadata:
backbone: R-18-D8
crop size: (512,512)
lr schd: 80000
inference time (ms/im):
- value: 11.75
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 1.5
Results:
- Task: Semantic Segmentation
Dataset: Potsdam
Metrics:
mIoU: 77.09
mIoU(ms+flip): 78.3
Config: configs/pspnet/pspnet_r18-d8_4x4_512x512_80k_potsdam.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18-d8_4x4_512x512_80k_potsdam/pspnet_r18-d8_4x4_512x512_80k_potsdam_20211220_125612-7cd046e1.pth
- Name: pspnet_r50-d8_4x4_512x512_80k_potsdam
In Collection: PSPNet
Metadata:
backbone: R-50-D8
crop size: (512,512)
lr schd: 80000
inference time (ms/im):
- value: 33.1
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 6.14
Results:
- Task: Semantic Segmentation
Dataset: Potsdam
Metrics:
mIoU: 78.12
mIoU(ms+flip): 78.98
Config: configs/pspnet/pspnet_r50-d8_4x4_512x512_80k_potsdam.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_4x4_512x512_80k_potsdam/pspnet_r50-d8_4x4_512x512_80k_potsdam_20211219_043541-2dd5fe67.pth
- Name: pspnet_r101-d8_4x4_512x512_80k_potsdam
In Collection: PSPNet
Metadata:
backbone: R-101-D8
crop size: (512,512)
lr schd: 80000
inference time (ms/im):
- value: 51.55
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 9.61
Results:
- Task: Semantic Segmentation
Dataset: Potsdam
Metrics:
mIoU: 78.62
mIoU(ms+flip): 79.47
Config: configs/pspnet/pspnet_r101-d8_4x4_512x512_80k_potsdam.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_4x4_512x512_80k_potsdam/pspnet_r101-d8_4x4_512x512_80k_potsdam_20211220_125612-aed036c4.pth
- Name: pspnet_r18-d8_4x4_512x512_80k_vaihingen
In Collection: PSPNet
Metadata:
backbone: R-18-D8
crop size: (512,512)
lr schd: 80000
inference time (ms/im):
- value: 11.76
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 1.45
Results:
- Task: Semantic Segmentation
Dataset: Vaihingen
Metrics:
mIoU: 71.46
mIoU(ms+flip): 73.36
Config: configs/pspnet/pspnet_r18-d8_4x4_512x512_80k_vaihingen.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18-d8_4x4_512x512_80k_vaihingen/pspnet_r18-d8_4x4_512x512_80k_vaihingen_20211228_160355-52a8a6f6.pth
- Name: pspnet_r50-d8_4x4_512x512_80k_vaihingen
In Collection: PSPNet
Metadata:
backbone: R-50-D8
crop size: (512,512)
lr schd: 80000
inference time (ms/im):
- value: 33.01
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 6.14
Results:
- Task: Semantic Segmentation
Dataset: Vaihingen
Metrics:
mIoU: 72.36
mIoU(ms+flip): 73.75
Config: configs/pspnet/pspnet_r50-d8_4x4_512x512_80k_vaihingen.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_4x4_512x512_80k_vaihingen/pspnet_r50-d8_4x4_512x512_80k_vaihingen_20211228_160355-382f8f5b.pth
- Name: pspnet_r101-d8_4x4_512x512_80k_vaihingen
In Collection: PSPNet
Metadata:
backbone: R-101-D8
crop size: (512,512)
lr schd: 80000
inference time (ms/im):
- value: 50.08
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 9.61
Results:
- Task: Semantic Segmentation
Dataset: Vaihingen
Metrics:
mIoU: 72.61
mIoU(ms+flip): 74.18
Config: configs/pspnet/pspnet_r101-d8_4x4_512x512_80k_vaihingen.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_4x4_512x512_80k_vaihingen/pspnet_r101-d8_4x4_512x512_80k_vaihingen_20211231_230806-8eba0a09.pth
- Name: pspnet_r18-d8_4x4_896x896_80k_isaid
In Collection: PSPNet
Metadata:
backbone: R-18-D8
crop size: (896,896)
lr schd: 80000
inference time (ms/im):
- value: 37.16
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (896,896)
Training Memory (GB): 4.52
Results:
- Task: Semantic Segmentation
Dataset: iSAID
Metrics:
mIoU: 60.22
mIoU(ms+flip): 61.25
