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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/mmseg/models/backbones/cgnet.py#L187
Version: v0.17.0
Converted From:
Code: https://github.com/wutianyiRosun/CGNet
Models:
- Name: cgnet_680x680_60k_cityscapes
In Collection: CGNet
Metadata:
backbone: M3N21
crop size: (680,680)
lr schd: 60000
inference time (ms/im):
- value: 32.78
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (680,680)
Training Memory (GB): 7.5
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 65.63
mIoU(ms+flip): 68.04
Config: configs/cgnet/cgnet_680x680_60k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/cgnet/cgnet_680x680_60k_cityscapes/cgnet_680x680_60k_cityscapes_20201101_110253-4c0b2f2d.pth
- Name: cgnet_512x1024_60k_cityscapes
In Collection: CGNet
Metadata:
backbone: M3N21
crop size: (512,1024)
lr schd: 60000
inference time (ms/im):
- value: 32.11
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,1024)
Training Memory (GB): 8.3
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 68.27
mIoU(ms+flip): 70.33
Config: configs/cgnet/cgnet_512x1024_60k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/cgnet/cgnet_512x1024_60k_cityscapes/cgnet_512x1024_60k_cityscapes_20201101_110254-124ea03b.pth
| 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
checkpoint_config = dict(by_epoch=False, interval=4000)
evaluation = dict(interval=4000, metric='mIoU')
# dataset settings
dataset_type = 'CityscapesDataset'
data_root = 'data/cityscapes/'
img_norm_cfg = dict(
mean=[72.39239876, 82.90891754, 73.15835921], std=[1, 1, 1], to_rgb=True)
crop_size = (512, 1024)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations'),
dict(type='Resize', img_scale=(2048, 1024), ratio_range=(0.5, 2.0)),
dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
dict(type='RandomFlip', flip_ratio=0.5),
dict(type='PhotoMetricDistortion'),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size=crop_size, pad_val=0, seg_pad_val=255),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_semantic_seg']),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(2048, 1024),
# img_ratios=[0.5, 0.75, 1.0, 1.25, 1.5, 1.75],
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(type='Normalize', **img_norm_cfg),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img']),
])
]
data = dict(
samples_per_gpu=8,
workers_per_gpu=4,
train=dict(
type=dataset_type,
data_root=data_root,
img_dir='leftImg8bit/train',
ann_dir='gtFine/train',
pipeline=train_pipeline),
val=dict(
type=dataset_type,
data_root=data_root,
img_dir='leftImg8bit/val',
ann_dir='gtFine/val',
pipeline=test_pipeline),
test=dict(
type=dataset_type,
data_root=data_root,
img_dir='leftImg8bit/val',
ann_dir='gtFine/val',
pipeline=test_pipeline))
| 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)
# runtime settings
total_iters = 60000
checkpoint_config = dict(by_epoch=False, interval=4000)
evaluation = dict(interval=4000, metric='mIoU')
img_norm_cfg = dict(
mean=[72.39239876, 82.90891754, 73.15835921], std=[1, 1, 1], to_rgb=True)
crop_size = (680, 680)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations'),
dict(type='Resize', img_scale=(2048, 1024), ratio_range=(0.5, 2.0)),
dict(type='RandomCrop', crop_size=crop_size),
dict(type='RandomFlip', flip_ratio=0.5),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size=crop_size, pad_val=0, seg_pad_val=255),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_semantic_seg']),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(2048, 1024),
# img_ratios=[0.5, 0.75, 1.0, 1.25, 1.5, 1.75],
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(type='Normalize', **img_norm_cfg),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img']),
])
]
data = dict(
samples_per_gpu=8,
workers_per_gpu=4,
train=dict(pipeline=train_pipeline),
val=dict(pipeline=test_pipeline),
test=dict(pipeline=test_pipeline))
| 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>
## Abstract
<!-- [ABSTRACT] -->
The "Roaring 20s" of visual recognition began with the introduction of Vision Transformers (ViTs), which quickly superseded ConvNets as the state-of-the-art image classification model. A vanilla ViT, on the other hand, faces difficulties when applied to general computer vision tasks such as object detection and semantic segmentation. It is the hierarchical Transformers (e.g., Swin Transformers) that reintroduced several ConvNet priors, making Transformers practically viable as a generic vision backbone and demonstrating remarkable performance on a wide variety of vision tasks. However, the effectiveness of such hybrid approaches is still largely credited to the intrinsic superiority of Transformers, rather than the inherent inductive biases of convolutions. In this work, we reexamine the design spaces and test the limits of what a pure ConvNet can achieve. We gradually "modernize" a standard ResNet toward the design of a vision Transformer, and discover several key components that contribute to the performance difference along the way. The outcome of this exploration is a family of pure ConvNet models dubbed ConvNeXt. Constructed entirely from standard ConvNet modules, ConvNeXts compete favorably with Transformers in terms of accuracy and scalability, achieving 87.8% ImageNet top-1 accuracy and outperforming Swin Transformers on COCO detection and ADE20K segmentation, while maintaining the simplicity and efficiency of standard ConvNets.
<!-- [IMAGE] -->
<div align=center>
<img src="https://user-images.githubusercontent.com/8370623/148624004-e9581042-ea4d-4e10-b3bd-42c92b02053b.png" width="90%"/>
</div>
```bibtex
@article{liu2022convnet,
title={A ConvNet for the 2020s},
author={Liu, Zhuang and Mao, Hanzi and Wu, Chao-Yuan and Feichtenhofer, Christoph and Darrell, Trevor and Xie, Saining},
journal={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
year={2022}
}
```
### Usage
- ConvNeXt backbone needs to install [MMClassification](https://github.com/open-mmlab/mmclassification) first, which has abundant backbones for downstream tasks.
