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/_base_/models/ocrnet_r50-d8.py | # model settings
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='CascadeEncoderDecoder',
num_stages=2,
pretrained='open-mmlab://resnet50_v1c',
backbone=dict(
type='ResNetV1c',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
dilations=(1, 1... | 1,385 | 27.875 | 78 | py |
mmsegmentation | mmsegmentation-master/configs/_base_/models/pointrend_r50.py | # model settings
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='CascadeEncoderDecoder',
num_stages=2,
pretrained='open-mmlab://resnet50_v1c',
backbone=dict(
type='ResNetV1c',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
dilations=(1, 1... | 1,704 | 28.912281 | 78 | py |
mmsegmentation | mmsegmentation-master/configs/_base_/models/psanet_r50-d8.py | # model settings
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='EncoderDecoder',
pretrained='open-mmlab://resnet50_v1c',
backbone=dict(
type='ResNetV1c',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
dilations=(1, 1, 2, 4),
strides=... | 1,406 | 27.14 | 74 | py |
mmsegmentation | mmsegmentation-master/configs/_base_/models/pspnet_r50-d8.py | # model settings
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='EncoderDecoder',
pretrained='open-mmlab://resnet50_v1c',
backbone=dict(
type='ResNetV1c',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
dilations=(1, 1, 2, 4),
strides=... | 1,271 | 27.266667 | 74 | py |
mmsegmentation | mmsegmentation-master/configs/_base_/models/pspnet_unet_s5-d16.py | # model settings
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='EncoderDecoder',
pretrained=None,
backbone=dict(
type='UNet',
in_channels=3,
base_channels=64,
num_stages=5,
strides=(1, 1, 1, 1, 1),
enc_num_convs=(2, 2, 2, 2, 2),
... | 1,511 | 28.647059 | 74 | py |
mmsegmentation | mmsegmentation-master/configs/_base_/models/segformer_mit-b0.py | # model settings
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='EncoderDecoder',
pretrained=None,
backbone=dict(
type='MixVisionTransformer',
in_channels=3,
embed_dims=32,
num_stages=4,
num_layers=[2, 2, 2, 2],
num_heads=[1, 2, 5, 8],
... | 993 | 27.4 | 74 | py |
mmsegmentation | mmsegmentation-master/configs/_base_/models/segmenter_vit-b16_mask.py | checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segmenter/vit_base_p16_384_20220308-96dfe169.pth' # noqa
# model settings
backbone_norm_cfg = dict(type='LN', eps=1e-6, requires_grad=True)
model = dict(
type='EncoderDecoder',
pretrained=checkpoint,
backbone=dict(
type='Visi... | 1,109 | 29 | 131 | py |
mmsegmentation | mmsegmentation-master/configs/_base_/models/setr_mla.py | # model settings
backbone_norm_cfg = dict(type='LN', eps=1e-6, requires_grad=True)
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='EncoderDecoder',
pretrained='pretrain/jx_vit_large_p16_384-b3be5167.pth',
backbone=dict(
type='VisionTransformer',
img_size=(768, 768),
... | 2,860 | 28.802083 | 78 | py |
mmsegmentation | mmsegmentation-master/configs/_base_/models/setr_naive.py | # model settings
backbone_norm_cfg = dict(type='LN', eps=1e-6, requires_grad=True)
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='EncoderDecoder',
pretrained='pretrain/jx_vit_large_p16_384-b3be5167.pth',
backbone=dict(
type='VisionTransformer',
img_size=(768, 768),
... | 2,365 | 28.209877 | 78 | py |
mmsegmentation | mmsegmentation-master/configs/_base_/models/setr_pup.py | # model settings
backbone_norm_cfg = dict(type='LN', eps=1e-6, requires_grad=True)
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='EncoderDecoder',
pretrained='pretrain/jx_vit_large_p16_384-b3be5167.pth',
backbone=dict(
type='VisionTransformer',
img_size=(768, 768),
... | 2,366 | 28.222222 | 78 | py |
mmsegmentation | mmsegmentation-master/configs/_base_/models/stdc.py | norm_cfg = dict(type='BN', requires_grad=True)
model = dict(
type='EncoderDecoder',
pretrained=None,
backbone=dict(
type='STDCContextPathNet',
