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/ocrnet/ocrnet_hr18_512x1024_160k_cityscapes.py | _base_ = [
'../_base_/models/ocrnet_hr18.py', '../_base_/datasets/cityscapes.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py'
]
| 163 | 31.8 | 74 | py |
mmsegmentation | mmsegmentation-master/configs/ocrnet/ocrnet_hr18_512x1024_40k_cityscapes.py | _base_ = [
'../_base_/models/ocrnet_hr18.py', '../_base_/datasets/cityscapes.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py'
]
| 162 | 31.6 | 74 | py |
mmsegmentation | mmsegmentation-master/configs/ocrnet/ocrnet_hr18_512x1024_80k_cityscapes.py | _base_ = [
'../_base_/models/ocrnet_hr18.py', '../_base_/datasets/cityscapes.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
]
| 162 | 31.6 | 74 | py |
mmsegmentation | mmsegmentation-master/configs/ocrnet/ocrnet_hr18_512x512_160k_ade20k.py | _base_ = [
'../_base_/models/ocrnet_hr18.py', '../_base_/datasets/ade20k.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py'
]
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(decode_head=[
dict(
type='FCNHead',
in_channels=[18, 36, 72, 144],
ch... | 1,107 | 29.777778 | 74 | py |
mmsegmentation | mmsegmentation-master/configs/ocrnet/ocrnet_hr18_512x512_20k_voc12aug.py | _base_ = [
'../_base_/models/ocrnet_hr18.py',
'../_base_/datasets/pascal_voc12_aug.py', '../_base_/default_runtime.py',
'../_base_/schedules/schedule_20k.py'
]
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(decode_head=[
dict(
type='FCNHead',
in_channels=[18, 36, 72, 144... | 1,118 | 29.243243 | 77 | py |
mmsegmentation | mmsegmentation-master/configs/ocrnet/ocrnet_hr18_512x512_40k_voc12aug.py | _base_ = [
'../_base_/models/ocrnet_hr18.py',
'../_base_/datasets/pascal_voc12_aug.py', '../_base_/default_runtime.py',
'../_base_/schedules/schedule_40k.py'
]
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(decode_head=[
dict(
type='FCNHead',
in_channels=[18, 36, 72, 144... | 1,118 | 29.243243 | 77 | py |
mmsegmentation | mmsegmentation-master/configs/ocrnet/ocrnet_hr18_512x512_80k_ade20k.py | _base_ = [
'../_base_/models/ocrnet_hr18.py', '../_base_/datasets/ade20k.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
]
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(decode_head=[
dict(
type='FCNHead',
in_channels=[18, 36, 72, 144],
cha... | 1,106 | 29.75 | 74 | py |
mmsegmentation | mmsegmentation-master/configs/ocrnet/ocrnet_hr18s_512x1024_160k_cityscapes.py | _base_ = './ocrnet_hr18_512x1024_160k_cityscapes.py'
model = dict(
pretrained='open-mmlab://msra/hrnetv2_w18_small',
backbone=dict(
extra=dict(
stage1=dict(num_blocks=(2, )),
stage2=dict(num_blocks=(2, 2)),
stage3=dict(num_modules=3, num_blocks=(2, 2, 2)),
... | 376 | 36.7 | 66 | py |
mmsegmentation | mmsegmentation-master/configs/ocrnet/ocrnet_hr18s_512x1024_40k_cityscapes.py | _base_ = './ocrnet_hr18_512x1024_40k_cityscapes.py'
model = dict(
pretrained='open-mmlab://msra/hrnetv2_w18_small',
backbone=dict(
extra=dict(
stage1=dict(num_blocks=(2, )),
stage2=dict(num_blocks=(2, 2)),
stage3=dict(num_modules=3, num_blocks=(2, 2, 2)),
... | 375 | 36.6 | 66 | py |
mmsegmentation | mmsegmentation-master/configs/ocrnet/ocrnet_hr18s_512x1024_80k_cityscapes.py | _base_ = './ocrnet_hr18_512x1024_80k_cityscapes.py'
model = dict(
pretrained='open-mmlab://msra/hrnetv2_w18_small',
backbone=dict(
extra=dict(
stage1=dict(num_blocks=(2, )),
stage2=dict(num_blocks=(2, 2)),
stage3=dict(num_modules=3, num_blocks=(2, 2, 2)),
... | 375 | 36.6 | 66 | py |
mmsegmentation | mmsegmentation-master/configs/ocrnet/ocrnet_hr18s_512x512_160k_ade20k.py | _base_ = './ocrnet_hr18_512x512_160k_ade20k.py'
model = dict(
pretrained='open-mmlab://msra/hrnetv2_w18_small',
backbone=dict(
extra=dict(
stage1=dict(num_blocks=(2, )),
stage2=dict(num_blocks=(2, 2)),
stage3=dict(num_modules=3, num_blocks=(2, 2, 2)),
stag... | 371 | 36.2 | 66 | py |
mmsegmentation | mmsegmentation-master/configs/ocrnet/ocrnet_hr18s_512x512_20k_voc12aug.py | _base_ = './ocrnet_hr18_512x512_20k_voc12aug.py'
model = dict(
pretrained='open-mmlab://msra/hrnetv2_w18_small',
backbone=dict(
extra=dict(
stage1=dict(num_blocks=(2, )),
stage2=dict(num_blocks=(2, 2)),