Config: configs/pspnet/pspnet_r18-d8_4x4_896x896_80k_isaid.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18-d8_4x4_896x896_80k_isaid/pspnet_r18-d8_4x4_896x896_80k_isaid_20220110_180526-e84c0b6a.pth
- Name: pspnet_r50-d8_4x4_896x896_80k_isaid
In Collection: PSPNet
Metadata:
backbone: R-50-D8
crop size: (896,896)
lr schd: 80000
inference time (ms/im):
- value: 112.61
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (896,896)
Training Memory (GB): 16.58
Results:
- Task: Semantic Segmentation
Dataset: iSAID
Metrics:
mIoU: 65.36
mIoU(ms+flip): 66.48
Config: configs/pspnet/pspnet_r50-d8_4x4_896x896_80k_isaid.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_4x4_896x896_80k_isaid/pspnet_r50-d8_4x4_896x896_80k_isaid_20220110_180629-1f21dc32.pth
| 35,801 | 32.211503 | 213 | yml |
mmsegmentation | mmsegmentation-master/configs/pspnet/pspnet_r101-d8_480x480_40k_pascal_context.py | _base_ = './pspnet_r50-d8_480x480_40k_pascal_context.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
| 137 | 45 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/pspnet/pspnet_r101-d8_480x480_40k_pascal_context_59.py | _base_ = './pspnet_r50-d8_480x480_40k_pascal_context_59.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
| 140 | 46 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/pspnet/pspnet_r101-d8_480x480_80k_pascal_context.py | _base_ = './pspnet_r50-d8_480x480_80k_pascal_context.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
| 137 | 45 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/pspnet/pspnet_r101-d8_480x480_80k_pascal_context_59.py | _base_ = './pspnet_r50-d8_480x480_80k_pascal_context_59.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
| 140 | 46 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/pspnet/pspnet_r101-d8_4x4_512x512_80k_potsdam.py | _base_ = './pspnet_r50-d8_4x4_512x512_80k_potsdam.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
| 134 | 44 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/pspnet/pspnet_r101-d8_4x4_512x512_80k_vaihingen.py | _base_ = './pspnet_r50-d8_4x4_512x512_80k_vaihingen.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
| 136 | 44.666667 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/pspnet/pspnet_r101-d8_512x1024_40k_cityscapes.py | _base_ = './pspnet_r50-d8_512x1024_40k_cityscapes.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
| 134 | 44 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/pspnet/pspnet_r101-d8_512x1024_40k_dark.py | _base_ = './pspnet_r50-d8_512x1024_40k_dark.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
| 128 | 42 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/pspnet/pspnet_r101-d8_512x1024_40k_night_driving.py | _base_ = './pspnet_r50-d8_512x1024_40k_night_driving.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
| 137 | 45 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/pspnet/pspnet_r101-d8_512x1024_80k_cityscapes.py | _base_ = './pspnet_r50-d8_512x1024_80k_cityscapes.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
| 134 | 44 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/pspnet/pspnet_r101-d8_512x512_160k_ade20k.py | _base_ = './pspnet_r50-d8_512x512_160k_ade20k.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
| 130 | 42.666667 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/pspnet/pspnet_r101-d8_512x512_20k_voc12aug.py | _base_ = './pspnet_r50-d8_512x512_20k_voc12aug.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
