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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
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mmsegmentation | mmsegmentation-master/configs/fastscnn/fastscnn.yml | Collections:
- Name: FastSCNN
Metadata:
Training Data:
- Cityscapes
Paper:
URL: https://arxiv.org/abs/1902.04502
Title: Fast-SCNN for Semantic Segmentation
README: configs/fastscnn/README.md
Code:
URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/fast_scnn... | 1,064 | 28.583333 | 174 | yml |
mmsegmentation | mmsegmentation-master/configs/fcn/README.md | # FCN
[Fully Convolutional Networks for Semantic Segmentation](https://arxiv.org/abs/1411.4038)
## Introduction
<!-- [ALGORITHM] -->
<a href="https://github.com/BVLC/caffe/wiki/Model-Zoo#fcn">Official Repo</a>
<a href="https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fcn_head.py#... | 31,116 | 276.830357 | 1,194 | md |
mmsegmentation | mmsegmentation-master/configs/fcn/fcn.yml | Collections:
- Name: FCN
Metadata:
Training Data:
- Cityscapes
- ADE20K
- Pascal VOC 2012 + Aug
- Pascal Context
- Pascal Context 59
Paper:
URL: https://arxiv.org/abs/1411.4038
Title: Fully Convolutional Networks for Semantic Segmentation
README: configs/fcn/README.md
Code:
U... | 26,473 | 30.97343 | 178 | yml |
mmsegmentation | mmsegmentation-master/configs/fcn/fcn_d6_r101-d16_512x1024_40k_cityscapes.py | _base_ = './fcn_d6_r50-d16_512x1024_40k_cityscapes.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
| 135 | 44.333333 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/fcn/fcn_d6_r101-d16_512x1024_80k_cityscapes.py | _base_ = './fcn_d6_r50-d16_512x1024_80k_cityscapes.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
| 135 | 44.333333 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/fcn/fcn_d6_r101-d16_769x769_40k_cityscapes.py | _base_ = './fcn_d6_r50-d16_769x769_40k_cityscapes.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
| 134 | 44 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/fcn/fcn_d6_r101-d16_769x769_80k_cityscapes.py | _base_ = './fcn_d6_r50-d16_769x769_80k_cityscapes.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
| 134 | 44 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/fcn/fcn_d6_r101b-d16_512x1024_80k_cityscapes.py | _base_ = './fcn_d6_r50b-d16_512x1024_80k_cityscapes.py'
model = dict(
pretrained='torchvision://resnet101',
backbone=dict(type='ResNet', depth=101))
| 157 | 30.6 | 55 | py |
mmsegmentation | mmsegmentation-master/configs/fcn/fcn_d6_r101b-d16_769x769_80k_cityscapes.py | _base_ = './fcn_d6_r50b-d16_769x769_80k_cityscapes.py'
model = dict(
pretrained='torchvision://resnet101',
backbone=dict(type='ResNet', depth=101))
| 156 | 30.4 | 54 | py |
mmsegmentation | mmsegmentation-master/configs/fcn/fcn_d6_r50-d16_512x1024_40k_cityscapes.py | _base_ = [
'../_base_/models/fcn_r50-d8.py', '../_base_/datasets/cityscapes.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py'
]
model = dict(
backbone=dict(dilations=(1, 1, 1, 2), strides=(1, 2, 2, 1)),
decode_head=dict(dilation=6),
auxiliary_head=dict(dilation=6))
| 311 | 33.666667 | 73 | py |
mmsegmentation | mmsegmentation-master/configs/fcn/fcn_d6_r50-d16_512x1024_80k_cityscapes.py | _base_ = [
'../_base_/models/fcn_r50-d8.py', '../_base_/datasets/cityscapes.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
]
model = dict(
backbone=dict(dilations=(1, 1, 1, 2), strides=(1, 2, 2, 1)),
decode_head=dict(dilation=6),
auxiliary_head=dict(dilation=6))
| 311 | 33.666667 | 73 | py |