stage3=dict(num_modules=3, num_blocks=(2, 2, 2)),
sta... | 372 | 36.3 | 66 | py |
mmsegmentation | mmsegmentation-master/configs/ocrnet/ocrnet_hr18s_512x512_40k_voc12aug.py | _base_ = './ocrnet_hr18_512x512_40k_voc12aug.py'
model = dict(
pretrained='open-mmlab://msra/hrnetv2_w18_small',
backbone=dict(
extra=dict(
stage1=dict(num_blocks=(2, )),
stage2=dict(num_blocks=(2, 2)),
stage3=dict(num_modules=3, num_blocks=(2, 2, 2)),
sta... | 372 | 36.3 | 66 | py |
mmsegmentation | mmsegmentation-master/configs/ocrnet/ocrnet_hr18s_512x512_80k_ade20k.py | _base_ = './ocrnet_hr18_512x512_80k_ade20k.py'
model = dict(
pretrained='open-mmlab://msra/hrnetv2_w18_small',
backbone=dict(
extra=dict(
stage1=dict(num_blocks=(2, )),
stage2=dict(num_blocks=(2, 2)),
stage3=dict(num_modules=3, num_blocks=(2, 2, 2)),
stage... | 370 | 36.1 | 66 | py |
mmsegmentation | mmsegmentation-master/configs/ocrnet/ocrnet_hr48_512x1024_160k_cityscapes.py | _base_ = './ocrnet_hr18_512x1024_160k_cityscapes.py'
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
pretrained='open-mmlab://msra/hrnetv2_w48',
backbone=dict(
extra=dict(
stage2=dict(num_channels=(48, 96)),
stage3=dict(num_channels=(48, 96, 192)),
st... | 1,370 | 33.275 | 78 | py |
mmsegmentation | mmsegmentation-master/configs/ocrnet/ocrnet_hr48_512x1024_40k_cityscapes.py | _base_ = './ocrnet_hr18_512x1024_40k_cityscapes.py'
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
pretrained='open-mmlab://msra/hrnetv2_w48',
backbone=dict(
extra=dict(
stage2=dict(num_channels=(48, 96)),
stage3=dict(num_channels=(48, 96, 192)),
sta... | 1,369 | 33.25 | 78 | py |
mmsegmentation | mmsegmentation-master/configs/ocrnet/ocrnet_hr48_512x1024_80k_cityscapes.py | _base_ = './ocrnet_hr18_512x1024_80k_cityscapes.py'
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
pretrained='open-mmlab://msra/hrnetv2_w48',
backbone=dict(
extra=dict(
stage2=dict(num_channels=(48, 96)),
stage3=dict(num_channels=(48, 96, 192)),
sta... | 1,369 | 33.25 | 78 | py |
mmsegmentation | mmsegmentation-master/configs/ocrnet/ocrnet_hr48_512x512_160k_ade20k.py | _base_ = './ocrnet_hr18_512x512_160k_ade20k.py'
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
pretrained='open-mmlab://msra/hrnetv2_w48',
backbone=dict(
extra=dict(
stage2=dict(num_channels=(48, 96)),
stage3=dict(num_channels=(48, 96, 192)),
stage4=... | 1,367 | 33.2 | 78 | py |
mmsegmentation | mmsegmentation-master/configs/ocrnet/ocrnet_hr48_512x512_20k_voc12aug.py | _base_ = './ocrnet_hr18_512x512_20k_voc12aug.py'
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
pretrained='open-mmlab://msra/hrnetv2_w48',
backbone=dict(
extra=dict(
stage2=dict(num_channels=(48, 96)),
stage3=dict(num_channels=(48, 96, 192)),
stage4... | 1,366 | 33.175 | 78 | py |
mmsegmentation | mmsegmentation-master/configs/ocrnet/ocrnet_hr48_512x512_40k_voc12aug.py | _base_ = './ocrnet_hr18_512x512_40k_voc12aug.py'
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
pretrained='open-mmlab://msra/hrnetv2_w48',
backbone=dict(
extra=dict(
stage2=dict(num_channels=(48, 96)),
stage3=dict(num_channels=(48, 96, 192)),
stage4... | 1,366 | 33.175 | 78 | py |
mmsegmentation | mmsegmentation-master/configs/ocrnet/ocrnet_hr48_512x512_80k_ade20k.py | _base_ = './ocrnet_hr18_512x512_80k_ade20k.py'
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
pretrained='open-mmlab://msra/hrnetv2_w48',
backbone=dict(
extra=dict(
stage2=dict(num_channels=(48, 96)),
stage3=dict(num_channels=(48, 96, 192)),
stage4=d... | 1,366 | 33.175 | 78 | py |
mmsegmentation | mmsegmentation-master/configs/ocrnet/ocrnet_r101-d8_512x1024_40k_b16_cityscapes.py | _base_ = [
'../_base_/models/ocrnet_r50-d8.py', '../_base_/datasets/cityscapes.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py'
]
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
optimizer = dict(lr=0.02)
lr_config = dict(min_lr=2e-4)
| 300 | 36.625 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/ocrnet/ocrnet_r101-d8_512x1024_40k_b8_cityscapes.py | _base_ = [
'../_base_/models/ocrnet_r50-d8.py', '../_base_/datasets/cityscapes.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py'
]
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
