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/pspnet/pspnet_r50-d8_4x4_896x896_80k_isaid.py | _base_ = [
'../_base_/models/pspnet_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))
| 248 | 34.571429 | 74 | py |
mmsegmentation | mmsegmentation-master/configs/pspnet/pspnet_r50-d8_512x1024_40k_cityscapes.py | _base_ = [
'../_base_/models/pspnet_r50-d8.py', '../_base_/datasets/cityscapes.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py'
]
| 164 | 32 | 76 | py |
mmsegmentation | mmsegmentation-master/configs/pspnet/pspnet_r50-d8_512x1024_40k_dark.py | _base_ = [
'../_base_/models/pspnet_r50-d8.py', '../_base_/datasets/cityscapes.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.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'),
... | 967 | 31.266667 | 77 | py |
mmsegmentation | mmsegmentation-master/configs/pspnet/pspnet_r50-d8_512x1024_40k_night_driving.py | _base_ = [
'../_base_/models/pspnet_r50-d8.py', '../_base_/datasets/cityscapes.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.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'),
... | 992 | 32.1 | 77 | py |
mmsegmentation | mmsegmentation-master/configs/pspnet/pspnet_r50-d8_512x1024_80k_cityscapes.py | _base_ = [
'../_base_/models/pspnet_r50-d8.py', '../_base_/datasets/cityscapes.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
]
| 164 | 32 | 76 | py |
mmsegmentation | mmsegmentation-master/configs/pspnet/pspnet_r50-d8_512x1024_80k_dark.py | _base_ = [
'../_base_/models/pspnet_r50-d8.py', '../_base_/datasets/cityscapes.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.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'),
... | 968 | 30.258065 | 77 | py |
mmsegmentation | mmsegmentation-master/configs/pspnet/pspnet_r50-d8_512x1024_80k_night_driving.py | _base_ = [
'../_base_/models/pspnet_r50-d8.py', '../_base_/datasets/cityscapes.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.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'),
... | 992 | 32.1 | 77 | py |
mmsegmentation | mmsegmentation-master/configs/pspnet/pspnet_r50-d8_512x512_160k_ade20k.py | _base_ = [
'../_base_/models/pspnet_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/pspnet/pspnet_r50-d8_512x512_20k_voc12aug.py | _base_ = [
'../_base_/models/pspnet_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/pspnet/pspnet_r50-d8_512x512_40k_voc12aug.py | _base_ = [
'../_base_/models/pspnet_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/pspnet/pspnet_r50-d8_512x512_4x4_160k_coco-stuff164k.py | _base_ = [
'../_base_/models/pspnet_r50-d8.py',
'../_base_/datasets/coco-stuff164k.py', '../_base_/default_runtime.py',
'../_base_/schedules/schedule_160k.py'
]
model = dict(
decode_head=dict(num_classes=171), auxiliary_head=dict(num_classes=171))
| 264 | 32.125 | 76 | py |
mmsegmentation | mmsegmentation-master/configs/pspnet/pspnet_r50-d8_512x512_4x4_20k_coco-stuff10k.py | _base_ = [
'../_base_/models/pspnet_r50-d8.py', '../_base_/datasets/coco-stuff10k.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_20k.py'
]
model = dict(
decode_head=dict(num_classes=171), auxiliary_head=dict(num_classes=171))
| 258 | 36 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/pspnet/pspnet_r50-d8_512x512_4x4_320k_coco-stuff164k.py | _base_ = [
'../_base_/models/pspnet_r50-d8.py',
'../_base_/datasets/coco-stuff164k.py', '../_base_/default_runtime.py',
'../_base_/schedules/schedule_320k.py'
]
model = dict(
decode_head=dict(num_classes=171), auxiliary_head=dict(num_classes=171))
| 264 | 32.125 | 76 | py |
mmsegmentation | mmsegmentation-master/configs/pspnet/pspnet_r50-d8_512x512_4x4_40k_coco-stuff10k.py | _base_ = [
'../_base_/models/pspnet_r50-d8.py', '../_base_/datasets/coco-stuff10k.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py'
]
model = dict(
decode_head=dict(num_classes=171), auxiliary_head=dict(num_classes=171))
| 258 | 36 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/pspnet/pspnet_r50-d8_512x512_4x4_80k_coco-stuff164k.py | _base_ = [
'../_base_/models/pspnet_r50-d8.py',
'../_base_/datasets/coco-stuff164k.py', '../_base_/default_runtime.py',
'../_base_/schedules/schedule_80k.py'
]
model = dict(
decode_head=dict(num_classes=171), auxiliary_head=dict(num_classes=171))
| 263 | 32 | 76 | py |
mmsegmentation | mmsegmentation-master/configs/pspnet/pspnet_r50-d8_512x512_80k_ade20k.py | _base_ = [
