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/cgnet/cgnet.yml | Collections:
- Name: CGNet
Metadata:
Training Data:
- Cityscapes
Paper:
URL: https://arxiv.org/abs/1811.08201
Title: 'CGNet: A Light-weight Context Guided Network for Semantic Segmentation'
README: configs/cgnet/README.md
Code:
URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/m... | 1,774 | 28.583333 | 156 | yml |
mmsegmentation | mmsegmentation-master/configs/cgnet/cgnet_512x1024_60k_cityscapes.py | _base_ = ['../_base_/models/cgnet.py', '../_base_/default_runtime.py']
# optimizer
optimizer = dict(type='Adam', lr=0.001, eps=1e-08, 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
total_iters = 60000
checkpoin... | 2,202 | 31.880597 | 77 | py |
mmsegmentation | mmsegmentation-master/configs/cgnet/cgnet_680x680_60k_cityscapes.py | _base_ = [
'../_base_/models/cgnet.py', '../_base_/datasets/cityscapes.py',
'../_base_/default_runtime.py'
]
# optimizer
optimizer = dict(type='Adam', lr=0.001, eps=1e-08, weight_decay=0.0005)
optimizer_config = dict()
# learning policy
lr_config = dict(policy='poly', power=0.9, min_lr=1e-4, by_epoch=False)
# ... | 1,708 | 32.509804 | 77 | py |
mmsegmentation | mmsegmentation-master/configs/convnext/README.md | # ConvNeXt
[A ConvNet for the 2020s](https://arxiv.org/abs/2201.03545)
## Introduction
<!-- [BACKBONE] -->
<a href="https://github.com/facebookresearch/ConvNeXt">Official Repo</a>
<a href="https://github.com/open-mmlab/mmclassification/blob/v0.20.1/mmcls/models/backbones/convnext.py#L133">Code Snippet</a>
## Abst... | 9,593 | 130.424658 | 1,460 | md |
mmsegmentation | mmsegmentation-master/configs/convnext/convnext.yml | Models:
- Name: upernet_convnext_tiny_fp16_512x512_160k_ade20k
In Collection: UPerNet
Metadata:
backbone: ConvNeXt-T
crop size: (512,512)
lr schd: 160000
inference time (ms/im):
- value: 50.25
hardware: V100
backend: PyTorch
batch size: 1
mode: FP16
resolution: (512... | 4,474 | 32.395522 | 197 | yml |
mmsegmentation | mmsegmentation-master/configs/convnext/upernet_convnext_base_fp16_512x512_160k_ade20k.py | _base_ = [
'../_base_/models/upernet_convnext.py', '../_base_/datasets/ade20k.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py'
]
crop_size = (512, 512)
model = dict(
decode_head=dict(in_channels=[128, 256, 512, 1024], num_classes=150),
auxiliary_head=dict(in_channels=512, num_... | 1,092 | 25.658537 | 75 | py |
mmsegmentation | mmsegmentation-master/configs/convnext/upernet_convnext_base_fp16_640x640_160k_ade20k.py | _base_ = [
'../_base_/models/upernet_convnext.py',
'../_base_/datasets/ade20k_640x640.py', '../_base_/default_runtime.py',
'../_base_/schedules/schedule_160k.py'
]
crop_size = (640, 640)
checkpoint_file = 'https://download.openmmlab.com/mmclassification/v0/convnext/downstream/convnext-base_3rdparty_in21k_20... | 1,599 | 27.571429 | 149 | py |
mmsegmentation | mmsegmentation-master/configs/convnext/upernet_convnext_large_fp16_640x640_160k_ade20k.py | _base_ = [
'../_base_/models/upernet_convnext.py',
'../_base_/datasets/ade20k_640x640.py', '../_base_/default_runtime.py',
'../_base_/schedules/schedule_160k.py'
]
crop_size = (640, 640)
checkpoint_file = 'https://download.openmmlab.com/mmclassification/v0/convnext/downstream/convnext-large_3rdparty_in21k_2... | 1,601 | 27.607143 | 150 | py |
mmsegmentation | mmsegmentation-master/configs/convnext/upernet_convnext_small_fp16_512x512_160k_ade20k.py | _base_ = [
'../_base_/models/upernet_convnext.py', '../_base_/datasets/ade20k.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py'
]
crop_size = (512, 512)
checkpoint_file = 'https://download.openmmlab.com/mmclassification/v0/convnext/downstream/convnext-small_3rdparty_32xb128-noema_in1k_... | 1,600 | 28.109091 | 163 | py |