| 244 | 39.833333 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/ocrnet/ocrnet_r101-d8_512x1024_80k_b16_cityscapes.py | _base_ = [
'../_base_/models/ocrnet_r50-d8.py', '../_base_/datasets/cityscapes.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
]
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
optimizer = dict(lr=0.02)
lr_config = dict(min_lr=2e-4)
| 300 | 36.625 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/point_rend/README.md | # PointRend
[PointRend: Image Segmentation as Rendering](https://arxiv.org/abs/1912.08193)
## Introduction
<!-- [ALGORITHM] -->
<a href="https://github.com/facebookresearch/detectron2/tree/master/projects/PointRend">Official Repo</a>
<a href="https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/d... | 6,883 | 131.384615 | 1,326 | md |
mmsegmentation | mmsegmentation-master/configs/point_rend/point_rend.yml | Collections:
- Name: PointRend
Metadata:
Training Data:
- Cityscapes
- ADE20K
Paper:
URL: https://arxiv.org/abs/1912.08193
Title: 'PointRend: Image Segmentation as Rendering'
README: configs/point_rend/README.md
Code:
URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/m... | 3,296 | 30.4 | 179 | yml |
mmsegmentation | mmsegmentation-master/configs/point_rend/pointrend_r101_512x1024_80k_cityscapes.py | _base_ = './pointrend_r50_512x1024_80k_cityscapes.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
| 134 | 44 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/point_rend/pointrend_r101_512x512_160k_ade20k.py | _base_ = './pointrend_r50_512x512_160k_ade20k.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
| 130 | 42.666667 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/point_rend/pointrend_r50_512x1024_80k_cityscapes.py | _base_ = [
'../_base_/models/pointrend_r50.py', '../_base_/datasets/cityscapes.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
]
lr_config = dict(warmup='linear', warmup_iters=200)
| 216 | 35.166667 | 76 | py |
mmsegmentation | mmsegmentation-master/configs/point_rend/pointrend_r50_512x512_160k_ade20k.py | _base_ = [
'../_base_/models/pointrend_r50.py', '../_base_/datasets/ade20k.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py'
]
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(decode_head=[
dict(
type='FPNHead',
in_channels=[256, 256, 256, 256],
... | 1,014 | 29.757576 | 74 | py |
mmsegmentation | mmsegmentation-master/configs/poolformer/README.md | # PoolFormer
[MetaFormer is Actually What You Need for Vision](https://arxiv.org/abs/2111.11418)
## Introduction
<!-- [BACKBONE] -->
<a href="https://github.com/sail-sg/poolformer/tree/main/segmentation">Official Repo</a>
<a href="https://github.com/open-mmlab/mmclassification/blob/v0.23.0/mmcls/models/backbones/p... | 7,922 | 122.796875 | 1,722 | md |
mmsegmentation | mmsegmentation-master/configs/poolformer/fpn_poolformer_m36_8x4_512x512_40k_ade20k.py | _base_ = './fpn_poolformer_s12_8x4_512x512_40k_ade20k.py'
checkpoint_file = 'https://download.openmmlab.com/mmclassification/v0/poolformer/poolformer-m36_3rdparty_32xb128_in1k_20220414-c55e0949.pth' # noqa
# model settings
model = dict(
backbone=dict(
arch='m36',
init_cfg=dict(
type='P... | 442 | 35.916667 | 148 | py |
mmsegmentation | mmsegmentation-master/configs/poolformer/fpn_poolformer_m48_8x4_512x512_40k_ade20k.py | _base_ = './fpn_poolformer_s12_8x4_512x512_40k_ade20k.py'
checkpoint_file = 'https://download.openmmlab.com/mmclassification/v0/poolformer/poolformer-m48_3rdparty_32xb128_in1k_20220414-9378f3eb.pth' # noqa
# model settings
model = dict(
backbone=dict(
arch='m48',
init_cfg=dict(
type='P... | 442 | 35.916667 | 148 | py |
mmsegmentation | mmsegmentation-master/configs/poolformer/fpn_poolformer_s12_8x4_512x512_40k_ade20k.py | _base_ = [
'../_base_/models/fpn_poolformer_s12.py', '../_base_/default_runtime.py',
'../_base_/schedules/schedule_40k.py'
]
# model settings
model = dict(
neck=dict(in_channels=[64, 128, 320, 512]),
decode_head=dict(num_classes=150))
# optimizer
optimizer = dict(_delete_=True, type='AdamW', lr=0.0002... | 2,454 | 31.733333 | 77 | py |