mmsegmentation | mmsegmentation-master/configs/fcn/fcn_d6_r50-d16_769x769_40k_cityscapes.py | _base_ = [
'../_base_/models/fcn_r50-d8.py',
'../_base_/datasets/cityscapes_769x769.py', '../_base_/default_runtime.py',
'../_base_/schedules/schedule_40k.py'
]
model = dict(
backbone=dict(dilations=(1, 1, 1, 2), strides=(1, 2, 2, 1)),
decode_head=dict(align_corners=True, dilation=6),
auxiliary_... | 437 | 38.818182 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/fcn/fcn_d6_r50-d16_769x769_80k_cityscapes.py | _base_ = [
'../_base_/models/fcn_r50-d8.py',
'../_base_/datasets/cityscapes_769x769.py', '../_base_/default_runtime.py',
'../_base_/schedules/schedule_80k.py'
]
model = dict(
backbone=dict(dilations=(1, 1, 1, 2), strides=(1, 2, 2, 1)),
decode_head=dict(align_corners=True, dilation=6),
auxiliary_... | 437 | 38.818182 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/fcn/fcn_d6_r50b-d16_512x1024_80k_cityscapes.py | _base_ = './fcn_d6_r50-d16_512x1024_80k_cityscapes.py'
model = dict(pretrained='torchvision://resnet50', backbone=dict(type='ResNet'))
| 135 | 44.333333 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/fcn/fcn_d6_r50b-d16_769x769_80k_cityscapes.py | _base_ = './fcn_d6_r50-d16_769x769_80k_cityscapes.py'
model = dict(pretrained='torchvision://resnet50', backbone=dict(type='ResNet'))
| 134 | 44 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/fcn/fcn_r101-d8_480x480_40k_pascal_context.py | _base_ = './fcn_r50-d8_480x480_40k_pascal_context.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
| 134 | 44 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/fcn/fcn_r101-d8_480x480_40k_pascal_context_59.py | _base_ = './fcn_r50-d8_480x480_40k_pascal_context_59.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
| 137 | 45 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/fcn/fcn_r101-d8_480x480_80k_pascal_context.py | _base_ = './fcn_r50-d8_480x480_80k_pascal_context.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
| 134 | 44 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/fcn/fcn_r101-d8_480x480_80k_pascal_context_59.py | _base_ = './fcn_r50-d8_480x480_80k_pascal_context_59.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
| 137 | 45 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/fcn/fcn_r101-d8_512x1024_40k_cityscapes.py | _base_ = './fcn_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/fcn/fcn_r101-d8_512x1024_80k_cityscapes.py | _base_ = './fcn_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/fcn/fcn_r101-d8_512x512_160k_ade20k.py | _base_ = './fcn_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/fcn/fcn_r101-d8_512x512_20k_voc12aug.py | _base_ = './fcn_r50-d8_512x512_20k_voc12aug.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
| 128 | 42 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/fcn/fcn_r101-d8_512x512_40k_voc12aug.py | _base_ = './fcn_r50-d8_512x512_40k_voc12aug.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
| 128 | 42 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/fcn/fcn_r101-d8_512x512_80k_ade20k.py | _base_ = './fcn_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/fcn/fcn_r101-d8_769x769_40k_cityscapes.py | _base_ = './fcn_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/fcn/fcn_r101-d8_769x769_80k_cityscapes.py | _base_ = './fcn_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/fcn/fcn_r101-d8_fp16_512x1024_80k_cityscapes.py | _base_ = './fcn_r101-d8_512x1024_80k_cityscapes.py'
# fp16 settings
optimizer_config = dict(type='Fp16OptimizerHook', loss_scale=512.)