'../_base_/models/pspnet_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/pspnet/pspnet_r50-d8_512x512_80k_loveda.py | _base_ = [
'../_base_/models/pspnet_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))
| 247 | 34.428571 | 73 | py |
mmsegmentation | mmsegmentation-master/configs/pspnet/pspnet_r50-d8_769x769_40k_cityscapes.py | _base_ = [
'../_base_/models/pspnet_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/pspnet/pspnet_r50-d8_769x769_80k_cityscapes.py | _base_ = [
'../_base_/models/pspnet_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/pspnet/pspnet_r50-d8_rsb-pretrain_512x1024_adamw_80k_cityscapes.py | _base_ = [
'../_base_/models/pspnet_r50-d8.py', '../_base_/datasets/cityscapes.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
]
checkpoint = 'https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb256-rsb-a1-600e_in1k_20211228-20e21305.pth' # noqa
model = dict(
pr... | 805 | 32.583333 | 135 | py |
mmsegmentation | mmsegmentation-master/configs/pspnet/pspnet_r50b-d32_512x1024_80k_cityscapes.py | _base_ = [
'../_base_/models/pspnet_r50-d8.py', '../_base_/datasets/cityscapes.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
]
model = dict(
pretrained='torchvision://resnet50',
backbone=dict(type='ResNet', dilations=(1, 1, 2, 4), strides=(1, 2, 2, 2)))
| 299 | 36.5 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/pspnet/pspnet_r50b-d8_512x1024_80k_cityscapes.py | _base_ = './pspnet_r50-d8_512x1024_80k_cityscapes.py'
model = dict(pretrained='torchvision://resnet50', backbone=dict(type='ResNet'))
| 134 | 44 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/pspnet/pspnet_r50b-d8_769x769_80k_cityscapes.py | _base_ = './pspnet_r50-d8_769x769_80k_cityscapes.py'
model = dict(pretrained='torchvision://resnet50', backbone=dict(type='ResNet'))
| 133 | 43.666667 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/resnest/README.md | # ResNeSt
[ResNeSt: Split-Attention Networks](https://arxiv.org/abs/2004.08955)
## Introduction
<!-- [BACKBONE] -->
<a href="https://github.com/zhanghang1989/ResNeSt">Official Repo</a>
<a href="https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/resnest.py#L271">Code Snippet</a>
## Ab... | 8,976 | 162.218182 | 839 | md |
mmsegmentation | mmsegmentation-master/configs/resnest/deeplabv3_s101-d8_512x1024_80k_cityscapes.py | _base_ = '../deeplabv3/deeplabv3_r101-d8_512x1024_80k_cityscapes.py'
model = dict(
pretrained='open-mmlab://resnest101',
backbone=dict(
type='ResNeSt',
stem_channels=128,
radix=2,
reduction_factor=4,
avg_down_stride=True))
| 271 | 26.2 | 68 | py |
mmsegmentation | mmsegmentation-master/configs/resnest/deeplabv3_s101-d8_512x512_160k_ade20k.py | _base_ = '../deeplabv3/deeplabv3_r101-d8_512x512_160k_ade20k.py'
model = dict(
pretrained='open-mmlab://resnest101',
backbone=dict(
type='ResNeSt',
stem_channels=128,
radix=2,
reduction_factor=4,
avg_down_stride=True))
| 267 | 25.8 | 64 | py |
mmsegmentation | mmsegmentation-master/configs/resnest/deeplabv3plus_s101-d8_512x1024_80k_cityscapes.py | _base_ = '../deeplabv3plus/deeplabv3plus_r101-d8_512x1024_80k_cityscapes.py'
model = dict(
pretrained='open-mmlab://resnest101',
backbone=dict(
type='ResNeSt',
stem_channels=128,
radix=2,
reduction_factor=4,
avg_down_stride=True))
| 279 | 27 | 76 | py |
mmsegmentation | mmsegmentation-master/configs/resnest/deeplabv3plus_s101-d8_512x512_160k_ade20k.py | _base_ = '../deeplabv3plus/deeplabv3plus_r101-d8_512x512_160k_ade20k.py'
model = dict(
pretrained='open-mmlab://resnest101',
backbone=dict(
type='ResNeSt',
stem_channels=128,
radix=2,
reduction_factor=4,
avg_down_stride=True))
| 275 | 26.6 | 72 | py |
mmsegmentation | mmsegmentation-master/configs/resnest/fcn_s101-d8_512x1024_80k_cityscapes.py | _base_ = '../fcn/fcn_r101-d8_512x1024_80k_cityscapes.py'
model = dict(
pretrained='open-mmlab://resnest101',
backbone=dict(
type='ResNeSt',
stem_channels=128,
radix=2,
reduction_factor=4,
avg_down_stride=True))
| 259 | 25 | 56 | py |
mmsegmentation | mmsegmentation-master/configs/resnest/fcn_s101-d8_512x512_160k_ade20k.py | _base_ = '../fcn/fcn_r101-d8_512x512_160k_ade20k.py'
model = dict(
pretrained='open-mmlab://resnest101',
backbone=dict(
type='ResNeSt',
stem_channels=128,
radix=2,
reduction_factor=4,
avg_down_stride=True))
| 255 | 24.6 | 52 | py |
mmsegmentation | mmsegmentation-master/configs/resnest/pspnet_s101-d8_512x1024_80k_cityscapes.py | _base_ = '../pspnet/pspnet_r101-d8_512x1024_80k_cityscapes.py'
model = dict(
