Search is not available for this dataset
repo stringlengths 2 152 ⌀ | file stringlengths 15 239 | code stringlengths 0 58.4M | file_length int64 0 58.4M | avg_line_length float64 0 1.81M | max_line_length int64 0 12.7M | extension_type stringclasses 364
values |
|---|---|---|---|---|---|---|
mmsegmentation | mmsegmentation-master/configs/deeplabv3plus/deeplabv3plus_r18-d8_4x4_896x896_80k_isaid.py | _base_ = './deeplabv3plus_r50-d8_4x4_896x896_80k_isaid.py'
model = dict(
pretrained='open-mmlab://resnet18_v1c',
backbone=dict(depth=18),
decode_head=dict(
c1_in_channels=64,
c1_channels=12,
in_channels=512,
channels=128,
),
auxiliary_head=dict(in_channels=256, channe... | 328 | 26.416667 | 58 | py |
mmsegmentation | mmsegmentation-master/configs/deeplabv3plus/deeplabv3plus_r18-d8_512x1024_80k_cityscapes.py | _base_ = './deeplabv3plus_r50-d8_512x1024_80k_cityscapes.py'
model = dict(
pretrained='open-mmlab://resnet18_v1c',
backbone=dict(depth=18),
decode_head=dict(
c1_in_channels=64,
c1_channels=12,
in_channels=512,
channels=128,
),
auxiliary_head=dict(in_channels=256, chan... | 330 | 26.583333 | 60 | py |
mmsegmentation | mmsegmentation-master/configs/deeplabv3plus/deeplabv3plus_r18-d8_512x512_80k_loveda.py | _base_ = './deeplabv3plus_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(
c1_in_channels=64,
c1_channels=12,
in_channels=512,
channels=12... | 385 | 26.571429 | 72 | py |
mmsegmentation | mmsegmentation-master/configs/deeplabv3plus/deeplabv3plus_r18-d8_512x512_80k_potsdam.py | _base_ = './deeplabv3plus_r50-d8_512x512_80k_potsdam.py'
model = dict(
pretrained='open-mmlab://resnet18_v1c',
backbone=dict(depth=18),
decode_head=dict(
c1_in_channels=64,
c1_channels=12,
in_channels=512,
channels=128,
),
auxiliary_head=dict(in_channels=256, channels... | 326 | 26.25 | 56 | py |
mmsegmentation | mmsegmentation-master/configs/deeplabv3plus/deeplabv3plus_r18-d8_769x769_80k_cityscapes.py | _base_ = './deeplabv3plus_r50-d8_769x769_80k_cityscapes.py'
model = dict(
pretrained='open-mmlab://resnet18_v1c',
backbone=dict(depth=18),
decode_head=dict(
c1_in_channels=64,
c1_channels=12,
in_channels=512,
channels=128,
),
auxiliary_head=dict(in_channels=256, chann... | 329 | 26.5 | 59 | py |
mmsegmentation | mmsegmentation-master/configs/deeplabv3plus/deeplabv3plus_r18b-d8_512x1024_80k_cityscapes.py | _base_ = './deeplabv3plus_r50-d8_512x1024_80k_cityscapes.py'
model = dict(
pretrained='torchvision://resnet18',
backbone=dict(type='ResNet', depth=18),
decode_head=dict(
c1_in_channels=64,
c1_channels=12,
in_channels=512,
channels=128,
),
auxiliary_head=dict(in_channe... | 342 | 27.583333 | 60 | py |
mmsegmentation | mmsegmentation-master/configs/deeplabv3plus/deeplabv3plus_r18b-d8_769x769_80k_cityscapes.py | _base_ = './deeplabv3plus_r50-d8_769x769_80k_cityscapes.py'
model = dict(
pretrained='torchvision://resnet18',
backbone=dict(type='ResNet', depth=18),
decode_head=dict(
c1_in_channels=64,
c1_channels=12,
in_channels=512,
channels=128,
),
auxiliary_head=dict(in_channel... | 341 | 27.5 | 59 | py |
mmsegmentation | mmsegmentation-master/configs/deeplabv3plus/deeplabv3plus_r50-d8_480x480_40k_pascal_context.py | _base_ = [
'../_base_/models/deeplabv3plus_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, ... | 420 | 37.272727 | 75 | py |
mmsegmentation | mmsegmentation-master/configs/deeplabv3plus/deeplabv3plus_r50-d8_480x480_40k_pascal_context_59.py | _base_ = [
'../_base_/models/deeplabv3plus_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=(48... | 423 | 37.545455 | 78 | py |
mmsegmentation | mmsegmentation-master/configs/deeplabv3plus/deeplabv3plus_r50-d8_480x480_80k_pascal_context.py | _base_ = [