backbone_cfg=dict(
type='STDCNet',
stdc_type='STDCNet1',
in_channels=3,
channels=(32, 64, 256, 512, 1... | 2,721 | 31.404762 | 78 | py |
mmsegmentation | mmsegmentation-master/configs/_base_/models/twins_pcpvt-s_fpn.py | checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/twins/pcpvt_small_20220308-e638c41c.pth' # noqa
# model settings
backbone_norm_cfg = dict(type='LN')
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='EncoderDecoder',
backbone=dict(
type='PCPVT',
in... | 1,442 | 30.369565 | 122 | py |
mmsegmentation | mmsegmentation-master/configs/_base_/models/twins_pcpvt-s_upernet.py | checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/twins/pcpvt_small_20220308-e638c41c.pth' # noqa
# model settings
backbone_norm_cfg = dict(type='LN')
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='EncoderDecoder',
backbone=dict(
type='PCPVT',
in... | 1,687 | 30.259259 | 122 | py |
mmsegmentation | mmsegmentation-master/configs/_base_/models/upernet_beit.py | norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='EncoderDecoder',
pretrained=None,
backbone=dict(
type='BEiT',
img_size=(640, 640),
patch_size=16,
in_channels=3,
embed_dims=768,
num_layers=12,
num_heads=12,
mlp_ratio=4,
... | 1,496 | 28.352941 | 78 | py |
mmsegmentation | mmsegmentation-master/configs/_base_/models/upernet_convnext.py | norm_cfg = dict(type='SyncBN', requires_grad=True)
custom_imports = dict(imports='mmcls.models', allow_failed_imports=False)
checkpoint_file = 'https://download.openmmlab.com/mmclassification/v0/convnext/downstream/convnext-base_3rdparty_32xb128-noema_in1k_20220301-2a0ee547.pth' # noqa
model = dict(
type='EncoderD... | 1,527 | 32.955556 | 162 | py |
mmsegmentation | mmsegmentation-master/configs/_base_/models/upernet_mae.py | norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='EncoderDecoder',
pretrained=None,
backbone=dict(
type='MAE',
img_size=(640, 640),
patch_size=16,
in_channels=3,
embed_dims=768,
num_layers=12,
num_heads=12,
mlp_ratio=4,
... | 1,471 | 28.44 | 78 | py |
mmsegmentation | mmsegmentation-master/configs/_base_/models/upernet_r50.py | # model settings
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='EncoderDecoder',
pretrained='open-mmlab://resnet50_v1c',
backbone=dict(
type='ResNetV1c',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
dilations=(1, 1, 1, 1),
strides=... | 1,301 | 27.933333 | 74 | py |
mmsegmentation | mmsegmentation-master/configs/_base_/models/upernet_swin.py | # model settings
norm_cfg = dict(type='SyncBN', requires_grad=True)
backbone_norm_cfg = dict(type='LN', requires_grad=True)
model = dict(
type='EncoderDecoder',
pretrained=None,
backbone=dict(
type='SwinTransformer',
pretrain_img_size=224,
embed_dims=96,
patch_size=4,
... | 1,590 | 27.927273 | 74 | py |
mmsegmentation | mmsegmentation-master/configs/_base_/models/upernet_vit-b16_ln_mln.py | # model settings
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='EncoderDecoder',
pretrained='pretrain/jx_vit_base_p16_224-80ecf9dd.pth',
backbone=dict(
type='VisionTransformer',
img_size=(512, 512),
patch_size=16,
in_channels=3,
embed_dims=768,... | 1,711 | 28.517241 | 74 | py |
mmsegmentation | mmsegmentation-master/configs/_base_/schedules/schedule_160k.py | # optimizer
optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0005)
optimizer_config = dict()
# learning policy
lr_config = dict(policy='poly', power=0.9, min_lr=1e-4, by_epoch=False)
# runtime settings
runner = dict(type='IterBasedRunner', max_iters=160000)
checkpoint_config = dict(by_epoch=False, int... | 397 | 38.8 | 72 | py |
mmsegmentation | mmsegmentation-master/configs/_base_/schedules/schedule_20k.py | # optimizer
optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0005)
optimizer_config = dict()
# learning policy
lr_config = dict(policy='poly', power=0.9, min_lr=1e-4, by_epoch=False)
# runtime settings
runner = dict(type='IterBasedRunner', max_iters=20000)