pretrained='open-mmlab://resnest101',
backbone=dict(
type='ResNeSt',
stem_channels=128,
radix=2,
reduction_factor=4,
avg_down_stride=True))
| 265 | 25.6 | 62 | py |
mmsegmentation | mmsegmentation-master/configs/resnest/pspnet_s101-d8_512x512_160k_ade20k.py | _base_ = '../pspnet/pspnet_r101-d8_512x512_160k_ade20k.py'
model = dict(
pretrained='open-mmlab://resnest101',
backbone=dict(
type='ResNeSt',
stem_channels=128,
radix=2,
reduction_factor=4,
avg_down_stride=True))
| 261 | 25.2 | 58 | py |
mmsegmentation | mmsegmentation-master/configs/resnest/resnest.yml | Models:
- Name: fcn_s101-d8_512x1024_80k_cityscapes
In Collection: FCN
Metadata:
backbone: S-101-D8
crop size: (512,1024)
lr schd: 80000
inference time (ms/im):
- value: 418.41
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,1024)
Train... | 5,664 | 30.825843 | 190 | yml |
mmsegmentation | mmsegmentation-master/configs/segformer/README.md | # SegFormer
[SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://arxiv.org/abs/2105.15203)
## Introduction
<!-- [ALGORITHM] -->
<a href="https://github.com/NVlabs/SegFormer">Official Repo</a>
<a href="https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/bac... | 15,635 | 121.15625 | 1,339 | md |
mmsegmentation | mmsegmentation-master/configs/segformer/segformer.yml | Collections:
- Name: Segformer
Metadata:
Training Data:
- ADE20K
- Cityscapes
Paper:
URL: https://arxiv.org/abs/2105.15203
Title: 'SegFormer: Simple and Efficient Design for Semantic Segmentation with
Transformers'
README: configs/segformer/README.md
Code:
URL: https://github.com/o... | 9,886 | 31.523026 | 194 | yml |
mmsegmentation | mmsegmentation-master/configs/segformer/segformer_mit-b0_512x512_160k_ade20k.py | _base_ = [
'../_base_/models/segformer_mit-b0.py', '../_base_/datasets/ade20k.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py'
]
checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth' # noqa
model = dict(pretrained=checkpoi... | 899 | 24.714286 | 121 | py |
mmsegmentation | mmsegmentation-master/configs/segformer/segformer_mit-b0_8x1_1024x1024_160k_cityscapes.py | _base_ = [
'../_base_/models/segformer_mit-b0.py',
'../_base_/datasets/cityscapes_1024x1024.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py'
]
checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth' # noqa
model = dict(
... | 1,012 | 25.657895 | 121 | py |
mmsegmentation | mmsegmentation-master/configs/segformer/segformer_mit-b1_512x512_160k_ade20k.py | _base_ = ['./segformer_mit-b0_512x512_160k_ade20k.py']
checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b1_20220624-02e5a6a1.pth' # noqa
# model settings
model = dict(
pretrained=checkpoint,
backbone=dict(
embed_dims=64, num_heads=[1, 2, 5, 8], num_layers=[2, 2,... | 384 | 34 | 121 | py |
mmsegmentation | mmsegmentation-master/configs/segformer/segformer_mit-b1_8x1_1024x1024_160k_cityscapes.py | _base_ = ['./segformer_mit-b0_8x1_1024x1024_160k_cityscapes.py']
checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b1_20220624-02e5a6a1.pth' # noqa
model = dict(
backbone=dict(
init_cfg=dict(type='Pretrained', checkpoint=checkpoint),
embed_dims=64),
decode... | 365 | 39.666667 | 121 | py |
mmsegmentation | mmsegmentation-master/configs/segformer/segformer_mit-b2_512x512_160k_ade20k.py | _base_ = ['./segformer_mit-b0_512x512_160k_ade20k.py']
checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b2_20220624-66e8bf70.pth' # noqa
# model settings
model = dict(
pretrained=checkpoint,
backbone=dict(
embed_dims=64, num_heads=[1, 2, 5, 8], num_layers=[3, 4,... | 384 | 34 | 121 | py |
mmsegmentation | mmsegmentation-master/configs/segformer/segformer_mit-b2_8x1_1024x1024_160k_cityscapes.py | _base_ = ['./segformer_mit-b0_8x1_1024x1024_160k_cityscapes.py']
checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b2_20220624-66e8bf70.pth' # noqa
model = dict(
backbone=dict(
init_cfg=dict(type='Pretrained', checkpoint=checkpoint),
embed_dims=64,
num... | 398 | 38.9 | 121 | py |
mmsegmentation | mmsegmentation-master/configs/segformer/segformer_mit-b3_512x512_160k_ade20k.py | _base_ = ['./segformer_mit-b0_512x512_160k_ade20k.py']
checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b3_20220624-13b1141c.pth' # noqa
# model settings
model = dict(
pretrained=checkpoint,
backbone=dict(
embed_dims=64, num_heads=[1, 2, 5, 8], num_layers=[3, 4,... | 385 | 34.090909 | 121 | py |