# fp16 placeholder
fp16 = dict()
| 168 | 27.166667 | 66 | py |
mmsegmentation | mmsegmentation-master/configs/fcn/fcn_r101b-d8_512x1024_80k_cityscapes.py | _base_ = './fcn_r50-d8_512x1024_80k_cityscapes.py'
model = dict(
pretrained='torchvision://resnet101',
backbone=dict(type='ResNet', depth=101))
| 152 | 29.6 | 50 | py |
mmsegmentation | mmsegmentation-master/configs/fcn/fcn_r101b-d8_769x769_80k_cityscapes.py | _base_ = './fcn_r50-d8_769x769_80k_cityscapes.py'
model = dict(
pretrained='torchvision://resnet101',
backbone=dict(type='ResNet', depth=101))
| 151 | 29.4 | 49 | py |
mmsegmentation | mmsegmentation-master/configs/fcn/fcn_r18-d8_512x1024_80k_cityscapes.py | _base_ = './fcn_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))
| 269 | 26 | 54 | py |
mmsegmentation | mmsegmentation-master/configs/fcn/fcn_r18-d8_769x769_80k_cityscapes.py | _base_ = './fcn_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))
| 268 | 25.9 | 54 | py |
mmsegmentation | mmsegmentation-master/configs/fcn/fcn_r18b-d8_512x1024_80k_cityscapes.py | _base_ = './fcn_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))
| 281 | 27.2 | 54 | py |
mmsegmentation | mmsegmentation-master/configs/fcn/fcn_r18b-d8_769x769_80k_cityscapes.py | _base_ = './fcn_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))
| 280 | 27.1 | 54 | py |
mmsegmentation | mmsegmentation-master/configs/fcn/fcn_r50-d8_480x480_40k_pascal_context.py | _base_ = [
'../_base_/models/fcn_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=(... | 406 | 39.7 | 77 | py |
mmsegmentation | mmsegmentation-master/configs/fcn/fcn_r50-d8_480x480_40k_pascal_context_59.py | _base_ = [
'../_base_/models/fcn_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), s... | 413 | 36.636364 | 78 | py |
mmsegmentation | mmsegmentation-master/configs/fcn/fcn_r50-d8_480x480_80k_pascal_context.py | _base_ = [
'../_base_/models/fcn_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=(... | 406 | 39.7 | 77 | py |
mmsegmentation | mmsegmentation-master/configs/fcn/fcn_r50-d8_480x480_80k_pascal_context_59.py | _base_ = [
'../_base_/models/fcn_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), s... | 413 | 36.636364 | 78 | py |
mmsegmentation | mmsegmentation-master/configs/fcn/fcn_r50-d8_512x1024_40k_cityscapes.py | _base_ = [
'../_base_/models/fcn_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/fcn/fcn_r50-d8_512x1024_80k_cityscapes.py | _base_ = [
'../_base_/models/fcn_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/fcn/fcn_r50-d8_512x512_160k_ade20k.py | _base_ = [
'../_base_/models/fcn_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/fcn/fcn_r50-d8_512x512_20k_voc12aug.py | _base_ = [
'../_base_/models/fcn_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))
| 256 | 35.714286 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/fcn/fcn_r50-d8_512x512_40k_voc12aug.py | _base_ = [
'../_base_/models/fcn_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))
| 256 | 35.714286 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/fcn/fcn_r50-d8_512x512_80k_ade20k.py | _base_ = [
'../_base_/models/fcn_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/fcn/fcn_r50-d8_769x769_40k_cityscapes.py | _base_ = [
'../_base_/models/fcn_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/fcn/fcn_r50-d8_769x769_80k_cityscapes.py | _base_ = [
'../_base_/models/fcn_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... | 348 | 33.9 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/fcn/fcn_r50b-d8_512x1024_80k_cityscapes.py | _base_ = './fcn_r50-d8_512x1024_80k_cityscapes.py'
model = dict(pretrained='torchvision://resnet50', backbone=dict(type='ResNet'))
| 131 | 43 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/fcn/fcn_r50b-d8_769x769_80k_cityscapes.py | _base_ = './fcn_r50-d8_769x769_80k_cityscapes.py'
model = dict(pretrained='torchvision://resnet50', backbone=dict(type='ResNet'))
| 130 | 42.666667 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/gcnet/README.md | # GCNet
[GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond](https://arxiv.org/abs/1904.11492)