checkpoint_config = dict(by_epoch=False, inte... | 394 | 38.5 | 72 | py |
mmsegmentation | mmsegmentation-master/configs/_base_/schedules/schedule_320k.py | # optimizer
optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0005)
optimizer_config = dict()
# learning policy
lr_config = dict(policy='poly', power=0.9, min_lr=1e-4, by_epoch=False)
# runtime settings
runner = dict(type='IterBasedRunner', max_iters=320000)
checkpoint_config = dict(by_epoch=False, int... | 382 | 37.3 | 72 | py |
mmsegmentation | mmsegmentation-master/configs/_base_/schedules/schedule_40k.py | # optimizer
optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0005)
optimizer_config = dict()
# learning policy
lr_config = dict(policy='poly', power=0.9, min_lr=1e-4, by_epoch=False)
# runtime settings
runner = dict(type='IterBasedRunner', max_iters=40000)
checkpoint_config = dict(by_epoch=False, inte... | 394 | 38.5 | 72 | py |
mmsegmentation | mmsegmentation-master/configs/_base_/schedules/schedule_80k.py | # optimizer
optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0005)
optimizer_config = dict()
# learning policy
lr_config = dict(policy='poly', power=0.9, min_lr=1e-4, by_epoch=False)
# runtime settings
runner = dict(type='IterBasedRunner', max_iters=80000)
checkpoint_config = dict(by_epoch=False, inte... | 394 | 38.5 | 72 | py |
mmsegmentation | mmsegmentation-master/configs/ann/README.md | # ANN
[Asymmetric Non-local Neural Networks for Semantic Segmentation](https://arxiv.org/abs/1908.07678)
## Introduction
<!-- [ALGORITHM] -->
<a href="https://github.com/MendelXu/ANN">Official Repo</a>
<a href="https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/ann_head.py#L185">Co... | 13,885 | 200.246377 | 1,136 | md |
mmsegmentation | mmsegmentation-master/configs/ann/ann.yml | Collections:
- Name: ANN
Metadata:
Training Data:
- Cityscapes
- ADE20K
- Pascal VOC 2012 + Aug
Paper:
URL: https://arxiv.org/abs/1908.07678
Title: Asymmetric Non-local Neural Networks for Semantic Segmentation
README: configs/ann/README.md
Code:
URL: https://github.com/open-mmlab/mm... | 9,777 | 30.954248 | 166 | yml |
mmsegmentation | mmsegmentation-master/configs/ann/ann_r101-d8_512x1024_40k_cityscapes.py | _base_ = './ann_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/ann/ann_r101-d8_512x1024_80k_cityscapes.py | _base_ = './ann_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/ann/ann_r101-d8_512x512_160k_ade20k.py | _base_ = './ann_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/ann/ann_r101-d8_512x512_20k_voc12aug.py | _base_ = './ann_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/ann/ann_r101-d8_512x512_40k_voc12aug.py | _base_ = './ann_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/ann/ann_r101-d8_512x512_80k_ade20k.py | _base_ = './ann_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/ann/ann_r101-d8_769x769_40k_cityscapes.py | _base_ = './ann_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/ann/ann_r101-d8_769x769_80k_cityscapes.py | _base_ = './ann_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/ann/ann_r50-d8_512x1024_40k_cityscapes.py | _base_ = [
'../_base_/models/ann_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/ann/ann_r50-d8_512x1024_80k_cityscapes.py | _base_ = [
'../_base_/models/ann_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/ann/ann_r50-d8_512x512_160k_ade20k.py | _base_ = [
'../_base_/models/ann_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/ann/ann_r50-d8_512x512_20k_voc12aug.py | _base_ = [
'../_base_/models/ann_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/ann/ann_r50-d8_512x512_40k_voc12aug.py | _base_ = [
'../_base_/models/ann_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/ann/ann_r50-d8_512x512_80k_ade20k.py | _base_ = [
'../_base_/models/ann_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/ann/ann_r50-d8_769x769_40k_cityscapes.py | _base_ = [