mmsegmentation | mmsegmentation-master/configs/segformer/segformer_mit-b3_8x1_1024x1024_160k_cityscapes.py | _base_ = ['./segformer_mit-b0_8x1_1024x1024_160k_cityscapes.py']
checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b3_20220624-13b1141c.pth' # noqa
model = dict(
backbone=dict(
init_cfg=dict(type='Pretrained', checkpoint=checkpoint),
embed_dims=64,
num... | 399 | 39 | 121 | py |
mmsegmentation | mmsegmentation-master/configs/segformer/segformer_mit-b4_512x512_160k_ade20k.py | _base_ = ['./segformer_mit-b0_512x512_160k_ade20k.py']
checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b4_20220624-d588d980.pth' # noqa
# model settings
model = dict(
pretrained=checkpoint,
backbone=dict(
embed_dims=64, num_heads=[1, 2, 5, 8], num_layers=[3, 8,... | 385 | 34.090909 | 121 | py |
mmsegmentation | mmsegmentation-master/configs/segformer/segformer_mit-b4_8x1_1024x1024_160k_cityscapes.py | _base_ = ['./segformer_mit-b0_8x1_1024x1024_160k_cityscapes.py']
checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b4_20220624-d588d980.pth' # noqa
model = dict(
backbone=dict(
init_cfg=dict(type='Pretrained', checkpoint=checkpoint),
embed_dims=64,
num... | 399 | 39 | 121 | py |
mmsegmentation | mmsegmentation-master/configs/segformer/segformer_mit-b5_512x512_160k_ade20k.py | _base_ = ['./segformer_mit-b0_512x512_160k_ade20k.py']
checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b5_20220624-658746d9.pth' # noqa
# model settings
model = dict(
pretrained=checkpoint,
backbone=dict(
embed_dims=64, num_heads=[1, 2, 5, 8], num_layers=[3, 6,... | 385 | 34.090909 | 121 | py |
mmsegmentation | mmsegmentation-master/configs/segformer/segformer_mit-b5_640x640_160k_ade20k.py | _base_ = ['./segformer_mit-b0_512x512_160k_ade20k.py']
# dataset settings
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
crop_size = (640, 640)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', reduce_zero_label=True),
dict(t... | 1,680 | 35.543478 | 121 | py |
mmsegmentation | mmsegmentation-master/configs/segformer/segformer_mit-b5_8x1_1024x1024_160k_cityscapes.py | _base_ = ['./segformer_mit-b0_8x1_1024x1024_160k_cityscapes.py']
checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b5_20220624-658746d9.pth' # noqa
model = dict(
backbone=dict(
init_cfg=dict(type='Pretrained', checkpoint=checkpoint),
embed_dims=64,
num... | 399 | 39 | 121 | py |
mmsegmentation | mmsegmentation-master/configs/segmenter/README.md | # Segmenter
[Segmenter: Transformer for Semantic Segmentation](https://arxiv.org/abs/2105.05633)
## Introduction
<!-- [ALGORITHM] -->
<a href="https://github.com/rstrudel/segmenter">Official Repo</a>
<a href="https://github.com/open-mmlab/mmsegmentation/blob/v0.21.0/mmseg/models/decode_heads/segmenter_mask_head.py... | 9,362 | 117.518987 | 1,290 | md |
mmsegmentation | mmsegmentation-master/configs/segmenter/segmenter.yml | Collections:
- Name: Segmenter
Metadata:
Training Data:
- ADE20K
Paper:
URL: https://arxiv.org/abs/2105.05633
Title: 'Segmenter: Transformer for Semantic Segmentation'
README: configs/segmenter/README.md
Code:
URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.21.0/mmseg/models/decode... | 4,132 | 31.801587 | 194 | yml |
mmsegmentation | mmsegmentation-master/configs/segmenter/segmenter_vit-b_mask_8x1_512x512_160k_ade20k.py | _base_ = [
'../_base_/models/segmenter_vit-b16_mask.py',
'../_base_/datasets/ade20k.py', '../_base_/default_runtime.py',
'../_base_/schedules/schedule_160k.py'
]
optimizer = dict(lr=0.001, weight_decay=0.0)
img_norm_cfg = dict(
mean=[127.5, 127.5, 127.5], std=[127.5, 127.5, 127.5], to_rgb=True)
crop_si... | 1,546 | 34.159091 | 71 | py |
mmsegmentation | mmsegmentation-master/configs/segmenter/segmenter_vit-l_mask_8x1_640x640_160k_ade20k.py | _base_ = [
'../_base_/models/segmenter_vit-b16_mask.py',
'../_base_/datasets/ade20k_640x640.py', '../_base_/default_runtime.py',
'../_base_/schedules/schedule_160k.py'
]
checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segmenter/vit_large_p16_384_20220308-d4efb41d.pth' # noqa
mode... | 2,121 | 33.225806 | 132 | py |
mmsegmentation | mmsegmentation-master/configs/segmenter/segmenter_vit-s_linear_8x1_512x512_160k_ade20k.py | _base_ = './segmenter_vit-s_mask_8x1_512x512_160k_ade20k.py'
model = dict(
decode_head=dict(
_delete_=True,
type='FCNHead',
in_channels=384,
channels=384,
num_convs=0,
dropout_ratio=0.0,
concat_input=False,
num_classes=150,
loss_decode=dict(
... | 394 | 25.333333 | 74 | py |