## Introduction
<!-- [ALGORITHM] -->
<a href="https://github.com/xvjiarui/GCNet">Official Repo</a>
<a href="https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/gc_head.p... | 14,374 | 207.333333 | 1,264 | md |
mmsegmentation | mmsegmentation-master/configs/gcnet/gcnet.yml | Collections:
- Name: GCNet
Metadata:
Training Data:
- Cityscapes
- ADE20K
- Pascal VOC 2012 + Aug
Paper:
URL: https://arxiv.org/abs/1904.11492
Title: 'GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond'
README: configs/gcnet/README.md
Code:
URL: https://github.com/... | 10,020 | 31.748366 | 172 | yml |
mmsegmentation | mmsegmentation-master/configs/gcnet/gcnet_r101-d8_512x1024_40k_cityscapes.py | _base_ = './gcnet_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/gcnet/gcnet_r101-d8_512x1024_80k_cityscapes.py | _base_ = './gcnet_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/gcnet/gcnet_r101-d8_512x512_160k_ade20k.py | _base_ = './gcnet_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/gcnet/gcnet_r101-d8_512x512_20k_voc12aug.py | _base_ = './gcnet_r50-d8_512x512_20k_voc12aug.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
| 130 | 42.666667 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/gcnet/gcnet_r101-d8_512x512_40k_voc12aug.py | _base_ = './gcnet_r50-d8_512x512_40k_voc12aug.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
| 130 | 42.666667 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/gcnet/gcnet_r101-d8_512x512_80k_ade20k.py | _base_ = './gcnet_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/gcnet/gcnet_r101-d8_769x769_40k_cityscapes.py | _base_ = './gcnet_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/gcnet/gcnet_r101-d8_769x769_80k_cityscapes.py | _base_ = './gcnet_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/gcnet/gcnet_r50-d8_512x1024_40k_cityscapes.py | _base_ = [
'../_base_/models/gcnet_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/gcnet/gcnet_r50-d8_512x1024_80k_cityscapes.py | _base_ = [
'../_base_/models/gcnet_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/gcnet/gcnet_r50-d8_512x512_160k_ade20k.py | _base_ = [
'../_base_/models/gcnet_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/gcnet/gcnet_r50-d8_512x512_20k_voc12aug.py | _base_ = [
'../_base_/models/gcnet_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))
| 262 | 31.875 | 77 | py |
mmsegmentation | mmsegmentation-master/configs/gcnet/gcnet_r50-d8_512x512_40k_voc12aug.py | _base_ = [
'../_base_/models/gcnet_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))
| 262 | 31.875 | 77 | py |
mmsegmentation | mmsegmentation-master/configs/gcnet/gcnet_r50-d8_512x512_80k_ade20k.py | _base_ = [
'../_base_/models/gcnet_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/gcnet/gcnet_r50-d8_769x769_40k_cityscapes.py | _base_ = [
'../_base_/models/gcnet_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/gcnet/gcnet_r50-d8_769x769_80k_cityscapes.py | _base_ = [
'../_base_/models/gcnet_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/hrnet/README.md | # HRNet
[Deep High-Resolution Representation Learning for Human Pose Estimation](https://arxiv.org/abs/1908.07919)
## Introduction
<!-- [BACKBONE] -->
<a href="https://github.com/HRNet/HRNet-Semantic-Segmentation">Official Repo</a>
<a href="https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/bac... | 32,361 | 262.105691 | 1,219 | md |
mmsegmentation | mmsegmentation-master/configs/hrnet/fcn_hr18_480x480_40k_pascal_context.py | _base_ = [
'../_base_/models/fcn_hr18.py', '../_base_/datasets/pascal_context.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py'
]
model = dict(
decode_head=dict(num_classes=60),
test_cfg=dict(mode='slide', crop_size=(480, 480), stride=(320, 320)))
optimizer = dict(type='SGD', lr... | 363 | 39.444444 | 75 | py |
mmsegmentation | mmsegmentation-master/configs/hrnet/fcn_hr18_480x480_40k_pascal_context_59.py | _base_ = [
'../_base_/models/fcn_hr18.py', '../_base_/datasets/pascal_context_59.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py'