mmsegmentation | mmsegmentation-master/configs/convnext/upernet_convnext_tiny_fp16_512x512_160k_ade20k.py | _base_ = [
'../_base_/models/upernet_convnext.py', '../_base_/datasets/ade20k.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py'
]
crop_size = (512, 512)
checkpoint_file = 'https://download.openmmlab.com/mmclassification/v0/convnext/downstream/convnext-tiny_3rdparty_32xb128-noema_in1k_2... | 1,597 | 28.054545 | 162 | py |
mmsegmentation | mmsegmentation-master/configs/convnext/upernet_convnext_xlarge_fp16_640x640_160k_ade20k.py | _base_ = [
'../_base_/models/upernet_convnext.py',
'../_base_/datasets/ade20k_640x640.py', '../_base_/default_runtime.py',
'../_base_/schedules/schedule_160k.py'
]
crop_size = (640, 640)
checkpoint_file = 'https://download.openmmlab.com/mmclassification/v0/convnext/downstream/convnext-xlarge_3rdparty_in21k_... | 1,606 | 27.696429 | 151 | py |
mmsegmentation | mmsegmentation-master/configs/danet/README.md | # DANet
[Dual Attention Network for Scene Segmentation](https://arxiv.org/abs/1809.02983)
## Introduction
<!-- [ALGORITHM] -->
<a href="https://github.com/junfu1115/DANet/">Official Repo</a>
<a href="https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/da_head.py#L76">Code Snippet</a... | 14,436 | 211.308824 | 1,394 | md |
mmsegmentation | mmsegmentation-master/configs/danet/danet.yml | Collections:
- Name: DANet
Metadata:
Training Data:
- Cityscapes
- ADE20K
- Pascal VOC 2012 + Aug
Paper:
URL: https://arxiv.org/abs/1809.02983
Title: Dual Attention Network for Scene Segmentation
README: configs/danet/README.md
Code:
URL: https://github.com/open-mmlab/mmsegmentation/... | 9,890 | 31.751656 | 172 | yml |
mmsegmentation | mmsegmentation-master/configs/danet/danet_r101-d8_512x1024_40k_cityscapes.py | _base_ = './danet_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/danet/danet_r101-d8_512x1024_80k_cityscapes.py | _base_ = './danet_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/danet/danet_r101-d8_512x512_160k_ade20k.py | _base_ = './danet_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/danet/danet_r101-d8_512x512_20k_voc12aug.py | _base_ = './danet_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/danet/danet_r101-d8_512x512_40k_voc12aug.py | _base_ = './danet_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/danet/danet_r101-d8_512x512_80k_ade20k.py | _base_ = './danet_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/danet/danet_r101-d8_769x769_40k_cityscapes.py | _base_ = './danet_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/danet/danet_r101-d8_769x769_80k_cityscapes.py | _base_ = './danet_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/danet/danet_r50-d8_512x1024_40k_cityscapes.py | _base_ = [
'../_base_/models/danet_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/danet/danet_r50-d8_512x1024_80k_cityscapes.py | _base_ = [
'../_base_/models/danet_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/danet/danet_r50-d8_512x512_160k_ade20k.py | _base_ = [
'../_base_/models/danet_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/danet/danet_r50-d8_512x512_20k_voc12aug.py | _base_ = [
'../_base_/models/danet_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/danet/danet_r50-d8_512x512_40k_voc12aug.py | _base_ = [
'../_base_/models/danet_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/danet/danet_r50-d8_512x512_80k_ade20k.py | _base_ = [
'../_base_/models/danet_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/danet/danet_r50-d8_769x769_40k_cityscapes.py | _base_ = [
'../_base_/models/danet_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/danet/danet_r50-d8_769x769_80k_cityscapes.py | _base_ = [
'../_base_/models/danet_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/deeplabv3/README.md | # DeepLabV3