mmsegmentation | mmsegmentation-master/configs/segmenter/segmenter_vit-s_mask_8x1_512x512_160k_ade20k.py | _base_ = [
'../_base_/models/segmenter_vit-b16_mask.py',
'../_base_/datasets/ade20k.py', '../_base_/default_runtime.py',
'../_base_/schedules/schedule_160k.py'
]
checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segmenter/vit_small_p16_384_20220308-410f6037.pth' # noqa
backbone_no... | 2,223 | 32.19403 | 132 | py |
mmsegmentation | mmsegmentation-master/configs/segmenter/segmenter_vit-t_mask_8x1_512x512_160k_ade20k.py | _base_ = [
'../_base_/models/segmenter_vit-b16_mask.py',
'../_base_/datasets/ade20k.py', '../_base_/default_runtime.py',
'../_base_/schedules/schedule_160k.py'
]
checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segmenter/vit_tiny_p16_384_20220308-cce8c795.pth' # noqa
model = dict... | 1,930 | 32.877193 | 131 | py |
mmsegmentation | mmsegmentation-master/configs/segnext/README.md | # SegNeXt
[SegNeXt: Rethinking Convolutional Attention Design for Semantic Segmentation](https://arxiv.org/abs/2209.08575)
## Introduction
<!-- [ALGORITHM] -->
<a href="https://github.com/visual-attention-network/segnext">Official Repo</a>
<a href="https://github.com/open-mmlab/mmsegmentation/blob/v0.31.0/mmseg/mo... | 7,509 | 120.129032 | 1,402 | md |
mmsegmentation | mmsegmentation-master/configs/segnext/segnext.yml | Collections:
- Name: SegNeXt
Metadata:
Training Data:
- ADE20K
Paper:
URL: https://arxiv.org/abs/2209.08575
Title: 'SegNeXt: Rethinking Convolutional Attention Design for Semantic Segmentation'
README: configs/segnext/README.md
Code:
URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.... | 3,417 | 31.865385 | 192 | yml |
mmsegmentation | mmsegmentation-master/configs/segnext/segnext_mscan-b_1x16_512x512_adamw_160k_ade20k.py | _base_ = './segnext_mscan-t_1x16_512x512_adamw_160k_ade20k.py'
# model settings
checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_b_20230227-3ab7d230.pth' # noqa
ham_norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
type='EncoderDecoder',
bac... | 1,029 | 35.785714 | 125 | py |
mmsegmentation | mmsegmentation-master/configs/segnext/segnext_mscan-l_1x16_512x512_adamw_160k_ade20k.py | _base_ = './segnext_mscan-t_1x16_512x512_adamw_160k_ade20k.py'
# model settings
checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_l_20230227-cef260d4.pth' # noqa
ham_norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
type='EncoderDecoder',
bac... | 1,031 | 35.857143 | 125 | py |
mmsegmentation | mmsegmentation-master/configs/segnext/segnext_mscan-s_1x16_512x512_adamw_160k_ade20k.py | _base_ = './segnext_mscan-t_1x16_512x512_adamw_160k_ade20k.py'
# model settings
checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_s_20230227-f33ccdf2.pth' # noqa
ham_norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
type='EncoderDecoder',
bac... | 1,034 | 35.964286 | 125 | py |
mmsegmentation | mmsegmentation-master/configs/segnext/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k.py | _base_ = [
'../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py'
]
# model settings
checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth' # noqa
ham_norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
type=... | 3,968 | 30.251969 | 125 | py |
mmsegmentation | mmsegmentation-master/configs/sem_fpn/README.md | # Semantic FPN
[Panoptic Feature Pyramid Networks](https://arxiv.org/abs/1901.02446)
## Introduction
<!-- [ALGORITHM] -->
<a href="https://github.com/facebookresearch/detectron2">Official Repo</a>
<a href="https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fpn_head.py#L12">Code Sni... | 6,364 | 121.403846 | 1,184 | md |
mmsegmentation | mmsegmentation-master/configs/sem_fpn/fpn_r101_512x1024_80k_cityscapes.py | _base_ = './fpn_r50_512x1024_80k_cityscapes.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
| 128 | 42 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/sem_fpn/fpn_r101_512x512_160k_ade20k.py | _base_ = './fpn_r50_512x512_160k_ade20k.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
| 124 | 40.666667 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/sem_fpn/fpn_r50_512x1024_80k_cityscapes.py | _base_ = [
'../_base_/models/fpn_r50.py', '../_base_/datasets/cityscapes.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
]
| 158 | 30.8 | 73 | py |
mmsegmentation | mmsegmentation-master/configs/sem_fpn/fpn_r50_512x512_160k_ade20k.py | _base_ = [