]
model = dict(
decode_head=dict(num_classes=59),
test_cfg=dict(mode='slide', crop_size=(480, 480), stride=(320, 320)))
optimizer = dict(type='SGD',... | 366 | 39.777778 | 78 | py |
mmsegmentation | mmsegmentation-master/configs/hrnet/fcn_hr18_480x480_80k_pascal_context.py | _base_ = [
'../_base_/models/fcn_hr18.py', '../_base_/datasets/pascal_context.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
]
model = dict(
decode_head=dict(num_classes=60),
test_cfg=dict(mode='slide', crop_size=(480, 480), stride=(320, 320)))
optimizer = dict(type='SGD', lr... | 363 | 39.444444 | 75 | py |
mmsegmentation | mmsegmentation-master/configs/hrnet/fcn_hr18_480x480_80k_pascal_context_59.py | _base_ = [
'../_base_/models/fcn_hr18.py', '../_base_/datasets/pascal_context_59.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
]
model = dict(
decode_head=dict(num_classes=59),
test_cfg=dict(mode='slide', crop_size=(480, 480), stride=(320, 320)))
optimizer = dict(type='SGD',... | 366 | 39.777778 | 78 | py |
mmsegmentation | mmsegmentation-master/configs/hrnet/fcn_hr18_4x4_512x512_80k_vaihingen.py | _base_ = [
'../_base_/models/fcn_hr18.py', '../_base_/datasets/vaihingen.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
]
model = dict(decode_head=dict(num_classes=6))
| 204 | 33.166667 | 73 | py |
mmsegmentation | mmsegmentation-master/configs/hrnet/fcn_hr18_4x4_896x896_80k_isaid.py | _base_ = [
'../_base_/models/fcn_hr18.py', '../_base_/datasets/isaid.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
]
model = dict(decode_head=dict(num_classes=16))
| 201 | 32.666667 | 73 | py |
mmsegmentation | mmsegmentation-master/configs/hrnet/fcn_hr18_512x1024_160k_cityscapes.py | _base_ = [
'../_base_/models/fcn_hr18.py', '../_base_/datasets/cityscapes.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py'
]
| 160 | 31.2 | 74 | py |
mmsegmentation | mmsegmentation-master/configs/hrnet/fcn_hr18_512x1024_40k_cityscapes.py | _base_ = [
'../_base_/models/fcn_hr18.py', '../_base_/datasets/cityscapes.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py'
]
| 159 | 31 | 73 | py |
mmsegmentation | mmsegmentation-master/configs/hrnet/fcn_hr18_512x1024_80k_cityscapes.py | _base_ = [
'../_base_/models/fcn_hr18.py', '../_base_/datasets/cityscapes.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
]
| 159 | 31 | 73 | py |
mmsegmentation | mmsegmentation-master/configs/hrnet/fcn_hr18_512x512_160k_ade20k.py | _base_ = [
'../_base_/models/fcn_hr18.py', '../_base_/datasets/ade20k.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py'
]
model = dict(decode_head=dict(num_classes=150))
| 204 | 33.166667 | 74 | py |
mmsegmentation | mmsegmentation-master/configs/hrnet/fcn_hr18_512x512_20k_voc12aug.py | _base_ = [
'../_base_/models/fcn_hr18.py', '../_base_/datasets/pascal_voc12_aug.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_20k.py'
]
model = dict(decode_head=dict(num_classes=21))
| 212 | 34.5 | 77 | py |
mmsegmentation | mmsegmentation-master/configs/hrnet/fcn_hr18_512x512_40k_voc12aug.py | _base_ = [
'../_base_/models/fcn_hr18.py', '../_base_/datasets/pascal_voc12_aug.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py'
]
model = dict(decode_head=dict(num_classes=21))
| 212 | 34.5 | 77 | py |
mmsegmentation | mmsegmentation-master/configs/hrnet/fcn_hr18_512x512_80k_ade20k.py | _base_ = [
'../_base_/models/fcn_hr18.py', '../_base_/datasets/ade20k.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
]
model = dict(decode_head=dict(num_classes=150))
| 203 | 33 | 73 | py |
mmsegmentation | mmsegmentation-master/configs/hrnet/fcn_hr18_512x512_80k_loveda.py | _base_ = [
'../_base_/models/fcn_hr18.py', '../_base_/datasets/loveda.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
]
model = dict(decode_head=dict(num_classes=7))
| 201 | 32.666667 | 73 | py |
mmsegmentation | mmsegmentation-master/configs/hrnet/fcn_hr18_512x512_80k_potsdam.py | _base_ = [
'../_base_/models/fcn_hr18.py', '../_base_/datasets/potsdam.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
]
model = dict(decode_head=dict(num_classes=6))