| 131 | 43 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/pspnet/pspnet_r101-d8_512x512_40k_voc12aug.py | _base_ = './pspnet_r50-d8_512x512_40k_voc12aug.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
| 131 | 43 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/pspnet/pspnet_r101-d8_512x512_4x4_160k_coco-stuff164k.py | _base_ = './pspnet_r50-d8_512x512_4x4_160k_coco-stuff164k.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
| 142 | 46.666667 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/pspnet/pspnet_r101-d8_512x512_4x4_20k_coco-stuff10k.py | _base_ = './pspnet_r50-d8_512x512_4x4_20k_coco-stuff10k.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
| 140 | 46 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/pspnet/pspnet_r101-d8_512x512_4x4_320k_coco-stuff164k.py | _base_ = './pspnet_r50-d8_512x512_4x4_320k_coco-stuff164k.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
| 142 | 46.666667 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/pspnet/pspnet_r101-d8_512x512_4x4_40k_coco-stuff10k.py | _base_ = './pspnet_r50-d8_512x512_4x4_40k_coco-stuff10k.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
| 140 | 46 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/pspnet/pspnet_r101-d8_512x512_4x4_80k_coco-stuff164k.py | _base_ = './pspnet_r50-d8_512x512_4x4_80k_coco-stuff164k.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
| 141 | 46.333333 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/pspnet/pspnet_r101-d8_512x512_80k_ade20k.py | _base_ = './pspnet_r50-d8_512x512_80k_ade20k.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
| 129 | 42.333333 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/pspnet/pspnet_r101-d8_512x512_80k_loveda.py | _base_ = './pspnet_r50-d8_512x512_80k_loveda.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://resnet101_v1c')))
| 198 | 27.428571 | 73 | py |
mmsegmentation | mmsegmentation-master/configs/pspnet/pspnet_r101-d8_769x769_40k_cityscapes.py | _base_ = './pspnet_r50-d8_769x769_40k_cityscapes.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
| 133 | 43.666667 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/pspnet/pspnet_r101-d8_769x769_80k_cityscapes.py | _base_ = './pspnet_r50-d8_769x769_80k_cityscapes.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
| 133 | 43.666667 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/pspnet/pspnet_r101-d8_fp16_512x1024_80k_cityscapes.py | _base_ = './pspnet_r101-d8_512x1024_80k_cityscapes.py'
# fp16 settings
optimizer_config = dict(type='Fp16OptimizerHook', loss_scale=512.)
# fp16 placeholder
fp16 = dict()
| 171 | 27.666667 | 66 | py |
mmsegmentation | mmsegmentation-master/configs/pspnet/pspnet_r101b-d8_512x1024_80k_cityscapes.py | _base_ = './pspnet_r50-d8_512x1024_80k_cityscapes.py'
model = dict(
pretrained='torchvision://resnet101',
backbone=dict(type='ResNet', depth=101))
| 155 | 30.2 | 53 | py |
mmsegmentation | mmsegmentation-master/configs/pspnet/pspnet_r101b-d8_512x1024_80k_dark.py | _base_ = './pspnet_r50-d8_512x1024_80k_dark.py'
model = dict(
pretrained='torchvision://resnet101',
backbone=dict(type='ResNet', depth=101))
| 149 | 29 | 47 | py |
mmsegmentation | mmsegmentation-master/configs/pspnet/pspnet_r101b-d8_512x1024_80k_night_driving.py | _base_ = './pspnet_r50-d8_512x1024_80k_night_driving.py'
model = dict(
pretrained='torchvision://resnet101',
backbone=dict(type='ResNet', depth=101))
| 158 | 30.8 | 56 | py |
mmsegmentation | mmsegmentation-master/configs/pspnet/pspnet_r101b-d8_769x769_80k_cityscapes.py | _base_ = './pspnet_r50-d8_769x769_80k_cityscapes.py'
model = dict(