mmsegmentation | mmsegmentation-master/configs/poolformer/fpn_poolformer_s24_8x4_512x512_40k_ade20k.py | _base_ = './fpn_poolformer_s12_8x4_512x512_40k_ade20k.py'
checkpoint_file = 'https://download.openmmlab.com/mmclassification/v0/poolformer/poolformer-s24_3rdparty_32xb128_in1k_20220414-d7055904.pth' # noqa
# model settings
model = dict(
backbone=dict(
arch='s24',
init_cfg=dict(
type='Pr... | 393 | 38.4 | 148 | py |
mmsegmentation | mmsegmentation-master/configs/poolformer/fpn_poolformer_s36_8x4_512x512_40k_ade20k.py | _base_ = './fpn_poolformer_s12_8x4_512x512_40k_ade20k.py'
checkpoint_file = 'https://download.openmmlab.com/mmclassification/v0/poolformer/poolformer-s36_3rdparty_32xb128_in1k_20220414-d78ff3e8.pth' # noqa
# model settings
model = dict(
backbone=dict(
arch='s36',
init_cfg=dict(
type='P... | 394 | 34.909091 | 148 | py |
mmsegmentation | mmsegmentation-master/configs/poolformer/poolformer.yml | Models:
- Name: fpn_poolformer_s12_8x4_512x512_40k_ade20k
In Collection: FPN
Metadata:
backbone: PoolFormer-S12
crop size: (512,512)
lr schd: 40000
inference time (ms/im):
- value: 42.59
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
... | 3,630 | 31.419643 | 185 | yml |
mmsegmentation | mmsegmentation-master/configs/psanet/README.md | # PSANet
[PSANet: Point-wise Spatial Attention Network for Scene Parsing](https://openaccess.thecvf.com/content_ECCV_2018/papers/Hengshuang_Zhao_PSANet_Point-wise_Spatial_ECCV_2018_paper.pdf)
## Introduction
<!-- [ALGORITHM] -->
<a href="https://github.com/hszhao/PSANet">Official Repo</a>
<a href="https://github.c... | 14,359 | 207.115942 | 966 | md |
mmsegmentation | mmsegmentation-master/configs/psanet/psanet.yml | Collections:
- Name: PSANet
Metadata:
Training Data:
- Cityscapes
- ADE20K
- Pascal VOC 2012 + Aug
Paper:
URL: https://openaccess.thecvf.com/content_ECCV_2018/papers/Hengshuang_Zhao_PSANet_Point-wise_Spatial_ECCV_2018_paper.pdf
Title: 'PSANet: Point-wise Spatial Attention Network for Scene P... | 10,212 | 32.375817 | 175 | yml |
mmsegmentation | mmsegmentation-master/configs/psanet/psanet_r101-d8_512x1024_40k_cityscapes.py | _base_ = './psanet_r50-d8_512x1024_40k_cityscapes.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
| 134 | 44 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/psanet/psanet_r101-d8_512x1024_80k_cityscapes.py | _base_ = './psanet_r50-d8_512x1024_80k_cityscapes.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
| 134 | 44 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/psanet/psanet_r101-d8_512x512_160k_ade20k.py | _base_ = './psanet_r50-d8_512x512_160k_ade20k.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
| 130 | 42.666667 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/psanet/psanet_r101-d8_512x512_20k_voc12aug.py | _base_ = './psanet_r50-d8_512x512_20k_voc12aug.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
| 131 | 43 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/psanet/psanet_r101-d8_512x512_40k_voc12aug.py | _base_ = './psanet_r50-d8_512x512_40k_voc12aug.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
| 131 | 43 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/psanet/psanet_r101-d8_512x512_80k_ade20k.py | _base_ = './psanet_r50-d8_512x512_80k_ade20k.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
| 129 | 42.333333 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/psanet/psanet_r101-d8_769x769_40k_cityscapes.py | _base_ = './psanet_r50-d8_769x769_40k_cityscapes.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
| 133 | 43.666667 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/psanet/psanet_r101-d8_769x769_80k_cityscapes.py | _base_ = './psanet_r50-d8_769x769_80k_cityscapes.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
| 133 | 43.666667 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/psanet/psanet_r50-d8_512x1024_40k_cityscapes.py | _base_ = [
'../_base_/models/psanet_r50-d8.py', '../_base_/datasets/cityscapes.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py'
]
| 164 | 32 | 76 | py |
mmsegmentation | mmsegmentation-master/configs/psanet/psanet_r50-d8_512x1024_80k_cityscapes.py | _base_ = [
'../_base_/models/psanet_r50-d8.py', '../_base_/datasets/cityscapes.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