'../_base_/models/deeplabv3plus_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, ... | 420 | 37.272727 | 75 | py |
mmsegmentation | mmsegmentation-master/configs/deeplabv3plus/deeplabv3plus_r50-d8_480x480_80k_pascal_context_59.py | _base_ = [
'../_base_/models/deeplabv3plus_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=(48... | 423 | 37.545455 | 78 | py |
mmsegmentation | mmsegmentation-master/configs/deeplabv3plus/deeplabv3plus_r50-d8_4x4_512x512_80k_vaihingen.py | _base_ = [
'../_base_/models/deeplabv3plus_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))
| 261 | 31.75 | 72 | py |
mmsegmentation | mmsegmentation-master/configs/deeplabv3plus/deeplabv3plus_r50-d8_4x4_896x896_80k_isaid.py | _base_ = [
'../_base_/models/deeplabv3plus_r50-d8.py', '../_base_/datasets/isaid.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
]
model = dict(
decode_head=dict(num_classes=16), auxiliary_head=dict(num_classes=16))
| 255 | 35.571429 | 78 | py |
mmsegmentation | mmsegmentation-master/configs/deeplabv3plus/deeplabv3plus_r50-d8_512x1024_40k_cityscapes.py | _base_ = [
'../_base_/models/deeplabv3plus_r50-d8.py',
'../_base_/datasets/cityscapes.py', '../_base_/default_runtime.py',
'../_base_/schedules/schedule_40k.py'
]
| 175 | 28.333333 | 71 | py |
mmsegmentation | mmsegmentation-master/configs/deeplabv3plus/deeplabv3plus_r50-d8_512x1024_80k_cityscapes.py | _base_ = [
'../_base_/models/deeplabv3plus_r50-d8.py',
'../_base_/datasets/cityscapes.py', '../_base_/default_runtime.py',
'../_base_/schedules/schedule_80k.py'
]
| 175 | 28.333333 | 71 | py |
mmsegmentation | mmsegmentation-master/configs/deeplabv3plus/deeplabv3plus_r50-d8_512x512_160k_ade20k.py | _base_ = [
'../_base_/models/deeplabv3plus_r50-d8.py', '../_base_/datasets/ade20k.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py'
]
model = dict(
decode_head=dict(num_classes=150), auxiliary_head=dict(num_classes=150))
| 259 | 36.142857 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/deeplabv3plus/deeplabv3plus_r50-d8_512x512_20k_voc12aug.py | _base_ = [
'../_base_/models/deeplabv3plus_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))
| 270 | 32.875 | 77 | py |
mmsegmentation | mmsegmentation-master/configs/deeplabv3plus/deeplabv3plus_r50-d8_512x512_40k_voc12aug.py | _base_ = [
'../_base_/models/deeplabv3plus_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))
| 270 | 32.875 | 77 | py |
mmsegmentation | mmsegmentation-master/configs/deeplabv3plus/deeplabv3plus_r50-d8_512x512_80k_ade20k.py | _base_ = [
'../_base_/models/deeplabv3plus_r50-d8.py', '../_base_/datasets/ade20k.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
]
model = dict(
decode_head=dict(num_classes=150), auxiliary_head=dict(num_classes=150))
| 258 | 36 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/deeplabv3plus/deeplabv3plus_r50-d8_512x512_80k_loveda.py | _base_ = [
'../_base_/models/deeplabv3plus_r50-d8.py', '../_base_/datasets/loveda.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
]
model = dict(
decode_head=dict(num_classes=7), auxiliary_head=dict(num_classes=7))
| 254 | 35.428571 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/deeplabv3plus/deeplabv3plus_r50-d8_512x512_80k_potsdam.py | _base_ = [
'../_base_/models/deeplabv3plus_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))
| 259 | 31.5 | 72 | py |
mmsegmentation | mmsegmentation-master/configs/deeplabv3plus/deeplabv3plus_r50-d8_769x769_40k_cityscapes.py | _base_ = [
'../_base_/models/deeplabv3plus_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... | 358 | 34.9 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/deeplabv3plus/deeplabv3plus_r50-d8_769x769_80k_cityscapes.py | _base_ = [