[Rethinking atrous convolution for semantic image segmentation](https://arxiv.org/abs/1706.05587)
## Introduction
<!-- [ALGORITHM] -->
<a href="https://github.com/tensorflow/models/tree/master/research/deeplab">Official Repo</a>
<a href="https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/m... | 36,366 | 307.194915 | 1,044 | md |
mmsegmentation | mmsegmentation-master/configs/deeplabv3/deeplabv3.yml | Collections:
- Name: DeepLabV3
Metadata:
Training Data:
- Cityscapes
- ADE20K
- Pascal VOC 2012 + Aug
- Pascal Context
- Pascal Context 59
- COCO-Stuff 10k
- COCO-Stuff 164k
Paper:
URL: https://arxiv.org/abs/1706.05587
Title: Rethinking atrous convolution for semantic image s... | 26,558 | 34.084544 | 200 | yml |
mmsegmentation | mmsegmentation-master/configs/deeplabv3/deeplabv3_r101-d16-mg124_512x1024_40k_cityscapes.py | _base_ = './deeplabv3_r50-d8_512x1024_40k_cityscapes.py'
model = dict(
pretrained='open-mmlab://resnet101_v1c',
backbone=dict(
depth=101,
dilations=(1, 1, 1, 2),
strides=(1, 2, 2, 1),
multi_grid=(1, 2, 4)),
decode_head=dict(
dilations=(1, 6, 12, 18),
sampler=d... | 368 | 29.75 | 64 | py |
mmsegmentation | mmsegmentation-master/configs/deeplabv3/deeplabv3_r101-d16-mg124_512x1024_80k_cityscapes.py | _base_ = './deeplabv3_r50-d8_512x1024_80k_cityscapes.py'
model = dict(
pretrained='open-mmlab://resnet101_v1c',
backbone=dict(
depth=101,
dilations=(1, 1, 1, 2),
strides=(1, 2, 2, 1),
multi_grid=(1, 2, 4)),
decode_head=dict(
dilations=(1, 6, 12, 18),
sampler=d... | 368 | 29.75 | 64 | py |
mmsegmentation | mmsegmentation-master/configs/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context.py | _base_ = './deeplabv3_r50-d8_480x480_40k_pascal_context.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
| 140 | 46 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context_59.py | _base_ = './deeplabv3_r50-d8_480x480_40k_pascal_context_59.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
| 143 | 47 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/deeplabv3/deeplabv3_r101-d8_480x480_80k_pascal_context.py | _base_ = './deeplabv3_r50-d8_480x480_80k_pascal_context.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
| 140 | 46 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/deeplabv3/deeplabv3_r101-d8_480x480_80k_pascal_context_59.py | _base_ = './deeplabv3_r50-d8_480x480_80k_pascal_context_59.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
| 143 | 47 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/deeplabv3/deeplabv3_r101-d8_512x1024_40k_cityscapes.py | _base_ = './deeplabv3_r50-d8_512x1024_40k_cityscapes.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
| 137 | 45 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/deeplabv3/deeplabv3_r101-d8_512x1024_80k_cityscapes.py | _base_ = './deeplabv3_r50-d8_512x1024_80k_cityscapes.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
| 137 | 45 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/deeplabv3/deeplabv3_r101-d8_512x512_160k_ade20k.py | _base_ = './deeplabv3_r50-d8_512x512_160k_ade20k.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
| 133 | 43.666667 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/deeplabv3/deeplabv3_r101-d8_512x512_20k_voc12aug.py | _base_ = './deeplabv3_r50-d8_512x512_20k_voc12aug.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
| 134 | 44 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/deeplabv3/deeplabv3_r101-d8_512x512_40k_voc12aug.py | _base_ = './deeplabv3_r50-d8_512x512_40k_voc12aug.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
| 134 | 44 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/deeplabv3/deeplabv3_r101-d8_512x512_4x4_160k_coco-stuff164k.py | _base_ = './deeplabv3_r50-d8_512x512_4x4_160k_coco-stuff164k.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