'../_base_/models/fpn_r50.py', '../_base_/datasets/ade20k.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py'
]
model = dict(decode_head=dict(num_classes=150))
| 203 | 33 | 74 | py |
mmsegmentation | mmsegmentation-master/configs/sem_fpn/sem_fpn.yml | Collections:
- Name: FPN
Metadata:
Training Data:
- Cityscapes
- ADE20K
Paper:
URL: https://arxiv.org/abs/1901.02446
Title: Panoptic Feature Pyramid Networks
README: configs/sem_fpn/README.md
Code:
URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/f... | 3,097 | 28.504762 | 164 | yml |
mmsegmentation | mmsegmentation-master/configs/setr/README.md | # SETR
[Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers](https://arxiv.org/abs/2012.15840)
## Introduction
<!-- [ALGORITHM] -->
<a href="https://github.com/fudan-zvg/SETR">Official Repo</a>
<a href="https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/de... | 9,431 | 124.76 | 1,292 | md |
mmsegmentation | mmsegmentation-master/configs/setr/setr.yml | Collections:
- Name: SETR
Metadata:
Training Data:
- ADE20K
- Cityscapes
Paper:
URL: https://arxiv.org/abs/2012.15840
Title: Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective
with Transformers
README: configs/setr/README.md
Code:
URL: https://github.com/open-... | 5,204 | 30.545455 | 191 | yml |
mmsegmentation | mmsegmentation-master/configs/setr/setr_mla_512x512_160k_b16_ade20k.py | _base_ = ['./setr_mla_512x512_160k_b8_ade20k.py']
# num_gpus: 8 -> batch_size: 16
data = dict(samples_per_gpu=2)
| 114 | 22 | 49 | py |
mmsegmentation | mmsegmentation-master/configs/setr/setr_mla_512x512_160k_b8_ade20k.py | _base_ = [
'../_base_/models/setr_mla.py', '../_base_/datasets/ade20k.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py'
]
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
pretrained=None,
backbone=dict(
img_size=(512, 512),
drop_rate=0.,
... | 2,635 | 29.651163 | 78 | py |
mmsegmentation | mmsegmentation-master/configs/setr/setr_naive_512x512_160k_b16_ade20k.py | _base_ = [
'../_base_/models/setr_naive.py', '../_base_/datasets/ade20k.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py'
]
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
pretrained=None,
backbone=dict(
img_size=(512, 512),
drop_rate=0.,
... | 2,077 | 29.558824 | 78 | py |
mmsegmentation | mmsegmentation-master/configs/setr/setr_pup_512x512_160k_b16_ade20k.py | _base_ = [
'../_base_/models/setr_pup.py', '../_base_/datasets/ade20k.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py'
]
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
pretrained=None,
backbone=dict(
img_size=(512, 512),
drop_rate=0.,
... | 2,077 | 29.558824 | 78 | py |
mmsegmentation | mmsegmentation-master/configs/setr/setr_vit-large_mla_8x1_768x768_80k_cityscapes.py | _base_ = [
'../_base_/models/setr_mla.py', '../_base_/datasets/cityscapes_768x768.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
]
model = dict(
pretrained=None,
backbone=dict(
drop_rate=0,
init_cfg=dict(
type='Pretrained', checkpoint='pretrain/vit... | 564 | 30.388889 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/setr/setr_vit-large_naive_8x1_768x768_80k_cityscapes.py | _base_ = [
'../_base_/models/setr_naive.py',
'../_base_/datasets/cityscapes_768x768.py', '../_base_/default_runtime.py',
'../_base_/schedules/schedule_80k.py'
]
model = dict(
pretrained=None,
backbone=dict(
drop_rate=0.,
init_cfg=dict(
type='Pretrained', checkpoint='pretr... | 558 | 28.421053 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/setr/setr_vit-large_pup_8x1_768x768_80k_cityscapes.py | _base_ = [
'../_base_/models/setr_pup.py', '../_base_/datasets/cityscapes_768x768.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
]
norm_cfg = dict(type='SyncBN', requires_grad=True)
crop_size = (768, 768)
model = dict(
pretrained=None,
backbone=dict(
drop_rate=0.,
... | 1,946 | 28.953846 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/stdc/README.md | # STDC
[Rethinking BiSeNet For Real-time Semantic Segmentation](https://arxiv.org/abs/2104.13188)
## Introduction
<!-- [ALGORITHM] -->
<a href="https://github.com/MichaelFan01/STDC-Seg">Official Repo</a>
<a href="https://github.com/open-mmlab/mmsegmentation/blob/v0.20.0/mmseg/models/backbones/stdc.py#L394">Code Sn... | 7,406 | 99.094595 | 1,347 | md |
mmsegmentation | mmsegmentation-master/configs/stdc/stdc.yml | Collections:
- Name: STDC
Metadata:
Training Data:
- Cityscapes
Paper:
URL: https://arxiv.org/abs/2104.13188