'../_base_/models/deeplabv3plus_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... | 358 | 34.9 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/deeplabv3plus/deeplabv3plus_r50b-d8_512x1024_80k_cityscapes.py | _base_ = './deeplabv3plus_r50-d8_512x1024_80k_cityscapes.py'
model = dict(pretrained='torchvision://resnet50', backbone=dict(type='ResNet'))
| 141 | 46.333333 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/deeplabv3plus/deeplabv3plus_r50b-d8_769x769_80k_cityscapes.py | _base_ = './deeplabv3plus_r50-d8_769x769_80k_cityscapes.py'
model = dict(pretrained='torchvision://resnet50', backbone=dict(type='ResNet'))
| 140 | 46 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/dmnet/README.md | # DMNet
[Dynamic Multi-scale Filters for Semantic Segmentation](https://openaccess.thecvf.com/content_ICCV_2019/papers/He_Dynamic_Multi-Scale_Filters_for_Semantic_Segmentation_ICCV_2019_paper.pdf)
## Introduction
<!-- [ALGORITHM] -->
<a href="https://github.com/Junjun2016/DMNet">Official Repo</a>
<a href="https://... | 11,130 | 184.516667 | 1,277 | md |
mmsegmentation | mmsegmentation-master/configs/dmnet/dmnet.yml | Collections:
- Name: DMNet
Metadata:
Training Data:
- Cityscapes
- ADE20K
Paper:
URL: https://openaccess.thecvf.com/content_ICCV_2019/papers/He_Dynamic_Multi-Scale_Filters_for_Semantic_Segmentation_ICCV_2019_paper.pdf
Title: Dynamic Multi-scale Filters for Semantic Segmentation
README: configs... | 7,673 | 31.935622 | 172 | yml |
mmsegmentation | mmsegmentation-master/configs/dmnet/dmnet_r101-d8_512x1024_40k_cityscapes.py | _base_ = './dmnet_r50-d8_512x1024_40k_cityscapes.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
| 133 | 43.666667 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/dmnet/dmnet_r101-d8_512x1024_80k_cityscapes.py | _base_ = './dmnet_r50-d8_512x1024_80k_cityscapes.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
| 133 | 43.666667 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/dmnet/dmnet_r101-d8_512x512_160k_ade20k.py | _base_ = './dmnet_r50-d8_512x512_160k_ade20k.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
| 129 | 42.333333 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/dmnet/dmnet_r101-d8_512x512_80k_ade20k.py | _base_ = './dmnet_r50-d8_512x512_80k_ade20k.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
| 128 | 42 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/dmnet/dmnet_r101-d8_769x769_40k_cityscapes.py | _base_ = './dmnet_r50-d8_769x769_40k_cityscapes.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
| 132 | 43.333333 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/dmnet/dmnet_r101-d8_769x769_80k_cityscapes.py | _base_ = './dmnet_r50-d8_769x769_80k_cityscapes.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
| 132 | 43.333333 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/dmnet/dmnet_r50-d8_512x1024_40k_cityscapes.py | _base_ = [
'../_base_/models/dmnet_r50-d8.py', '../_base_/datasets/cityscapes.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py'
]
| 163 | 31.8 | 75 | py |
mmsegmentation | mmsegmentation-master/configs/dmnet/dmnet_r50-d8_512x1024_80k_cityscapes.py | _base_ = [
'../_base_/models/dmnet_r50-d8.py', '../_base_/datasets/cityscapes.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
]
| 163 | 31.8 | 75 | py |
mmsegmentation | mmsegmentation-master/configs/dmnet/dmnet_r50-d8_512x512_160k_ade20k.py | _base_ = [
'../_base_/models/dmnet_r50-d8.py', '../_base_/datasets/ade20k.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py'
]
model = dict(