| 145 | 47.666667 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/deeplabv3/deeplabv3_r101-d8_512x512_4x4_20k_coco-stuff10k.py | _base_ = './deeplabv3_r50-d8_512x512_4x4_20k_coco-stuff10k.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
| 143 | 47 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/deeplabv3/deeplabv3_r101-d8_512x512_4x4_320k_coco-stuff164k.py | _base_ = './deeplabv3_r50-d8_512x512_4x4_320k_coco-stuff164k.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
| 145 | 47.666667 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/deeplabv3/deeplabv3_r101-d8_512x512_4x4_40k_coco-stuff10k.py | _base_ = './deeplabv3_r50-d8_512x512_4x4_40k_coco-stuff10k.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
| 143 | 47 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/deeplabv3/deeplabv3_r101-d8_512x512_4x4_80k_coco-stuff164k.py | _base_ = './deeplabv3_r50-d8_512x512_4x4_80k_coco-stuff164k.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
| 144 | 47.333333 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/deeplabv3/deeplabv3_r101-d8_512x512_80k_ade20k.py | _base_ = './deeplabv3_r50-d8_512x512_80k_ade20k.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
| 132 | 43.333333 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/deeplabv3/deeplabv3_r101-d8_769x769_40k_cityscapes.py | _base_ = './deeplabv3_r50-d8_769x769_40k_cityscapes.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
| 136 | 44.666667 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/deeplabv3/deeplabv3_r101-d8_769x769_80k_cityscapes.py | _base_ = './deeplabv3_r50-d8_769x769_80k_cityscapes.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
| 136 | 44.666667 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/deeplabv3/deeplabv3_r101-d8_fp16_512x1024_80k_cityscapes.py | _base_ = './deeplabv3_r101-d8_512x1024_80k_cityscapes.py'
# fp16 settings
optimizer_config = dict(type='Fp16OptimizerHook', loss_scale=512.)
# fp16 placeholder
fp16 = dict()
| 174 | 28.166667 | 66 | py |
mmsegmentation | mmsegmentation-master/configs/deeplabv3/deeplabv3_r101b-d8_512x1024_80k_cityscapes.py | _base_ = './deeplabv3_r50-d8_512x1024_80k_cityscapes.py'
model = dict(
pretrained='torchvision://resnet101',
backbone=dict(type='ResNet', depth=101))
| 158 | 30.8 | 56 | py |
mmsegmentation | mmsegmentation-master/configs/deeplabv3/deeplabv3_r101b-d8_769x769_80k_cityscapes.py | _base_ = './deeplabv3_r50-d8_769x769_80k_cityscapes.py'
model = dict(
pretrained='torchvision://resnet101',
backbone=dict(type='ResNet', depth=101))
| 157 | 30.6 | 55 | py |
mmsegmentation | mmsegmentation-master/configs/deeplabv3/deeplabv3_r18-d8_512x1024_80k_cityscapes.py | _base_ = './deeplabv3_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))
| 275 | 26.6 | 56 | py |
mmsegmentation | mmsegmentation-master/configs/deeplabv3/deeplabv3_r18-d8_769x769_80k_cityscapes.py | _base_ = './deeplabv3_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))
| 274 | 26.5 | 55 | py |
mmsegmentation | mmsegmentation-master/configs/deeplabv3/deeplabv3_r18b-d8_512x1024_80k_cityscapes.py | _base_ = './deeplabv3_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))
| 287 | 27.8 | 56 | py |
mmsegmentation | mmsegmentation-master/configs/deeplabv3/deeplabv3_r18b-d8_769x769_80k_cityscapes.py | _base_ = './deeplabv3_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))
| 286 | 27.7 | 55 | py |
mmsegmentation | mmsegmentation-master/configs/deeplabv3/deeplabv3_r50-d8_480x480_40k_pascal_context.py | _base_ = [
'../_base_/models/deeplabv3_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)... | 416 | 36.909091 | 75 | py |