| 202 | 32.833333 | 73 | py |
mmsegmentation | mmsegmentation-master/configs/hrnet/fcn_hr18s_480x480_40k_pascal_context.py | _base_ = './fcn_hr18_480x480_40k_pascal_context.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)),
... | 375 | 36.6 | 66 | py |
mmsegmentation | mmsegmentation-master/configs/hrnet/fcn_hr18s_480x480_40k_pascal_context_59.py | _base_ = './fcn_hr18_480x480_40k_pascal_context_59.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)),
... | 378 | 36.9 | 66 | py |
mmsegmentation | mmsegmentation-master/configs/hrnet/fcn_hr18s_480x480_80k_pascal_context.py | _base_ = './fcn_hr18_480x480_80k_pascal_context.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)),
... | 375 | 36.6 | 66 | py |
mmsegmentation | mmsegmentation-master/configs/hrnet/fcn_hr18s_480x480_80k_pascal_context_59.py | _base_ = './fcn_hr18_480x480_80k_pascal_context_59.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)),
... | 378 | 36.9 | 66 | py |
mmsegmentation | mmsegmentation-master/configs/hrnet/fcn_hr18s_4x4_512x512_80k_vaihingen.py | _base_ = './fcn_hr18_4x4_512x512_80k_vaihingen.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)),
s... | 374 | 36.5 | 66 | py |
mmsegmentation | mmsegmentation-master/configs/hrnet/fcn_hr18s_4x4_896x896_80k_isaid.py | _base_ = './fcn_hr18_4x4_896x896_80k_isaid.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)),
stage... | 370 | 36.1 | 66 | py |
mmsegmentation | mmsegmentation-master/configs/hrnet/fcn_hr18s_512x1024_160k_cityscapes.py | _base_ = './fcn_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)),
st... | 373 | 36.4 | 66 | py |
mmsegmentation | mmsegmentation-master/configs/hrnet/fcn_hr18s_512x1024_40k_cityscapes.py | _base_ = './fcn_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)),
sta... | 372 | 36.3 | 66 | py |
mmsegmentation | mmsegmentation-master/configs/hrnet/fcn_hr18s_512x1024_80k_cityscapes.py | _base_ = './fcn_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)),
sta... | 372 | 36.3 | 66 | py |
mmsegmentation | mmsegmentation-master/configs/hrnet/fcn_hr18s_512x512_160k_ade20k.py | _base_ = './fcn_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=... | 368 | 35.9 | 66 | py |
mmsegmentation | mmsegmentation-master/configs/hrnet/fcn_hr18s_512x512_20k_voc12aug.py | _base_ = './fcn_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... | 369 | 36 | 66 | py |
mmsegmentation | mmsegmentation-master/configs/hrnet/fcn_hr18s_512x512_40k_voc12aug.py | _base_ = './fcn_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... | 369 | 36 | 66 | py |
mmsegmentation | mmsegmentation-master/configs/hrnet/fcn_hr18s_512x512_80k_ade20k.py | _base_ = './fcn_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=d... | 367 | 35.8 | 66 | py |
mmsegmentation | mmsegmentation-master/configs/hrnet/fcn_hr18s_512x512_80k_loveda.py | _base_ = './fcn_hr18_512x512_80k_loveda.py'
model = dict(
backbone=dict(
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://msra/hrnetv2_w18_small'),
extra=dict(
stage1=dict(num_blocks=(2, )),
stage2=dict(num_blocks=(2, 2)),
stage3=... | 430 | 34.916667 | 66 | py |
mmsegmentation | mmsegmentation-master/configs/hrnet/fcn_hr18s_512x512_80k_potsdam.py | _base_ = './fcn_hr18_512x512_80k_potsdam.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=... | 368 | 35.9 | 66 | py |
mmsegmentation | mmsegmentation-master/configs/hrnet/fcn_hr48_480x480_40k_pascal_context.py | _base_ = './fcn_hr18_480x480_40k_pascal_context.py'
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)))),
de... | 411 | 36.454545 | 74 | py |
mmsegmentation | mmsegmentation-master/configs/hrnet/fcn_hr48_480x480_40k_pascal_context_59.py | _base_ = './fcn_hr18_480x480_40k_pascal_context_59.py'
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)))),
... | 414 | 36.727273 | 74 | py |
mmsegmentation | mmsegmentation-master/configs/hrnet/fcn_hr48_480x480_80k_pascal_context.py | _base_ = './fcn_hr18_480x480_80k_pascal_context.py'
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)))),
de... | 411 | 36.454545 | 74 | py |