decode_head=dict(num_classes=150), auxiliary_head=dict(num_classes=150))
| 251 | 35 | 76 | py |
mmsegmentation | mmsegmentation-master/configs/dmnet/dmnet_r50-d8_512x512_80k_ade20k.py | _base_ = [
'../_base_/models/dmnet_r50-d8.py', '../_base_/datasets/ade20k.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
]
model = dict(
decode_head=dict(num_classes=150), auxiliary_head=dict(num_classes=150))
| 250 | 34.857143 | 76 | py |
mmsegmentation | mmsegmentation-master/configs/dmnet/dmnet_r50-d8_769x769_40k_cityscapes.py | _base_ = [
'../_base_/models/dmnet_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=(7... | 350 | 34.1 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/dmnet/dmnet_r50-d8_769x769_80k_cityscapes.py | _base_ = [
'../_base_/models/dmnet_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=(7... | 350 | 34.1 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/dnlnet/README.md | # DNLNet
[Disentangled Non-Local Neural Networks](https://arxiv.org/abs/2006.06668)
## Introduction
<!-- [ALGORITHM] -->
<a href="https://github.com/yinmh17/DNL-Semantic-Segmentation">Official Repo</a>
<a href="https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/dnl_head.py#L88">Cod... | 10,831 | 170.936508 | 1,050 | md |
mmsegmentation | mmsegmentation-master/configs/dnlnet/dnl_r101-d8_512x1024_40k_cityscapes.py | _base_ = './dnl_r50-d8_512x1024_40k_cityscapes.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
| 131 | 43 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/dnlnet/dnl_r101-d8_512x1024_80k_cityscapes.py | _base_ = './dnl_r50-d8_512x1024_80k_cityscapes.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
| 131 | 43 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/dnlnet/dnl_r101-d8_512x512_160k_ade20k.py | _base_ = './dnl_r50-d8_512x512_160k_ade20k.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
| 127 | 41.666667 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/dnlnet/dnl_r101-d8_512x512_80k_ade20k.py | _base_ = './dnl_r50-d8_512x512_80k_ade20k.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
| 126 | 41.333333 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/dnlnet/dnl_r101-d8_769x769_40k_cityscapes.py | _base_ = './dnl_r50-d8_769x769_40k_cityscapes.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
| 130 | 42.666667 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/dnlnet/dnl_r101-d8_769x769_80k_cityscapes.py | _base_ = './dnl_r50-d8_769x769_80k_cityscapes.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
| 130 | 42.666667 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/dnlnet/dnl_r50-d8_512x1024_40k_cityscapes.py | _base_ = [
'../_base_/models/dnl_r50-d8.py', '../_base_/datasets/cityscapes.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py'
]
| 161 | 31.4 | 73 | py |
mmsegmentation | mmsegmentation-master/configs/dnlnet/dnl_r50-d8_512x1024_80k_cityscapes.py | _base_ = [
'../_base_/models/dnl_r50-d8.py', '../_base_/datasets/cityscapes.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
]
| 161 | 31.4 | 73 | py |
mmsegmentation | mmsegmentation-master/configs/dnlnet/dnl_r50-d8_512x512_160k_ade20k.py | _base_ = [
'../_base_/models/dnl_r50-d8.py', '../_base_/datasets/ade20k.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py'
]
model = dict(
decode_head=dict(num_classes=150), auxiliary_head=dict(num_classes=150))
| 249 | 34.714286 | 76 | py |
mmsegmentation | mmsegmentation-master/configs/dnlnet/dnl_r50-d8_512x512_80k_ade20k.py | _base_ = [
'../_base_/models/dnl_r50-d8.py', '../_base_/datasets/ade20k.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
]
model = dict(
decode_head=dict(num_classes=150), auxiliary_head=dict(num_classes=150))