mmsegmentation | mmsegmentation-master/configs/deeplabv3/deeplabv3_r50-d8_480x480_40k_pascal_context_59.py | _base_ = [
'../_base_/models/deeplabv3_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, 4... | 419 | 37.181818 | 78 | py |
mmsegmentation | mmsegmentation-master/configs/deeplabv3/deeplabv3_r50-d8_480x480_80k_pascal_context.py | _base_ = [
'../_base_/models/deeplabv3_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)... | 416 | 36.909091 | 75 | py |
mmsegmentation | mmsegmentation-master/configs/deeplabv3/deeplabv3_r50-d8_480x480_80k_pascal_context_59.py | _base_ = [
'../_base_/models/deeplabv3_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, 4... | 419 | 37.181818 | 78 | py |
mmsegmentation | mmsegmentation-master/configs/deeplabv3/deeplabv3_r50-d8_512x1024_40k_cityscapes.py | _base_ = [
'../_base_/models/deeplabv3_r50-d8.py', '../_base_/datasets/cityscapes.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py'
]
| 167 | 32.6 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/deeplabv3/deeplabv3_r50-d8_512x1024_80k_cityscapes.py | _base_ = [
'../_base_/models/deeplabv3_r50-d8.py', '../_base_/datasets/cityscapes.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
]
| 167 | 32.6 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/deeplabv3/deeplabv3_r50-d8_512x512_160k_ade20k.py | _base_ = [
'../_base_/models/deeplabv3_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))
| 255 | 35.571429 | 76 | py |
mmsegmentation | mmsegmentation-master/configs/deeplabv3/deeplabv3_r50-d8_512x512_20k_voc12aug.py | _base_ = [
'../_base_/models/deeplabv3_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))
| 266 | 32.375 | 77 | py |
mmsegmentation | mmsegmentation-master/configs/deeplabv3/deeplabv3_r50-d8_512x512_40k_voc12aug.py | _base_ = [
'../_base_/models/deeplabv3_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))
| 266 | 32.375 | 77 | py |
mmsegmentation | mmsegmentation-master/configs/deeplabv3/deeplabv3_r50-d8_512x512_4x4_160k_coco-stuff164k.py | _base_ = [
'../_base_/models/deeplabv3_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))
| 267 | 32.5 | 76 | py |
mmsegmentation | mmsegmentation-master/configs/deeplabv3/deeplabv3_r50-d8_512x512_4x4_20k_coco-stuff10k.py | _base_ = [
'../_base_/models/deeplabv3_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))
| 265 | 32.25 | 76 | py |
mmsegmentation | mmsegmentation-master/configs/deeplabv3/deeplabv3_r50-d8_512x512_4x4_320k_coco-stuff164k.py | _base_ = [
'../_base_/models/deeplabv3_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))
| 267 | 32.5 | 76 | py |
mmsegmentation | mmsegmentation-master/configs/deeplabv3/deeplabv3_r50-d8_512x512_4x4_40k_coco-stuff10k.py | _base_ = [
'../_base_/models/deeplabv3_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))
| 265 | 32.25 | 76 | py |
mmsegmentation | mmsegmentation-master/configs/deeplabv3/deeplabv3_r50-d8_512x512_4x4_80k_coco-stuff164k.py | _base_ = [
'../_base_/models/deeplabv3_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))
| 266 | 32.375 | 76 | py |
mmsegmentation | mmsegmentation-master/configs/deeplabv3/deeplabv3_r50-d8_512x512_80k_ade20k.py | _base_ = [
'../_base_/models/deeplabv3_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))
| 254 | 35.428571 | 76 | py |
mmsegmentation | mmsegmentation-master/configs/deeplabv3/deeplabv3_r50-d8_769x769_40k_cityscapes.py | _base_ = [
'../_base_/models/deeplabv3_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_siz... | 354 | 34.5 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/deeplabv3/deeplabv3_r50-d8_769x769_80k_cityscapes.py | _base_ = [
'../_base_/models/deeplabv3_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_siz... | 354 | 34.5 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/deeplabv3/deeplabv3_r50b-d8_512x1024_80k_cityscapes.py | _base_ = './deeplabv3_r50-d8_512x1024_80k_cityscapes.py'