pretrained='torchvision://resnet101',
backbone=dict(type='ResNet', depth=101))
| 154 | 30 | 52 | py |
mmsegmentation | mmsegmentation-master/configs/pspnet/pspnet_r18-d8_4x4_512x512_80k_potsdam.py | _base_ = './pspnet_r50-d8_4x4_512x512_80k_potsdam.py'
model = dict(
pretrained='open-mmlab://resnet18_v1c',
backbone=dict(depth=18),
decode_head=dict(
in_channels=512,
channels=128,
),
auxiliary_head=dict(in_channels=256, channels=64))
| 272 | 26.3 | 54 | py |
mmsegmentation | mmsegmentation-master/configs/pspnet/pspnet_r18-d8_4x4_512x512_80k_vaihingen.py | _base_ = './pspnet_r50-d8_4x4_512x512_80k_vaihingen.py'
model = dict(
pretrained='open-mmlab://resnet18_v1c',
backbone=dict(depth=18),
decode_head=dict(
in_channels=512,
channels=128,
),
auxiliary_head=dict(in_channels=256, channels=64))
| 274 | 26.5 | 55 | py |
mmsegmentation | mmsegmentation-master/configs/pspnet/pspnet_r18-d8_4x4_896x896_80k_isaid.py | _base_ = './pspnet_r50-d8_4x4_896x896_80k_isaid.py'
model = dict(
pretrained='open-mmlab://resnet18_v1c',
backbone=dict(depth=18),
decode_head=dict(
in_channels=512,
channels=128,
),
auxiliary_head=dict(in_channels=256, channels=64))
| 270 | 26.1 | 54 | py |
mmsegmentation | mmsegmentation-master/configs/pspnet/pspnet_r18-d8_512x1024_80k_cityscapes.py | _base_ = './pspnet_r50-d8_512x1024_80k_cityscapes.py'
model = dict(
pretrained='open-mmlab://resnet18_v1c',
backbone=dict(depth=18),
decode_head=dict(
in_channels=512,
channels=128,
),
auxiliary_head=dict(in_channels=256, channels=64))
| 272 | 26.3 | 54 | py |
mmsegmentation | mmsegmentation-master/configs/pspnet/pspnet_r18-d8_512x512_80k_loveda.py | _base_ = './pspnet_r50-d8_512x512_80k_loveda.py'
model = dict(
backbone=dict(
depth=18,
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://resnet18_v1c')),
decode_head=dict(
in_channels=512,
channels=128,
),
auxiliary_head=dict(in_channels=256, channels=64))
| 327 | 26.333333 | 72 | py |
mmsegmentation | mmsegmentation-master/configs/pspnet/pspnet_r18-d8_769x769_80k_cityscapes.py | _base_ = './pspnet_r50-d8_769x769_80k_cityscapes.py'
model = dict(
pretrained='open-mmlab://resnet18_v1c',
backbone=dict(depth=18),
decode_head=dict(
in_channels=512,
channels=128,
),
auxiliary_head=dict(in_channels=256, channels=64))
| 271 | 26.2 | 54 | py |
mmsegmentation | mmsegmentation-master/configs/pspnet/pspnet_r18b-d8_512x1024_80k_cityscapes.py | _base_ = './pspnet_r50-d8_512x1024_80k_cityscapes.py'
model = dict(
pretrained='torchvision://resnet18',
backbone=dict(type='ResNet', depth=18),
decode_head=dict(
in_channels=512,
channels=128,
),
auxiliary_head=dict(in_channels=256, channels=64))
| 284 | 27.5 | 54 | py |
mmsegmentation | mmsegmentation-master/configs/pspnet/pspnet_r18b-d8_769x769_80k_cityscapes.py | _base_ = './pspnet_r50-d8_769x769_80k_cityscapes.py'
model = dict(
pretrained='torchvision://resnet18',
backbone=dict(type='ResNet', depth=18),
decode_head=dict(
in_channels=512,
channels=128,
),
auxiliary_head=dict(in_channels=256, channels=64))
| 283 | 27.4 | 54 | py |
mmsegmentation | mmsegmentation-master/configs/pspnet/pspnet_r50-d32_512x1024_80k_cityscapes.py | _base_ = [
'../_base_/models/pspnet_r50-d8.py', '../_base_/datasets/cityscapes.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
]
model = dict(backbone=dict(dilations=(1, 1, 2, 4), strides=(1, 2, 2, 2)))
| 238 | 38.833333 | 76 | py |
mmsegmentation | mmsegmentation-master/configs/pspnet/pspnet_r50-d32_rsb-pretrain_512x1024_adamw_80k_cityscapes.py | _base_ = [