| 248 | 34.571429 | 76 | py |
mmsegmentation | mmsegmentation-master/configs/dnlnet/dnl_r50-d8_769x769_40k_cityscapes.py | _base_ = [
'../_base_/models/dnl_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... | 348 | 33.9 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/dnlnet/dnl_r50-d8_769x769_80k_cityscapes.py | _base_ = [
'../_base_/models/dnl_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... | 463 | 34.692308 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/dnlnet/dnlnet.yml | Collections:
- Name: DNLNet
Metadata:
Training Data:
- Cityscapes
- ADE20K
Paper:
URL: https://arxiv.org/abs/2006.06668
Title: Disentangled Non-Local Neural Networks
README: configs/dnlnet/README.md
Code:
URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_... | 7,410 | 31.362445 | 169 | yml |
mmsegmentation | mmsegmentation-master/configs/dpt/README.md | # DPT
[Vision Transformer for Dense Prediction](https://arxiv.org/abs/2103.13413)
## Introduction
<!-- [ALGORITHM] -->
<a href="https://github.com/isl-org/DPT">Official Repo</a>
<a href="https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/dpt_head.py#L215">Code Snippet</a>
## Abstr... | 4,889 | 70.911765 | 1,375 | md |
mmsegmentation | mmsegmentation-master/configs/dpt/dpt.yml | Collections:
- Name: DPT
Metadata:
Training Data:
- ADE20K
Paper:
URL: https://arxiv.org/abs/2103.13413
Title: Vision Transformer for Dense Prediction
README: configs/dpt/README.md
Code:
URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/dpt_head.py#L215... | 1,055 | 26.789474 | 142 | yml |
mmsegmentation | mmsegmentation-master/configs/dpt/dpt_vit-b16_512x512_160k_ade20k.py | _base_ = [
'../_base_/models/dpt_vit-b16.py', '../_base_/datasets/ade20k.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py'
]
# AdamW optimizer, no weight decay for position embedding & layer norm
# in backbone
optimizer = dict(
_delete_=True,
type='AdamW',
lr=0.00006,
... | 844 | 24.606061 | 74 | py |
mmsegmentation | mmsegmentation-master/configs/emanet/README.md | # EMANet
[Expectation-Maximization Attention Networks for Semantic Segmentation](https://arxiv.org/abs/1907.13426)
## Introduction
<!-- [ALGORITHM] -->
<a href="https://xialipku.github.io/EMANet">Official Repo</a>
<a href="https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/ema_head... | 5,464 | 115.276596 | 1,088 | md |
mmsegmentation | mmsegmentation-master/configs/emanet/emanet.yml | Collections:
- Name: EMANet
Metadata:
Training Data:
- Cityscapes
Paper:
URL: https://arxiv.org/abs/1907.13426
Title: Expectation-Maximization Attention Networks for Semantic Segmentation
README: configs/emanet/README.md
Code:
URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mm... | 3,256 | 30.317308 | 175 | yml |
mmsegmentation | mmsegmentation-master/configs/emanet/emanet_r101-d8_512x1024_80k_cityscapes.py | _base_ = './emanet_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/emanet/emanet_r101-d8_769x769_80k_cityscapes.py | _base_ = './emanet_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/emanet/emanet_r50-d8_512x1024_80k_cityscapes.py | _base_ = [
'../_base_/models/emanet_r50-d8.py', '../_base_/datasets/cityscapes.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
]
| 164 | 32 | 76 | py |
mmsegmentation | mmsegmentation-master/configs/emanet/emanet_r50-d8_769x769_80k_cityscapes.py | _base_ = [
'../_base_/models/emanet_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=(... | 351 | 34.2 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/encnet/README.md | # EncNet
[Context Encoding for Semantic Segmentation](https://arxiv.org/abs/1803.08904)