model = dict(pretrained='torchvision://resnet50', backbone=dict(type='ResNet'))
| 137 | 45 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/deeplabv3/deeplabv3_r50b-d8_769x769_80k_cityscapes.py | _base_ = './deeplabv3_r50-d8_769x769_80k_cityscapes.py'
model = dict(pretrained='torchvision://resnet50', backbone=dict(type='ResNet'))
| 136 | 44.666667 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/deeplabv3plus/README.md | # DeepLabV3+
[Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://arxiv.org/abs/1802.02611)
## Introduction
<!-- [ALGORITHM] -->
<a href="https://github.com/tensorflow/models/tree/master/research/deeplab">Official Repo</a>
<a href="https://github.com/open-mmlab/mmsegmentation... | 41,557 | 311.466165 | 1,275 | md |
mmsegmentation | mmsegmentation-master/configs/deeplabv3plus/deeplabv3plus.yml | Collections:
- Name: DeepLabV3+
Metadata:
Training Data:
- Cityscapes
- ADE20K
- Pascal VOC 2012 + Aug
- Pascal Context
- Pascal Context 59
- LoveDA
- Potsdam
- Vaihingen
- iSAID
Paper:
URL: https://arxiv.org/abs/1802.02611
Title: Encoder-Decoder with Atrous Separable... | 29,788 | 34.0047 | 210 | yml |
mmsegmentation | mmsegmentation-master/configs/deeplabv3plus/deeplabv3plus_r101-d16-mg124_512x1024_40k_cityscapes.py | _base_ = './deeplabv3plus_r50-d8_512x1024_40k_cityscapes.py'
model = dict(
pretrained='open-mmlab://resnet101_v1c',
backbone=dict(
depth=101,
dilations=(1, 1, 1, 2),
strides=(1, 2, 2, 1),
multi_grid=(1, 2, 4)),
decode_head=dict(
dilations=(1, 6, 12, 18),
sampl... | 372 | 30.083333 | 64 | py |
mmsegmentation | mmsegmentation-master/configs/deeplabv3plus/deeplabv3plus_r101-d16-mg124_512x1024_80k_cityscapes.py | _base_ = './deeplabv3plus_r50-d8_512x1024_80k_cityscapes.py'
model = dict(
pretrained='open-mmlab://resnet101_v1c',
backbone=dict(
depth=101,
dilations=(1, 1, 1, 2),
strides=(1, 2, 2, 1),
multi_grid=(1, 2, 4)),
decode_head=dict(
dilations=(1, 6, 12, 18),
sampl... | 372 | 30.083333 | 64 | py |
mmsegmentation | mmsegmentation-master/configs/deeplabv3plus/deeplabv3plus_r101-d8_480x480_40k_pascal_context.py | _base_ = './deeplabv3plus_r50-d8_480x480_40k_pascal_context.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
| 144 | 47.333333 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/deeplabv3plus/deeplabv3plus_r101-d8_480x480_40k_pascal_context_59.py | _base_ = './deeplabv3plus_r50-d8_480x480_40k_pascal_context_59.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
| 147 | 48.333333 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/deeplabv3plus/deeplabv3plus_r101-d8_480x480_80k_pascal_context.py | _base_ = './deeplabv3plus_r50-d8_480x480_80k_pascal_context.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
| 144 | 47.333333 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/deeplabv3plus/deeplabv3plus_r101-d8_480x480_80k_pascal_context_59.py | _base_ = './deeplabv3plus_r50-d8_480x480_80k_pascal_context_59.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
| 147 | 48.333333 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/deeplabv3plus/deeplabv3plus_r101-d8_4x4_512x512_80k_vaihingen.py | _base_ = './deeplabv3plus_r50-d8_4x4_512x512_80k_vaihingen.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
| 143 | 47 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/deeplabv3plus/deeplabv3plus_r101-d8_512x1024_40k_cityscapes.py | _base_ = './deeplabv3plus_r50-d8_512x1024_40k_cityscapes.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
| 141 | 46.333333 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/deeplabv3plus/deeplabv3plus_r101-d8_512x1024_80k_cityscapes.py | _base_ = './deeplabv3plus_r50-d8_512x1024_80k_cityscapes.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