]
| 164 | 32 | 76 | py |
mmsegmentation | mmsegmentation-master/configs/psanet/psanet_r50-d8_512x512_160k_ade20k.py | _base_ = [
'../_base_/models/psanet_r50-d8.py', '../_base_/datasets/ade20k.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py'
]
model = dict(
decode_head=dict(mask_size=(66, 66), num_classes=150),
auxiliary_head=dict(num_classes=150))
| 276 | 33.625 | 74 | py |
mmsegmentation | mmsegmentation-master/configs/psanet/psanet_r50-d8_512x512_20k_voc12aug.py | _base_ = [
'../_base_/models/psanet_r50-d8.py',
'../_base_/datasets/pascal_voc12_aug.py', '../_base_/default_runtime.py',
'../_base_/schedules/schedule_20k.py'
]
model = dict(
decode_head=dict(num_classes=21), auxiliary_head=dict(num_classes=21))
| 263 | 32 | 77 | py |
mmsegmentation | mmsegmentation-master/configs/psanet/psanet_r50-d8_512x512_40k_voc12aug.py | _base_ = [
'../_base_/models/psanet_r50-d8.py',
'../_base_/datasets/pascal_voc12_aug.py', '../_base_/default_runtime.py',
'../_base_/schedules/schedule_40k.py'
]
model = dict(
decode_head=dict(num_classes=21), auxiliary_head=dict(num_classes=21))
| 263 | 32 | 77 | py |
mmsegmentation | mmsegmentation-master/configs/psanet/psanet_r50-d8_512x512_80k_ade20k.py | _base_ = [
'../_base_/models/psanet_r50-d8.py', '../_base_/datasets/ade20k.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
]
model = dict(
decode_head=dict(mask_size=(66, 66), num_classes=150),
auxiliary_head=dict(num_classes=150))
| 275 | 33.5 | 73 | py |
mmsegmentation | mmsegmentation-master/configs/psanet/psanet_r50-d8_769x769_40k_cityscapes.py | _base_ = [
'../_base_/models/psanet_r50-d8.py',
'../_base_/datasets/cityscapes_769x769.py', '../_base_/default_runtime.py',
'../_base_/schedules/schedule_40k.py'
]
model = dict(
decode_head=dict(align_corners=True),
auxiliary_head=dict(align_corners=True),
test_cfg=dict(mode='slide', crop_size=(... | 351 | 34.2 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/psanet/psanet_r50-d8_769x769_80k_cityscapes.py | _base_ = [
'../_base_/models/psanet_r50-d8.py',
'../_base_/datasets/cityscapes_769x769.py', '../_base_/default_runtime.py',
'../_base_/schedules/schedule_80k.py'
]
model = dict(
decode_head=dict(align_corners=True),
auxiliary_head=dict(align_corners=True),
test_cfg=dict(mode='slide', crop_size=(... | 351 | 34.2 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/pspnet/README.md | # PSPNet
[Pyramid Scene Parsing Network](https://arxiv.org/abs/1612.01105)
## Introduction
<!-- [ALGORITHM] -->
<a href="https://github.com/hszhao/PSPNet">Official Repo</a>
<a href="https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/psp_head.py#L63">Code Snippet</a>
## Abstract
<... | 54,915 | 307.516854 | 780 | md |
mmsegmentation | mmsegmentation-master/configs/pspnet/pspnet.yml | Collections:
- Name: PSPNet
Metadata:
Training Data:
- Cityscapes
- ADE20K
- Pascal VOC 2012 + Aug
- Pascal Context
- Pascal Context 59
- Dark Zurich and Nighttime Driving
- COCO-Stuff 10k
- COCO-Stuff 164k
- LoveDA
- Potsdam
- Vaihingen
- iSAID
Paper:
URL: ht... | 35,801 | 32.211503 | 213 | yml |
mmsegmentation | mmsegmentation-master/configs/pspnet/pspnet_r101-d8_480x480_40k_pascal_context.py | _base_ = './pspnet_r50-d8_480x480_40k_pascal_context.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
| 137 | 45 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/pspnet/pspnet_r101-d8_480x480_40k_pascal_context_59.py | _base_ = './pspnet_r50-d8_480x480_40k_pascal_context_59.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
| 140 | 46 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/pspnet/pspnet_r101-d8_480x480_80k_pascal_context.py | _base_ = './pspnet_r50-d8_480x480_80k_pascal_context.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
| 137 | 45 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/pspnet/pspnet_r101-d8_480x480_80k_pascal_context_59.py | _base_ = './pspnet_r50-d8_480x480_80k_pascal_context_59.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
| 140 | 46 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/pspnet/pspnet_r101-d8_4x4_512x512_80k_potsdam.py | _base_ = './pspnet_r50-d8_4x4_512x512_80k_potsdam.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