'../_base_/models/ann_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/ann/ann_r50-d8_769x769_80k_cityscapes.py | _base_ = [
'../_base_/models/ann_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/apcnet/README.md | # APCNet
[Adaptive Pyramid Context Network for Semantic Segmentation](https://openaccess.thecvf.com/content_CVPR_2019/html/He_Adaptive_Pyramid_Context_Network_for_Semantic_Segmentation_CVPR_2019_paper.html)
## Introduction
<!-- [ALGORITHM] -->
<a href="https://github.com/Junjun2016/APCNet">Official Repo</a>
<a hre... | 11,402 | 189.05 | 1,367 | md |
mmsegmentation | mmsegmentation-master/configs/apcnet/apcnet.yml | Collections:
- Name: APCNet
Metadata:
Training Data:
- Cityscapes
- ADE20K
Paper:
URL: https://openaccess.thecvf.com/content_CVPR_2019/html/He_Adaptive_Pyramid_Context_Network_for_Semantic_Segmentation_CVPR_2019_paper.html
Title: Adaptive Pyramid Context Network for Semantic Segmentation
READM... | 7,771 | 32.356223 | 175 | yml |
mmsegmentation | mmsegmentation-master/configs/apcnet/apcnet_r101-d8_512x1024_40k_cityscapes.py | _base_ = './apcnet_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/apcnet/apcnet_r101-d8_512x1024_80k_cityscapes.py | _base_ = './apcnet_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/apcnet/apcnet_r101-d8_512x512_160k_ade20k.py | _base_ = './apcnet_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/apcnet/apcnet_r101-d8_512x512_80k_ade20k.py | _base_ = './apcnet_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/apcnet/apcnet_r101-d8_769x769_40k_cityscapes.py | _base_ = './apcnet_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/apcnet/apcnet_r101-d8_769x769_80k_cityscapes.py | _base_ = './apcnet_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/apcnet/apcnet_r50-d8_512x1024_40k_cityscapes.py | _base_ = [
'../_base_/models/apcnet_r50-d8.py', '../_base_/datasets/cityscapes.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py'
]
| 164 | 32 | 76 | py |
mmsegmentation | mmsegmentation-master/configs/apcnet/apcnet_r50-d8_512x1024_80k_cityscapes.py | _base_ = [
'../_base_/models/apcnet_r50-d8.py', '../_base_/datasets/cityscapes.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
]
| 164 | 32 | 76 | py |
mmsegmentation | mmsegmentation-master/configs/apcnet/apcnet_r50-d8_512x512_160k_ade20k.py | _base_ = [
'../_base_/models/apcnet_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/apcnet/apcnet_r50-d8_512x512_80k_ade20k.py | _base_ = [
'../_base_/models/apcnet_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/apcnet/apcnet_r50-d8_769x769_40k_cityscapes.py | _base_ = [
'../_base_/models/apcnet_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/apcnet/apcnet_r50-d8_769x769_80k_cityscapes.py | _base_ = [
'../_base_/models/apcnet_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/beit/README.md | # BEiT
[BEiT: BERT Pre-Training of Image Transformers](https://arxiv.org/abs/2106.08254)
## Introduction
<!-- [BACKBONE] -->
<a href="https://github.com/microsoft/unilm/tree/master/beit">Official Repo</a>
<a href="https://github.com/open-mmlab/mmsegmentation/blob/v0.23.0/mmseg/models/backbones/beit.py#1404">Code S... | 6,944 | 79.755814 | 1,396 | md |
mmsegmentation | mmsegmentation-master/configs/beit/beit.yml | Models:
- Name: upernet_beit-base_8x2_640x640_160k_ade20k
In Collection: UPerNet
Metadata:
backbone: BEiT-B
crop size: (640,640)
lr schd: 160000
inference time (ms/im):
- value: 500.0
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (640,640)
... | 1,422 | 29.934783 | 175 | yml |
mmsegmentation | mmsegmentation-master/configs/beit/upernet_beit-base_640x640_160k_ade20k_ms.py | _base_ = './upernet_beit-base_8x2_640x640_160k_ade20k.py'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(2560, 640),
img_ratios=[0.5, 0.75, 1... | 763 | 29.56 | 77 | py |
mmsegmentation | mmsegmentation-master/configs/beit/upernet_beit-base_8x2_640x640_160k_ade20k.py | _base_ = [
'../_base_/models/upernet_beit.py', '../_base_/datasets/ade20k_640x640.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py'