'../_base_/models/pspnet_r50-d8.py', '../_base_/datasets/cityscapes.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
]
checkpoint = 'https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb256-rsb-a1-600e_in1k_20211228-20e21305.pth' # noqa
model = dict(
pretrained=None,
backbone=dict(
type='ResNet',
init_cfg=dict(
type='Pretrained', prefix='backbone.', checkpoint=checkpoint),
dilations=(1, 1, 2, 4),
strides=(1, 2, 2, 2)))
optimizer = dict(_delete_=True, type='AdamW', lr=0.0005, weight_decay=0.05)
optimizer_config = dict(grad_clip=dict(max_norm=1, norm_type=2))
# learning policy
lr_config = dict(
_delete_=True,
policy='step',
warmup='linear',
warmup_iters=1000,
warmup_ratio=0.001,
step=[60000, 72000],
by_epoch=False)
| 867 | 32.384615 | 135 | py |
mmsegmentation | mmsegmentation-master/configs/pspnet/pspnet_r50-d8_480x480_40k_pascal_context.py | _base_ = [
'../_base_/models/pspnet_r50-d8.py',
'../_base_/datasets/pascal_context.py', '../_base_/default_runtime.py',
'../_base_/schedules/schedule_40k.py'
]
model = dict(
decode_head=dict(num_classes=60),
auxiliary_head=dict(num_classes=60),
test_cfg=dict(mode='slide', crop_size=(480, 480), stride=(320, 320)))
optimizer = dict(type='SGD', lr=0.004, momentum=0.9, weight_decay=0.0001)
| 413 | 36.636364 | 75 | py |
mmsegmentation | mmsegmentation-master/configs/pspnet/pspnet_r50-d8_480x480_40k_pascal_context_59.py | _base_ = [
'../_base_/models/pspnet_r50-d8.py',
'../_base_/datasets/pascal_context_59.py', '../_base_/default_runtime.py',
'../_base_/schedules/schedule_40k.py'
]
model = dict(
decode_head=dict(num_classes=59),
auxiliary_head=dict(num_classes=59),
test_cfg=dict(mode='slide', crop_size=(480, 480), stride=(320, 320)))
optimizer = dict(type='SGD', lr=0.004, momentum=0.9, weight_decay=0.0001)
| 416 | 36.909091 | 78 | py |
mmsegmentation | mmsegmentation-master/configs/pspnet/pspnet_r50-d8_480x480_80k_pascal_context.py | _base_ = [
'../_base_/models/pspnet_r50-d8.py',
'../_base_/datasets/pascal_context.py', '../_base_/default_runtime.py',
'../_base_/schedules/schedule_80k.py'
]
model = dict(
decode_head=dict(num_classes=60),
auxiliary_head=dict(num_classes=60),
test_cfg=dict(mode='slide', crop_size=(480, 480), stride=(320, 320)))
optimizer = dict(type='SGD', lr=0.004, momentum=0.9, weight_decay=0.0001)
| 413 | 36.636364 | 75 | py |
mmsegmentation | mmsegmentation-master/configs/pspnet/pspnet_r50-d8_480x480_80k_pascal_context_59.py | _base_ = [
'../_base_/models/pspnet_r50-d8.py',
'../_base_/datasets/pascal_context_59.py', '../_base_/default_runtime.py',
'../_base_/schedules/schedule_80k.py'
]
model = dict(
decode_head=dict(num_classes=59),
auxiliary_head=dict(num_classes=59),
test_cfg=dict(mode='slide', crop_size=(480, 480), stride=(320, 320)))
optimizer = dict(type='SGD', lr=0.004, momentum=0.9, weight_decay=0.0001)
| 416 | 36.909091 | 78 | py |
mmsegmentation | mmsegmentation-master/configs/pspnet/pspnet_r50-d8_4x4_512x512_80k_potsdam.py | _base_ = [
'../_base_/models/pspnet_r50-d8.py', '../_base_/datasets/potsdam.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
]
model = dict(
decode_head=dict(num_classes=6), auxiliary_head=dict(num_classes=6))
| 248 | 34.571429 | 73 | py |
mmsegmentation | mmsegmentation-master/configs/pspnet/pspnet_r50-d8_4x4_512x512_80k_vaihingen.py | _base_ = [
'../_base_/models/pspnet_r50-d8.py', '../_base_/datasets/vaihingen.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
]
model = dict(
decode_head=dict(num_classes=6), auxiliary_head=dict(num_classes=6))
| 250 | 34.857143 | 75 | py |
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