| 134 | 44 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/pspnet/pspnet_r101-d8_4x4_512x512_80k_vaihingen.py | _base_ = './pspnet_r50-d8_4x4_512x512_80k_vaihingen.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
| 136 | 44.666667 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/pspnet/pspnet_r101-d8_512x1024_40k_cityscapes.py | _base_ = './pspnet_r50-d8_512x1024_40k_cityscapes.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
| 134 | 44 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/pspnet/pspnet_r101-d8_512x1024_40k_dark.py | _base_ = './pspnet_r50-d8_512x1024_40k_dark.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
| 128 | 42 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/pspnet/pspnet_r101-d8_512x1024_40k_night_driving.py | _base_ = './pspnet_r50-d8_512x1024_40k_night_driving.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
| 137 | 45 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/pspnet/pspnet_r101-d8_512x1024_80k_cityscapes.py | _base_ = './pspnet_r50-d8_512x1024_80k_cityscapes.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
| 134 | 44 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/pspnet/pspnet_r101-d8_512x512_160k_ade20k.py | _base_ = './pspnet_r50-d8_512x512_160k_ade20k.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
| 130 | 42.666667 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/pspnet/pspnet_r101-d8_512x512_20k_voc12aug.py | _base_ = './pspnet_r50-d8_512x512_20k_voc12aug.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
| 131 | 43 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/pspnet/pspnet_r101-d8_512x512_40k_voc12aug.py | _base_ = './pspnet_r50-d8_512x512_40k_voc12aug.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
| 131 | 43 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/pspnet/pspnet_r101-d8_512x512_4x4_160k_coco-stuff164k.py | _base_ = './pspnet_r50-d8_512x512_4x4_160k_coco-stuff164k.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
| 142 | 46.666667 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/pspnet/pspnet_r101-d8_512x512_4x4_20k_coco-stuff10k.py | _base_ = './pspnet_r50-d8_512x512_4x4_20k_coco-stuff10k.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
| 140 | 46 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/pspnet/pspnet_r101-d8_512x512_4x4_320k_coco-stuff164k.py | _base_ = './pspnet_r50-d8_512x512_4x4_320k_coco-stuff164k.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
| 142 | 46.666667 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/pspnet/pspnet_r101-d8_512x512_4x4_40k_coco-stuff10k.py | _base_ = './pspnet_r50-d8_512x512_4x4_40k_coco-stuff10k.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
| 140 | 46 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/pspnet/pspnet_r101-d8_512x512_4x4_80k_coco-stuff164k.py | _base_ = './pspnet_r50-d8_512x512_4x4_80k_coco-stuff164k.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
| 141 | 46.333333 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/pspnet/pspnet_r101-d8_512x512_80k_ade20k.py | _base_ = './pspnet_r50-d8_512x512_80k_ade20k.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
| 129 | 42.333333 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/pspnet/pspnet_r101-d8_512x512_80k_loveda.py | _base_ = './pspnet_r50-d8_512x512_80k_loveda.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://resnet101_v1c')))
| 198 | 27.428571 | 73 | py |
mmsegmentation | mmsegmentation-master/configs/pspnet/pspnet_r101-d8_769x769_40k_cityscapes.py | _base_ = './pspnet_r50-d8_769x769_40k_cityscapes.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
| 133 | 43.666667 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/pspnet/pspnet_r101-d8_769x769_80k_cityscapes.py | _base_ = './pspnet_r50-d8_769x769_80k_cityscapes.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
| 133 | 43.666667 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/pspnet/pspnet_r101-d8_fp16_512x1024_80k_cityscapes.py | _base_ = './pspnet_r101-d8_512x1024_80k_cityscapes.py'
# fp16 settings
optimizer_config = dict(type='Fp16OptimizerHook', loss_scale=512.)