]
model = dict(
pretrained='pretrain/beit_base_patch16_224_pt22k_ft22k.pth',
test_cfg=dict(mode='slide', crop_size=(640, 640), stride=(426, 426))... | 821 | 25.516129 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/beit/upernet_beit-large_fp16_640x640_160k_ade20k_ms.py | _base_ = './upernet_beit-large_fp16_8x1_640x640_160k_ade20k.py'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(2560, 640),
img_ratios=[0.5, 0... | 742 | 31.304348 | 77 | py |
mmsegmentation | mmsegmentation-master/configs/beit/upernet_beit-large_fp16_8x1_640x640_160k_ade20k.py | _base_ = [
'../_base_/models/upernet_beit.py', '../_base_/datasets/ade20k_640x640.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_320k.py'
]
model = dict(
pretrained='pretrain/beit_large_patch16_224_pt22k_ft22k.pth',
backbone=dict(
type='BEiT',
embed_dims=1024,
... | 1,328 | 26.6875 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/bisenetv1/README.md | # BiSeNetV1
[BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation](https://arxiv.org/abs/1808.00897)
## Introduction
<!-- [ALGORITHM] -->
<a href="https://github.com/ycszen/TorchSeg/tree/master/model/bisenet">Official Repo</a>
<a href="https://github.com/open-mmlab/mmsegmentation/blob/v0.18.... | 12,951 | 198.261538 | 1,028 | md |
mmsegmentation | mmsegmentation-master/configs/bisenetv1/bisenetv1.yml | Collections:
- Name: BiSeNetV1
Metadata:
Training Data:
- Cityscapes
- COCO-Stuff 164k
Paper:
URL: https://arxiv.org/abs/1808.00897
Title: 'BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation'
README: configs/bisenetv1/README.md
Code:
URL: https://github.com/open-... | 8,708 | 36.059574 | 234 | yml |
mmsegmentation | mmsegmentation-master/configs/bisenetv1/bisenetv1_r101-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k.py | _base_ = './bisenetv1_r101-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k.py'
model = dict(
backbone=dict(
backbone_cfg=dict(
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://resnet101_v1c'))))
| 240 | 33.428571 | 78 | py |
mmsegmentation | mmsegmentation-master/configs/bisenetv1/bisenetv1_r101-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k.py | _base_ = [
'../_base_/models/bisenetv1_r18-d32.py',
'../_base_/datasets/coco-stuff164k.py', '../_base_/default_runtime.py',
'../_base_/schedules/schedule_160k.py'
]
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
backbone=dict(
context_channels=(512, 1024, 2048),
spatial... | 1,373 | 31.714286 | 78 | py |
mmsegmentation | mmsegmentation-master/configs/bisenetv1/bisenetv1_r18-d32_4x4_1024x1024_160k_cityscapes.py | _base_ = [
'../_base_/models/bisenetv1_r18-d32.py',
'../_base_/datasets/cityscapes_1024x1024.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py'
]
lr_config = dict(warmup='linear', warmup_iters=1000)
optimizer = dict(lr=0.025)
data = dict(
samples_per_gpu=4,
workers_per_gpu=4... | 324 | 26.083333 | 74 | py |
mmsegmentation | mmsegmentation-master/configs/bisenetv1/bisenetv1_r18-d32_in1k-pre_4x4_1024x1024_160k_cityscapes.py | _base_ = [
'../_base_/models/bisenetv1_r18-d32.py',
'../_base_/datasets/cityscapes_1024x1024.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py'
]
model = dict(
backbone=dict(
backbone_cfg=dict(
init_cfg=dict(
type='Pretrained', checkpoint='ope... | 489 | 27.823529 | 77 | py |
mmsegmentation | mmsegmentation-master/configs/bisenetv1/bisenetv1_r18-d32_in1k-pre_4x8_1024x1024_160k_cityscapes.py | _base_ = './bisenetv1_r18-d32_in1k-pre_4x4_1024x1024_160k_cityscapes.py'
data = dict(
samples_per_gpu=8,
workers_per_gpu=4,
)
| 134 | 21.5 | 72 | py |
mmsegmentation | mmsegmentation-master/configs/bisenetv1/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k.py | _base_ = './bisenetv1_r18-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k.py'
model = dict(
backbone=dict(
backbone_cfg=dict(
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://resnet18_v1c'))), )