| 141 | 46.333333 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/deeplabv3plus/deeplabv3plus_r101-d8_512x512_160k_ade20k.py | _base_ = './deeplabv3plus_r50-d8_512x512_160k_ade20k.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
| 137 | 45 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/deeplabv3plus/deeplabv3plus_r101-d8_512x512_20k_voc12aug.py | _base_ = './deeplabv3plus_r50-d8_512x512_20k_voc12aug.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
| 138 | 45.333333 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/deeplabv3plus/deeplabv3plus_r101-d8_512x512_40k_voc12aug.py | _base_ = './deeplabv3plus_r50-d8_512x512_40k_voc12aug.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
| 138 | 45.333333 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/deeplabv3plus/deeplabv3plus_r101-d8_512x512_80k_ade20k.py | _base_ = './deeplabv3plus_r50-d8_512x512_80k_ade20k.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
| 136 | 44.666667 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/deeplabv3plus/deeplabv3plus_r101-d8_512x512_80k_loveda.py | _base_ = './deeplabv3plus_r50-d8_512x512_80k_loveda.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://resnet101_v1c')))
| 205 | 28.428571 | 73 | py |
mmsegmentation | mmsegmentation-master/configs/deeplabv3plus/deeplabv3plus_r101-d8_512x512_80k_potsdam.py | _base_ = './deeplabv3plus_r50-d8_512x512_80k_potsdam.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
| 137 | 45 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/deeplabv3plus/deeplabv3plus_r101-d8_769x769_40k_cityscapes.py | _base_ = './deeplabv3plus_r50-d8_769x769_40k_cityscapes.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
| 140 | 46 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/deeplabv3plus/deeplabv3plus_r101-d8_769x769_80k_cityscapes.py | _base_ = './deeplabv3plus_r50-d8_769x769_80k_cityscapes.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
| 140 | 46 | 79 | py |
mmsegmentation | mmsegmentation-master/configs/deeplabv3plus/deeplabv3plus_r101-d8_fp16_512x1024_80k_cityscapes.py | _base_ = './deeplabv3plus_r101-d8_512x1024_80k_cityscapes.py'
# fp16 settings
optimizer_config = dict(type='Fp16OptimizerHook', loss_scale=512.)
# fp16 placeholder
fp16 = dict()
| 178 | 28.833333 | 66 | py |
mmsegmentation | mmsegmentation-master/configs/deeplabv3plus/deeplabv3plus_r101_512x512_C-CM+C-WO-NatOcc-SOT.py | # +
_base_ = '../_base_/datasets/occlude_face.py'
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='EncoderDecoder',
pretrained='open-mmlab://resnet101_v1c',
backbone=dict(
type='ResNetV1c',
depth=101,
num_stages=4,
out_indices=(0, 1, 2, 3),
dilat... | 2,122 | 32.171875 | 75 | py |
mmsegmentation | mmsegmentation-master/configs/deeplabv3plus/deeplabv3plus_r101b-d8_512x1024_80k_cityscapes.py | _base_ = './deeplabv3plus_r50-d8_512x1024_80k_cityscapes.py'
model = dict(
pretrained='torchvision://resnet101',
backbone=dict(type='ResNet', depth=101))
| 162 | 31.6 | 60 | py |
mmsegmentation | mmsegmentation-master/configs/deeplabv3plus/deeplabv3plus_r101b-d8_769x769_80k_cityscapes.py | _base_ = './deeplabv3plus_r50-d8_769x769_80k_cityscapes.py'
model = dict(
pretrained='torchvision://resnet101',
backbone=dict(type='ResNet', depth=101))
| 161 | 31.4 | 59 | py |
mmsegmentation | mmsegmentation-master/configs/deeplabv3plus/deeplabv3plus_r18-d8_4x4_512x512_80k_vaihingen.py | _base_ = './deeplabv3plus_r50-d8_4x4_512x512_80k_vaihingen.py'
model = dict(
pretrained='open-mmlab://resnet18_v1c',
backbone=dict(depth=18),
decode_head=dict(
c1_in_channels=64,
c1_channels=12,
in_channels=512,
channels=128,
),
auxiliary_head=dict(in_channels=256, ch... | 332 | 26.75 | 62 | py |