# fp16 placeholder
fp16 = dict()
| 171 | 27.666667 | 66 | py |
mmsegmentation | mmsegmentation-master/configs/pspnet/pspnet_r101b-d8_512x1024_80k_cityscapes.py | _base_ = './pspnet_r50-d8_512x1024_80k_cityscapes.py'
model = dict(
pretrained='torchvision://resnet101',
backbone=dict(type='ResNet', depth=101))
| 155 | 30.2 | 53 | py |
mmsegmentation | mmsegmentation-master/configs/pspnet/pspnet_r101b-d8_512x1024_80k_dark.py | _base_ = './pspnet_r50-d8_512x1024_80k_dark.py'
model = dict(
pretrained='torchvision://resnet101',
backbone=dict(type='ResNet', depth=101))
| 149 | 29 | 47 | py |
mmsegmentation | mmsegmentation-master/configs/pspnet/pspnet_r101b-d8_512x1024_80k_night_driving.py | _base_ = './pspnet_r50-d8_512x1024_80k_night_driving.py'
model = dict(
pretrained='torchvision://resnet101',
backbone=dict(type='ResNet', depth=101))
| 158 | 30.8 | 56 | py |
mmsegmentation | mmsegmentation-master/configs/pspnet/pspnet_r101b-d8_769x769_80k_cityscapes.py | _base_ = './pspnet_r50-d8_769x769_80k_cityscapes.py'
model = dict(
pretrained='torchvision://resnet101',
backbone=dict(type='ResNet', depth=101))
| 154 | 30 | 52 | py |
mmsegmentation | mmsegmentation-master/configs/pspnet/pspnet_r18-d8_4x4_512x512_80k_potsdam.py | _base_ = './pspnet_r50-d8_4x4_512x512_80k_potsdam.py'
model = dict(
pretrained='open-mmlab://resnet18_v1c',
backbone=dict(depth=18),
decode_head=dict(
in_channels=512,
channels=128,
),
auxiliary_head=dict(in_channels=256, channels=64))
| 272 | 26.3 | 54 | py |
mmsegmentation | mmsegmentation-master/configs/pspnet/pspnet_r18-d8_4x4_512x512_80k_vaihingen.py | _base_ = './pspnet_r50-d8_4x4_512x512_80k_vaihingen.py'
model = dict(
pretrained='open-mmlab://resnet18_v1c',
backbone=dict(depth=18),
decode_head=dict(
in_channels=512,
channels=128,
),
auxiliary_head=dict(in_channels=256, channels=64))
| 274 | 26.5 | 55 | py |
mmsegmentation | mmsegmentation-master/configs/pspnet/pspnet_r18-d8_4x4_896x896_80k_isaid.py | _base_ = './pspnet_r50-d8_4x4_896x896_80k_isaid.py'
model = dict(
pretrained='open-mmlab://resnet18_v1c',
backbone=dict(depth=18),
decode_head=dict(
in_channels=512,
channels=128,
),
auxiliary_head=dict(in_channels=256, channels=64))
| 270 | 26.1 | 54 | py |
mmsegmentation | mmsegmentation-master/configs/pspnet/pspnet_r18-d8_512x1024_80k_cityscapes.py | _base_ = './pspnet_r50-d8_512x1024_80k_cityscapes.py'
model = dict(
pretrained='open-mmlab://resnet18_v1c',
backbone=dict(depth=18),
decode_head=dict(
in_channels=512,
channels=128,
),
auxiliary_head=dict(in_channels=256, channels=64))
| 272 | 26.3 | 54 | py |
mmsegmentation | mmsegmentation-master/configs/pspnet/pspnet_r18-d8_512x512_80k_loveda.py | _base_ = './pspnet_r50-d8_512x512_80k_loveda.py'
model = dict(
backbone=dict(
depth=18,
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://resnet18_v1c')),
decode_head=dict(
in_channels=512,
channels=128,
),
auxiliary_head=dict(in_channels=256, channel... | 327 | 26.333333 | 72 | py |
mmsegmentation | mmsegmentation-master/configs/pspnet/pspnet_r18-d8_769x769_80k_cityscapes.py | _base_ = './pspnet_r50-d8_769x769_80k_cityscapes.py'
model = dict(
pretrained='open-mmlab://resnet18_v1c',
backbone=dict(depth=18),
decode_head=dict(
in_channels=512,
channels=128,
),
auxiliary_head=dict(in_channels=256, channels=64))
| 271 | 26.2 | 54 | py |
mmsegmentation | mmsegmentation-master/configs/pspnet/pspnet_r18b-d8_512x1024_80k_cityscapes.py | _base_ = './pspnet_r50-d8_512x1024_80k_cityscapes.py'
model = dict(
pretrained='torchvision://resnet18',
backbone=dict(type='ResNet', depth=18),
decode_head=dict(
in_channels=512,
channels=128,
),