## Introduction
<!-- [ALGORITHM] -->
<a href="https://github.com/zhanghang1989/PyTorch-Encoding">Official Repo</a>
<a href="https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/enc_head.py#L63">Co... | 11,211 | 185.866667 | 1,264 | md |
mmsegmentation | mmsegmentation-master/configs/encnet/encnet.yml | Collections:
- Name: EncNet
Metadata:
Training Data:
- Cityscapes
- ADE20K
Paper:
URL: https://arxiv.org/abs/1803.08904
Title: Context Encoding for Semantic Segmentation
README: configs/encnet/README.md
Code:
URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/dec... | 7,665 | 31.901288 | 175 | yml |
mmsegmentation | mmsegmentation-master/configs/encnet/encnet_r101-d8_512x1024_40k_cityscapes.py | _base_ = './encnet_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/encnet/encnet_r101-d8_512x1024_80k_cityscapes.py | _base_ = './encnet_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/encnet/encnet_r101-d8_512x512_160k_ade20k.py | _base_ = './encnet_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/encnet/encnet_r101-d8_512x512_20k_voc12aug.py | _base_ = './encnet_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/encnet/encnet_r101-d8_512x512_40k_voc12aug.py | _base_ = './encnet_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/encnet/encnet_r101-d8_512x512_80k_ade20k.py | _base_ = './encnet_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/encnet/encnet_r101-d8_769x769_40k_cityscapes.py | _base_ = './encnet_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/encnet/encnet_r101-d8_769x769_80k_cityscapes.py | _base_ = './encnet_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/encnet/encnet_r50-d8_512x1024_40k_cityscapes.py | _base_ = [
'../_base_/models/encnet_r50-d8.py', '../_base_/datasets/cityscapes.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py'
]
| 164 | 32 | 76 | py |
mmsegmentation | mmsegmentation-master/configs/encnet/encnet_r50-d8_512x1024_80k_cityscapes.py | _base_ = [
'../_base_/models/encnet_r50-d8.py', '../_base_/datasets/cityscapes.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
]
| 164 | 32 | 76 | py |
mmsegmentation | mmsegmentation-master/configs/encnet/encnet_r50-d8_512x512_160k_ade20k.py | _base_ = [
'../_base_/models/encnet_r50-d8.py', '../_base_/datasets/ade20k.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py'
]
model = dict(
decode_head=dict(num_classes=150), auxiliary_head=dict(num_classes=150))
| 252 | 35.142857 | 76 | py |
mmsegmentation | mmsegmentation-master/configs/encnet/encnet_r50-d8_512x512_20k_voc12aug.py | _base_ = [
'../_base_/models/encnet_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/encnet/encnet_r50-d8_512x512_40k_voc12aug.py | _base_ = [
'../_base_/models/encnet_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/encnet/encnet_r50-d8_512x512_80k_ade20k.py | _base_ = [
'../_base_/models/encnet_r50-d8.py', '../_base_/datasets/ade20k.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
]
model = dict(
decode_head=dict(num_classes=150), auxiliary_head=dict(num_classes=150))
| 251 | 35 | 76 | py |
mmsegmentation | mmsegmentation-master/configs/encnet/encnet_r50-d8_769x769_40k_cityscapes.py | _base_ = [
'../_base_/models/encnet_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=(... | 351 | 34.2 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/encnet/encnet_r50-d8_769x769_80k_cityscapes.py | _base_ = [
'../_base_/models/encnet_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=(... | 351 | 34.2 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/encnet/encnet_r50s-d8_512x512_80k_ade20k.py | _base_ = [
'../_base_/models/encnet_r50-d8.py', '../_base_/datasets/ade20k.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