| 240 | 33.428571 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/bisenetv1/bisenetv1_r18-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k.py | _base_ = [
'../_base_/models/bisenetv1_r18-d32.py',
'../_base_/datasets/coco-stuff164k.py', '../_base_/default_runtime.py',
'../_base_/schedules/schedule_160k.py'
]
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
decode_head=dict(num_classes=171),
auxiliary_head=[
dict(
... | 1,147 | 30.027027 | 78 | py |
mmsegmentation | mmsegmentation-master/configs/bisenetv1/bisenetv1_r50-d32_4x4_1024x1024_160k_cityscapes.py | _base_ = [
'../_base_/models/bisenetv1_r18-d32.py',
'../_base_/datasets/cityscapes_1024x1024.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py'
]
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='EncoderDecoder',
backbone=dict(
type='BiSeNetV1',
... | 1,227 | 27.55814 | 74 | py |
mmsegmentation | mmsegmentation-master/configs/bisenetv1/bisenetv1_r50-d32_in1k-pre_4x4_1024x1024_160k_cityscapes.py | _base_ = './bisenetv1_r50-d32_4x4_1024x1024_160k_cityscapes.py'
model = dict(
type='EncoderDecoder',
backbone=dict(
backbone_cfg=dict(
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://resnet50_v1c'))))
| 256 | 31.125 | 77 | py |
mmsegmentation | mmsegmentation-master/configs/bisenetv1/bisenetv1_r50-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k.py | _base_ = './bisenetv1_r50-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k.py'
model = dict(
backbone=dict(
backbone_cfg=dict(
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://resnet50_v1c'))))
| 239 | 29 | 77 | py |
mmsegmentation | mmsegmentation-master/configs/bisenetv1/bisenetv1_r50-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k.py | _base_ = [
'../_base_/models/bisenetv1_r18-d32.py',
'../_base_/datasets/coco-stuff164k.py', '../_base_/default_runtime.py',
'../_base_/schedules/schedule_160k.py'
]
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
backbone=dict(
context_channels=(512, 1024, 2048),
spatial... | 1,372 | 31.690476 | 78 | py |
mmsegmentation | mmsegmentation-master/configs/bisenetv2/README.md | # BiSeNetV2
[Bisenet v2: Bilateral Network with Guided Aggregation for Real-time Semantic Segmentation](https://arxiv.org/abs/2004.02147)
## Introduction
<!-- [ALGORITHM] -->
<a href="">Official Repo</a>
<a href="https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/backbones/bisenetv2.py#L545">Co... | 6,639 | 121.962963 | 1,651 | md |
mmsegmentation | mmsegmentation-master/configs/bisenetv2/bisenetv2.yml | Collections:
- Name: BiSeNetV2
Metadata:
Training Data:
- Cityscapes
Paper:
URL: https://arxiv.org/abs/2004.02147
Title: 'Bisenet v2: Bilateral Network with Guided Aggregation for Real-time Semantic
Segmentation'
README: configs/bisenetv2/README.md
Code:
URL: https://github.com/open-mm... | 3,106 | 33.910112 | 198 | yml |
mmsegmentation | mmsegmentation-master/configs/bisenetv2/bisenetv2_fcn_4x4_1024x1024_160k_cityscapes.py | _base_ = [
'../_base_/models/bisenetv2.py',
'../_base_/datasets/cityscapes_1024x1024.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py'
]
lr_config = dict(warmup='linear', warmup_iters=1000)
optimizer = dict(lr=0.05)
data = dict(
samples_per_gpu=4,
workers_per_gpu=4,
)
| 315 | 25.333333 | 74 | py |
mmsegmentation | mmsegmentation-master/configs/bisenetv2/bisenetv2_fcn_4x8_1024x1024_160k_cityscapes.py | _base_ = [
'../_base_/models/bisenetv2.py',
'../_base_/datasets/cityscapes_1024x1024.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py'
]
lr_config = dict(warmup='linear', warmup_iters=1000)
optimizer = dict(lr=0.05)
data = dict(
samples_per_gpu=8,
workers_per_gpu=4,
)
| 315 | 25.333333 | 74 | py |
mmsegmentation | mmsegmentation-master/configs/bisenetv2/bisenetv2_fcn_fp16_4x4_1024x1024_160k_cityscapes.py | _base_ = './bisenetv2_fcn_4x4_1024x1024_160k_cityscapes.py'
# fp16 settings
optimizer_config = dict(type='Fp16OptimizerHook', loss_scale=512.)