auxiliary_head=dict(in_channels=256, channels=64))
| 284 | 27.5 | 54 | py |
mmsegmentation | mmsegmentation-master/configs/pspnet/pspnet_r18b-d8_769x769_80k_cityscapes.py | _base_ = './pspnet_r50-d8_769x769_80k_cityscapes.py'
model = dict(
pretrained='torchvision://resnet18',
backbone=dict(type='ResNet', depth=18),
decode_head=dict(
in_channels=512,
channels=128,
),
auxiliary_head=dict(in_channels=256, channels=64))
| 283 | 27.4 | 54 | py |
mmsegmentation | mmsegmentation-master/configs/pspnet/pspnet_r50-d32_512x1024_80k_cityscapes.py | _base_ = [
'../_base_/models/pspnet_r50-d8.py', '../_base_/datasets/cityscapes.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
]
model = dict(backbone=dict(dilations=(1, 1, 2, 4), strides=(1, 2, 2, 2)))
| 238 | 38.833333 | 76 | py |
mmsegmentation | mmsegmentation-master/configs/pspnet/pspnet_r50-d32_rsb-pretrain_512x1024_adamw_80k_cityscapes.py | _base_ = [
'../_base_/models/pspnet_r50-d8.py', '../_base_/datasets/cityscapes.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
]
checkpoint = 'https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb256-rsb-a1-600e_in1k_20211228-20e21305.pth' # noqa
model = dict(
pr... | 867 | 32.384615 | 135 | py |
mmsegmentation | mmsegmentation-master/configs/pspnet/pspnet_r50-d8_480x480_40k_pascal_context.py | _base_ = [
'../_base_/models/pspnet_r50-d8.py',
'../_base_/datasets/pascal_context.py', '../_base_/default_runtime.py',
'../_base_/schedules/schedule_40k.py'
]
model = dict(
decode_head=dict(num_classes=60),
auxiliary_head=dict(num_classes=60),
test_cfg=dict(mode='slide', crop_size=(480, 480), s... | 413 | 36.636364 | 75 | py |
mmsegmentation | mmsegmentation-master/configs/pspnet/pspnet_r50-d8_480x480_40k_pascal_context_59.py | _base_ = [
'../_base_/models/pspnet_r50-d8.py',
'../_base_/datasets/pascal_context_59.py', '../_base_/default_runtime.py',
'../_base_/schedules/schedule_40k.py'
]
model = dict(
decode_head=dict(num_classes=59),
auxiliary_head=dict(num_classes=59),
test_cfg=dict(mode='slide', crop_size=(480, 480)... | 416 | 36.909091 | 78 | py |
mmsegmentation | mmsegmentation-master/configs/pspnet/pspnet_r50-d8_480x480_80k_pascal_context.py | _base_ = [
'../_base_/models/pspnet_r50-d8.py',
'../_base_/datasets/pascal_context.py', '../_base_/default_runtime.py',
'../_base_/schedules/schedule_80k.py'
]
model = dict(
decode_head=dict(num_classes=60),
auxiliary_head=dict(num_classes=60),
test_cfg=dict(mode='slide', crop_size=(480, 480), s... | 413 | 36.636364 | 75 | py |
mmsegmentation | mmsegmentation-master/configs/pspnet/pspnet_r50-d8_480x480_80k_pascal_context_59.py | _base_ = [
'../_base_/models/pspnet_r50-d8.py',
'../_base_/datasets/pascal_context_59.py', '../_base_/default_runtime.py',
'../_base_/schedules/schedule_80k.py'
]
model = dict(
decode_head=dict(num_classes=59),
auxiliary_head=dict(num_classes=59),
test_cfg=dict(mode='slide', crop_size=(480, 480)... | 416 | 36.909091 | 78 | py |
mmsegmentation | mmsegmentation-master/configs/pspnet/pspnet_r50-d8_4x4_512x512_80k_potsdam.py | _base_ = [
'../_base_/models/pspnet_r50-d8.py', '../_base_/datasets/potsdam.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
]
model = dict(
decode_head=dict(num_classes=6), auxiliary_head=dict(num_classes=6))
| 248 | 34.571429 | 73 | py |
mmsegmentation | mmsegmentation-master/configs/pspnet/pspnet_r50-d8_4x4_512x512_80k_vaihingen.py | _base_ = [
'../_base_/models/pspnet_r50-d8.py', '../_base_/datasets/vaihingen.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
]
model = dict(
decode_head=dict(num_classes=6), auxiliary_head=dict(num_classes=6))
| 250 | 34.857143 | 75 | py |