]
model = dict(
backbone=dict(stem_channels=128),
decode_head=dict(num_classes=150),
auxiliary_head=dict(num_classes=150))
| 293 | 31.666667 | 73 | py |
mmsegmentation | mmsegmentation-master/configs/erfnet/README.md | # ERFNet
[ERFNet: Efficient Residual Factorized ConvNet for Real-time Semantic Segmentation](http://www.robesafe.uah.es/personal/eduardo.romera/pdfs/Romera17tits.pdf)
## Introduction
<!-- [ALGORITHM] -->
<a href="https://github.com/Eromera/erfnet_pytorch">Official Repo</a>
<a href="https://github.com/open-mmlab/mm... | 4,602 | 82.690909 | 1,338 | md |
mmsegmentation | mmsegmentation-master/configs/erfnet/erfnet.yml | Collections:
- Name: ERFNet
Metadata:
Training Data:
- Cityscapes
Paper:
URL: http://www.robesafe.uah.es/personal/eduardo.romera/pdfs/Romera17tits.pdf
Title: 'ERFNet: Efficient Residual Factorized ConvNet for Real-time Semantic Segmentation'
README: configs/erfnet/README.md
Code:
URL: https:... | 1,218 | 31.078947 | 177 | yml |
mmsegmentation | mmsegmentation-master/configs/erfnet/erfnet_fcn_4x4_512x1024_160k_cityscapes.py | _base_ = [
'../_base_/models/erfnet_fcn.py', '../_base_/datasets/cityscapes.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py'
]
data = dict(
samples_per_gpu=4,
workers_per_gpu=4,
)
| 223 | 23.888889 | 74 | py |
mmsegmentation | mmsegmentation-master/configs/fastfcn/README.md | # FastFCN
[FastFCN: Rethinking Dilated Convolution in the Backbone for Semantic Segmentation](https://arxiv.org/abs/1903.11816)
## Introduction
<!-- [ALGORITHM] -->
<a href="https://github.com/wuhuikai/FastFCN">Official Repo</a>
<a href="https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/necks/... | 12,561 | 195.28125 | 972 | md |
mmsegmentation | mmsegmentation-master/configs/fastfcn/fastfcn.yml | Collections:
- Name: FastFCN
Metadata:
Training Data:
- Cityscapes
- ADE20K
Paper:
URL: https://arxiv.org/abs/1903.11816
Title: 'FastFCN: Rethinking Dilated Convolution in the Backbone for Semantic Segmentation'
README: configs/fastfcn/README.md
Code:
URL: https://github.com/open-mmlab/m... | 8,314 | 34.233051 | 204 | yml |
mmsegmentation | mmsegmentation-master/configs/fastfcn/fastfcn_r50-d32_jpu_aspp_4x4_512x1024_80k_cityscapes.py | # model settings
_base_ = './fastfcn_r50-d32_jpu_aspp_512x1024_80k_cityscapes.py'
data = dict(
samples_per_gpu=4,
workers_per_gpu=4,
)
| 143 | 19.571429 | 64 | py |
mmsegmentation | mmsegmentation-master/configs/fastfcn/fastfcn_r50-d32_jpu_aspp_512x1024_80k_cityscapes.py | # model settings
_base_ = './fastfcn_r50-d32_jpu_psp_512x1024_80k_cityscapes.py'
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
decode_head=dict(
_delete_=True,
type='ASPPHead',
in_channels=2048,
in_index=2,
channels=512,
dilations=(1, 12, 24, 36),
... | 624 | 28.761905 | 74 | py |
mmsegmentation | mmsegmentation-master/configs/fastfcn/fastfcn_r50-d32_jpu_aspp_512x512_160k_ade20k.py | # model settings
_base_ = './fastfcn_r50-d32_jpu_psp_512x512_160k_ade20k.py'
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
decode_head=dict(
_delete_=True,
type='ASPPHead',
in_channels=2048,
in_index=2,
channels=512,
dilations=(1, 12, 24, 36),
... | 621 | 28.619048 | 74 | py |
mmsegmentation | mmsegmentation-master/configs/fastfcn/fastfcn_r50-d32_jpu_aspp_512x512_80k_ade20k.py | # model settings
_base_ = './fastfcn_r50-d32_jpu_psp_512x512_80k_ade20k.py'
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
decode_head=dict(
_delete_=True,
type='ASPPHead',
in_channels=2048,
in_index=2,
channels=512,
dilations=(1, 12, 24, 36),
... | 620 | 28.571429 | 74 | py |