Title: Rethinking BiSeNet For Real-time Semantic Segmentation
README: configs/stdc/README.md
Code:
URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.20.0/mmseg/models/backbone... | 2,777 | 30.568182 | 173 | yml |
mmsegmentation | mmsegmentation-master/configs/stdc/stdc1_512x1024_80k_cityscapes.py | _base_ = [
'../_base_/models/stdc.py', '../_base_/datasets/cityscapes.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
]
lr_config = dict(warmup='linear', warmup_iters=1000)
data = dict(
samples_per_gpu=12,
workers_per_gpu=4,
)
| 270 | 26.1 | 73 | py |
mmsegmentation | mmsegmentation-master/configs/stdc/stdc1_in1k-pre_512x1024_80k_cityscapes.py | checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/stdc/stdc1_20220308-5368626c.pth' # noqa
_base_ = './stdc1_512x1024_80k_cityscapes.py'
model = dict(
backbone=dict(
backbone_cfg=dict(
init_cfg=dict(type='Pretrained', checkpoint=checkpoint))))
| 293 | 41 | 115 | py |
mmsegmentation | mmsegmentation-master/configs/stdc/stdc2_512x1024_80k_cityscapes.py | _base_ = './stdc1_512x1024_80k_cityscapes.py'
model = dict(backbone=dict(backbone_cfg=dict(stdc_type='STDCNet2')))
| 115 | 37.666667 | 68 | py |
mmsegmentation | mmsegmentation-master/configs/stdc/stdc2_in1k-pre_512x1024_80k_cityscapes.py | checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/stdc/stdc2_20220308-7dbd9127.pth' # noqa
_base_ = './stdc2_512x1024_80k_cityscapes.py'
model = dict(
backbone=dict(
backbone_cfg=dict(
init_cfg=dict(type='Pretrained', checkpoint=checkpoint))))
| 293 | 41 | 115 | py |
mmsegmentation | mmsegmentation-master/configs/swin/README.md | # Swin Transformer
[Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://arxiv.org/abs/2103.14030)
## Introduction
<!-- [BACKBONE] -->
<a href="https://github.com/microsoft/Swin-Transformer">Official Repo</a>
<a href="https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/model... | 13,529 | 166.037037 | 1,566 | md |
mmsegmentation | mmsegmentation-master/configs/swin/swin.yml | Models:
- Name: upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K
In Collection: UPerNet
Metadata:
backbone: Swin-T
crop size: (512,512)
lr schd: 160000
inference time (ms/im):
- value: 47.48
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
... | 6,339 | 38.135802 | 247 | yml |
mmsegmentation | mmsegmentation-master/configs/swin/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_1K.py | _base_ = [
'upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_'
'pretrain_224x224_1K.py'
]
checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_base_patch4_window12_384_20220317-55b0104a.pth' # noqa
model = dict(
backbone=dict(
init_cfg=dict(type='Pretrained',... | 627 | 38.25 | 144 | py |
mmsegmentation | mmsegmentation-master/configs/swin/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K.py | _base_ = [
'./upernet_swin_base_patch4_window12_512x512_160k_ade20k_'
'pretrain_384x384_1K.py'
]
checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_base_patch4_window12_384_22k_20220317-e5c09f74.pth' # noqa
model = dict(
backbone=dict(
init_cfg=dict(type='Pretr... | 358 | 38.888889 | 148 | py |
mmsegmentation | mmsegmentation-master/configs/swin/upernet_swin_base_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K.py | _base_ = [
'./upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_'
'pretrain_224x224_1K.py'
]
checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_base_patch4_window7_224_20220317-e9b98025.pth' # noqa
model = dict(
backbone=dict(
init_cfg=dict(type='Pretrained'... | 573 | 40 | 143 | py |
mmsegmentation | mmsegmentation-master/configs/swin/upernet_swin_base_patch4_window7_512x512_160k_ade20k_pretrain_224x224_22K.py | _base_ = [
'./upernet_swin_base_patch4_window7_512x512_160k_ade20k_'
'pretrain_224x224_1K.py'
]
checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_base_patch4_window7_224_22k_20220317-4f79f7c0.pth' # noqa
model = dict(
backbone=dict(
init_cfg=dict(type='Pretrai... | 356 | 38.666667 | 147 | py |
mmsegmentation | mmsegmentation-master/configs/swin/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k.py | _base_ = [
'upernet_swin_large_patch4_window7_512x512_'
'pretrain_224x224_22K_160k_ade20k.py'
]
checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_large_patch4_window12_384_22k_20220412-6580f57d.pth' # noqa
model = dict(
backbone=dict(
init_cfg=dict(type='Pretr... | 413 | 36.636364 | 149 | py |