# fp16 placeholder
fp16 = dict()
| 176 | 28.5 | 66 | py |
mmsegmentation | mmsegmentation-master/configs/bisenetv2/bisenetv2_fcn_ohem_4x4_1024x1024_160k_cityscapes.py | _base_ = [
'../_base_/models/bisenetv2.py',
'../_base_/datasets/cityscapes_1024x1024.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py'
]
# sampler = dict(type='OHEMPixelSampler', thresh=0.7, min_kept=10000)
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
decode... | 2,407 | 31.986301 | 78 | py |
mmsegmentation | mmsegmentation-master/configs/ccnet/README.md | # CCNet
[CCNet: Criss-Cross Attention for Semantic Segmentation](https://arxiv.org/abs/1811.11721)
## Introduction
<!-- [ALGORITHM] -->
<a href="https://github.com/speedinghzl/CCNet">Official Repo</a>
<a href="https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/apc_head.py#L111">Cod... | 14,527 | 212.647059 | 1,497 | md |
mmsegmentation | mmsegmentation-master/configs/ccnet/ccnet.yml | Collections:
- Name: CCNet
Metadata:
Training Data:
- Cityscapes
- ADE20K
- Pascal VOC 2012 + Aug
Paper:
URL: https://arxiv.org/abs/1811.11721
Title: 'CCNet: Criss-Cross Attention for Semantic Segmentation'
README: configs/ccnet/README.md
Code:
URL: https://github.com/open-mmlab/mmse... | 10,004 | 31.696078 | 172 | yml |
mmsegmentation | mmsegmentation-master/configs/ccnet/ccnet_r101-d8_512x1024_40k_cityscapes.py | _base_ = './ccnet_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/ccnet/ccnet_r101-d8_512x1024_80k_cityscapes.py | _base_ = './ccnet_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/ccnet/ccnet_r101-d8_512x512_160k_ade20k.py | _base_ = './ccnet_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/ccnet/ccnet_r101-d8_512x512_20k_voc12aug.py | _base_ = './ccnet_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/ccnet/ccnet_r101-d8_512x512_40k_voc12aug.py | _base_ = './ccnet_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/ccnet/ccnet_r101-d8_512x512_80k_ade20k.py | _base_ = './ccnet_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/ccnet/ccnet_r101-d8_769x769_40k_cityscapes.py | _base_ = './ccnet_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/ccnet/ccnet_r101-d8_769x769_80k_cityscapes.py | _base_ = './ccnet_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/ccnet/ccnet_r50-d8_512x1024_40k_cityscapes.py | _base_ = [
'../_base_/models/ccnet_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/ccnet/ccnet_r50-d8_512x1024_80k_cityscapes.py | _base_ = [
'../_base_/models/ccnet_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/ccnet/ccnet_r50-d8_512x512_160k_ade20k.py | _base_ = [
'../_base_/models/ccnet_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/ccnet/ccnet_r50-d8_512x512_20k_voc12aug.py | _base_ = [
'../_base_/models/ccnet_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/ccnet/ccnet_r50-d8_512x512_40k_voc12aug.py | _base_ = [
'../_base_/models/ccnet_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/ccnet/ccnet_r50-d8_512x512_80k_ade20k.py | _base_ = [
'../_base_/models/ccnet_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/ccnet/ccnet_r50-d8_769x769_40k_cityscapes.py | _base_ = [
'../_base_/models/ccnet_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/ccnet/ccnet_r50-d8_769x769_80k_cityscapes.py | _base_ = [
'../_base_/models/ccnet_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/cgnet/README.md | # CGNet
[CGNet: A Light-weight Context Guided Network for Semantic Segmentation](https://arxiv.org/abs/1811.08201)
## Introduction
<!-- [ALGORITHM] -->
<a href="https://github.com/wutianyiRosun/CGNet">Official Repo</a>
<a href="https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/cgnet.... | 4,480 | 94.340426 | 1,457 | md |