mmsegmentation | mmsegmentation-master/configs/fastfcn/fastfcn_r50-d32_jpu_enc_4x4_512x1024_80k_cityscapes.py | # model settings
_base_ = './fastfcn_r50-d32_jpu_enc_512x1024_80k_cityscapes.py'
data = dict(
samples_per_gpu=4,
workers_per_gpu=4,
)
| 142 | 19.428571 | 63 | py |
mmsegmentation | mmsegmentation-master/configs/fastfcn/fastfcn_r50-d32_jpu_enc_512x1024_80k_cityscapes.py | # model settings
_base_ = './fastfcn_r50-d32_jpu_psp_512x1024_80k_cityscapes.py'
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
decode_head=dict(
_delete_=True,
type='EncHead',
in_channels=[512, 1024, 2048],
in_index=(0, 1, 2),
channels=512,
num_code... | 786 | 30.48 | 73 | py |
mmsegmentation | mmsegmentation-master/configs/fastfcn/fastfcn_r50-d32_jpu_enc_512x512_160k_ade20k.py | # model settings
_base_ = './fastfcn_r50-d32_jpu_psp_512x512_160k_ade20k.py'
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
decode_head=dict(
_delete_=True,
type='EncHead',
in_channels=[512, 1024, 2048],
in_index=(0, 1, 2),
channels=512,
num_codes=32... | 783 | 30.36 | 73 | py |
mmsegmentation | mmsegmentation-master/configs/fastfcn/fastfcn_r50-d32_jpu_enc_512x512_80k_ade20k.py | # model settings
_base_ = './fastfcn_r50-d32_jpu_psp_512x512_80k_ade20k.py'
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
decode_head=dict(
_delete_=True,
type='EncHead',
in_channels=[512, 1024, 2048],
in_index=(0, 1, 2),
channels=512,
num_codes=32,... | 782 | 30.32 | 73 | py |
mmsegmentation | mmsegmentation-master/configs/fastfcn/fastfcn_r50-d32_jpu_psp_4x4_512x1024_80k_cityscapes.py | _base_ = [
'../_base_/models/fastfcn_r50-d32_jpu_psp.py',
'../_base_/datasets/cityscapes.py', '../_base_/default_runtime.py',
'../_base_/schedules/schedule_80k.py'
]
data = dict(
samples_per_gpu=4,
workers_per_gpu=4,
)
| 239 | 23 | 71 | py |
mmsegmentation | mmsegmentation-master/configs/fastfcn/fastfcn_r50-d32_jpu_psp_512x1024_80k_cityscapes.py | _base_ = [
'../_base_/models/fastfcn_r50-d32_jpu_psp.py',
'../_base_/datasets/cityscapes.py', '../_base_/default_runtime.py',
'../_base_/schedules/schedule_80k.py'
]
| 178 | 28.833333 | 71 | py |
mmsegmentation | mmsegmentation-master/configs/fastfcn/fastfcn_r50-d32_jpu_psp_512x512_160k_ade20k.py | _base_ = [
'../_base_/models/fastfcn_r50-d32_jpu_psp.py',
'../_base_/datasets/ade20k.py', '../_base_/default_runtime.py',
'../_base_/schedules/schedule_160k.py'
]
model = dict(
decode_head=dict(num_classes=150), auxiliary_head=dict(num_classes=150))
| 266 | 32.375 | 76 | py |
mmsegmentation | mmsegmentation-master/configs/fastfcn/fastfcn_r50-d32_jpu_psp_512x512_80k_ade20k.py | _base_ = [
'../_base_/models/fastfcn_r50-d32_jpu_psp.py',
'../_base_/datasets/ade20k.py', '../_base_/default_runtime.py',
'../_base_/schedules/schedule_80k.py'
]
model = dict(
decode_head=dict(num_classes=150), auxiliary_head=dict(num_classes=150))
| 265 | 32.25 | 76 | py |
mmsegmentation | mmsegmentation-master/configs/fastscnn/README.md | # Fast-SCNN
[Fast-SCNN for Semantic Segmentation](https://arxiv.org/abs/1902.04502)
## Introduction
<!-- [ALGORITHM] -->
<a href="">Official Repo</a>
<a href="https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/fast_scnn.py#L272">Code Snippet</a>
## Abstract
<!-- [ABSTRACT] -->
The ... | 3,606 | 82.883721 | 1,128 | md |
mmsegmentation | mmsegmentation-master/configs/fastscnn/fast_scnn_lr0.12_8x4_160k_cityscapes.py | _base_ = [
'../_base_/models/fast_scnn.py', '../_base_/datasets/cityscapes.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py'
]
# Re-config the data sampler.
data = dict(samples_per_gpu=4, workers_per_gpu=4)
# Re-config the optimizer.
optimizer = dict(type='SGD', lr=0.12, momentum=0.9... | 341 | 30.090909 | 74 | py |