mmsegmentation | mmsegmentation-master/configs/swin/upernet_swin_large_patch4_window7_512x512_pretrain_224x224_22K_160k_ade20k.py | _base_ = [
'upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_'
'pretrain_224x224_1K.py'
]
checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_large_patch4_window7_224_22k_20220412-aeecf2aa.pth' # noqa
model = dict(
backbone=dict(
init_cfg=dict(type='Pretrain... | 631 | 38.5 | 148 | py |
mmsegmentation | mmsegmentation-master/configs/swin/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K.py | _base_ = [
'./upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_'
'pretrain_224x224_1K.py'
]
checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_small_patch4_window7_224_20220317-7ba6d6dd.pth' # noqa
model = dict(
backbone=dict(
init_cfg=dict(type='Pretrained... | 514 | 41.916667 | 144 | py |
mmsegmentation | mmsegmentation-master/configs/swin/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K.py | _base_ = [
'../_base_/models/upernet_swin.py', '../_base_/datasets/ade20k.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py'
]
checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' # noqa
model = dict(
... | 1,428 | 30.065217 | 143 | py |
mmsegmentation | mmsegmentation-master/configs/twins/README.md | # Twins
[Twins: Revisiting the Design of Spatial Attention in Vision Transformers](https://arxiv.org/pdf/2104.13840.pdf)
## Introduction
<!-- [BACKBONE] -->
<a href = "https://github.com/Meituan-AutoML/Twins">Official Repo</a>
<a href="https://github.com/open-mmlab/mmsegmentation/blob/v0.20.0/mmseg/models/backbone... | 11,985 | 154.662338 | 1,075 | md |
mmsegmentation | mmsegmentation-master/configs/twins/twins.yml | Models:
- Name: twins_pcpvt-s_fpn_fpnhead_8x4_512x512_80k_ade20k
In Collection: FPN
Metadata:
backbone: PCPVT-S
crop size: (512,512)
lr schd: 80000
inference time (ms/im):
- value: 36.83
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
... | 8,747 | 31.887218 | 194 | yml |
mmsegmentation | mmsegmentation-master/configs/twins/twins_pcpvt-b_fpn_fpnhead_8x4_512x512_80k_ade20k.py | _base_ = ['./twins_pcpvt-s_fpn_fpnhead_8x4_512x512_80k_ade20k.py']
checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/twins/pcpvt_base_20220308-0621964c.pth' # noqa
model = dict(
backbone=dict(
init_cfg=dict(type='Pretrained', checkpoint=checkpoint),
depths=[3, 4, 18, 3]), ... | 322 | 34.888889 | 121 | py |
mmsegmentation | mmsegmentation-master/configs/twins/twins_pcpvt-b_uperhead_8x2_512x512_160k_ade20k.py | _base_ = ['./twins_pcpvt-s_uperhead_8x4_512x512_160k_ade20k.py']
checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/twins/pcpvt_base_20220308-0621964c.pth' # noqa
model = dict(
backbone=dict(
init_cfg=dict(type='Pretrained', checkpoint=checkpoint),
depths=[3, 4, 18, 3],
... | 397 | 32.166667 | 121 | py |
mmsegmentation | mmsegmentation-master/configs/twins/twins_pcpvt-l_fpn_fpnhead_8x4_512x512_80k_ade20k.py | _base_ = ['./twins_pcpvt-s_fpn_fpnhead_8x4_512x512_80k_ade20k.py']
checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/twins/pcpvt_large_20220308-37579dc6.pth' # noqa
model = dict(
backbone=dict(
init_cfg=dict(type='Pretrained', checkpoint=checkpoint),
depths=[3, 8, 27, 3]))... | 321 | 34.777778 | 122 | py |
mmsegmentation | mmsegmentation-master/configs/twins/twins_pcpvt-l_uperhead_8x2_512x512_160k_ade20k.py | _base_ = ['./twins_pcpvt-s_uperhead_8x4_512x512_160k_ade20k.py']
checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/twins/pcpvt_large_20220308-37579dc6.pth' # noqa
model = dict(
backbone=dict(
init_cfg=dict(type='Pretrained', checkpoint=checkpoint),
depths=[3, 8, 27, 3],
... | 398 | 32.25 | 122 | py |
mmsegmentation | mmsegmentation-master/configs/twins/twins_pcpvt-s_fpn_fpnhead_8x4_512x512_80k_ade20k.py | _base_ = [
'../_base_/models/twins_pcpvt-s_fpn.py', '../_base_/datasets/ade20k.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
]
optimizer = dict(_delete_=True, type='AdamW', lr=0.0001, weight_decay=0.0001)
| 243 | 33.857143 | 77 | py |
mmsegmentation | mmsegmentation-master/configs/twins/twins_pcpvt-s_uperhead_8x4_512x512_160k_ade20k.py | _base_ = [
'../_base_/models/twins_pcpvt-s_upernet.py',
'../_base_/datasets/ade20k.py', '../_base_/default_runtime.py',
'../_base_/schedules/schedule_160k.py'
]
optimizer = dict(
_delete_=True,
type='AdamW',
lr=0.00006,
betas=(0.9, 0.999),
weight_decay=0.01,
paramwise_cfg=dict(custo... | 590 | 20.888889 | 67 | py |