```shell
pip install mmcls>=0.20.1
```
### Pre-trained Models
The pre-trained models on ImageNet-1k or ImageNet-21k are used to fine-tune on the downstream tasks.
| Model | Training Data | Params(M) | Flops(G) | Download |
| :-----------: | :-----------: | :-------: | :------: | :----------------------------------------------------------------------------------------------------------------------------------------------: |
| ConvNeXt-T\* | ImageNet-1k | 28.59 | 4.46 | [model](https://download.openmmlab.com/mmclassification/v0/convnext/downstream/convnext-tiny_3rdparty_32xb128-noema_in1k_20220301-795e9634.pth) |
| ConvNeXt-S\* | ImageNet-1k | 50.22 | 8.69 | [model](https://download.openmmlab.com/mmclassification/v0/convnext/downstream/convnext-small_3rdparty_32xb128-noema_in1k_20220301-303e75e3.pth) |
| ConvNeXt-B\* | ImageNet-1k | 88.59 | 15.36 | [model](https://download.openmmlab.com/mmclassification/v0/convnext/downstream/convnext-base_3rdparty_32xb128-noema_in1k_20220301-2a0ee547.pth) |
| ConvNeXt-B\* | ImageNet-21k | 88.59 | 15.36 | [model](https://download.openmmlab.com/mmclassification/v0/convnext/downstream/convnext-base_3rdparty_in21k_20220301-262fd037.pth) |
| ConvNeXt-L\* | ImageNet-21k | 197.77 | 34.37 | [model](https://download.openmmlab.com/mmclassification/v0/convnext/downstream/convnext-large_3rdparty_in21k_20220301-e6e0ea0a.pth) |
| ConvNeXt-XL\* | ImageNet-21k | 350.20 | 60.93 | [model](https://download.openmmlab.com/mmclassification/v0/convnext/downstream/convnext-xlarge_3rdparty_in21k_20220301-08aa5ddc.pth) |
*Models with* are converted from the [official repo](https://github.com/facebookresearch/ConvNeXt/tree/main/semantic_segmentation#results-and-fine-tuned-models).\*
## Results and models
### ADE20K
| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download |
| ------- | ----------- | --------- | ------- | -------- | -------------- | ----- | ------------- | --------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| UPerNet | ConvNeXt-T | 512x512 | 160000 | 4.23 | 19.90 | 46.11 | 46.62 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/convnext/upernet_convnext_tiny_fp16_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_tiny_fp16_512x512_160k_ade20k/upernet_convnext_tiny_fp16_512x512_160k_ade20k_20220227_124553-cad485de.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_tiny_fp16_512x512_160k_ade20k/upernet_convnext_tiny_fp16_512x512_160k_ade20k_20220227_124553.log.json) |
| UPerNet | ConvNeXt-S | 512x512 | 160000 | 5.16 | 15.18 | 48.56 | 49.02 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/convnext/upernet_convnext_small_fp16_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_small_fp16_512x512_160k_ade20k/upernet_convnext_small_fp16_512x512_160k_ade20k_20220227_131208-1b1e394f.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_small_fp16_512x512_160k_ade20k/upernet_convnext_small_fp16_512x512_160k_ade20k_20220227_131208.log.json) |
| UPerNet | ConvNeXt-B | 512x512 | 160000 | 6.33 | 14.41 | 48.71 | 49.54 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/convnext/upernet_convnext_base_fp16_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_base_fp16_512x512_160k_ade20k/upernet_convnext_base_fp16_512x512_160k_ade20k_20220227_181227-02a24fc6.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_base_fp16_512x512_160k_ade20k/upernet_convnext_base_fp16_512x512_160k_ade20k_20220227_181227.log.json) |
| UPerNet | ConvNeXt-B | 640x640 | 160000 | 8.53 | 10.88 | 52.13 | 52.66 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/convnext/upernet_convnext_base_fp16_640x640_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_base_fp16_640x640_160k_ade20k/upernet_convnext_base_fp16_640x640_160k_ade20k_20220227_182859-9280e39b.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_base_fp16_640x640_160k_ade20k/upernet_convnext_base_fp16_640x640_160k_ade20k_20220227_182859.log.json) |
| UPerNet | ConvNeXt-L | 640x640 | 160000 | 12.08 | 7.69 | 53.16 | 53.38 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/convnext/upernet_convnext_large_fp16_640x640_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_large_fp16_640x640_160k_ade20k/upernet_convnext_large_fp16_640x640_160k_ade20k_20220226_040532-e57aa54d.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_large_fp16_640x640_160k_ade20k/upernet_convnext_large_fp16_640x640_160k_ade20k_20220226_040532.log.json) |
| UPerNet | ConvNeXt-XL | 640x640 | 160000 | 26.16\* | 6.33 | 53.58 | 54.11 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/convnext/upernet_convnext_xlarge_fp16_640x640_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_xlarge_fp16_640x640_160k_ade20k/upernet_convnext_xlarge_fp16_640x640_160k_ade20k_20220226_080344-95fc38c2.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_xlarge_fp16_640x640_160k_ade20k/upernet_convnext_xlarge_fp16_640x640_160k_ade20k_20220226_080344.log.json) |
Note:
- `Mem (GB)` with * is collected when `cudnn_benchmark=True`, and hardware is V100.
| 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: (512,512)
Training Memory (GB): 4.23
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 46.11
mIoU(ms+flip): 46.62
Config: configs/convnext/upernet_convnext_tiny_fp16_512x512_160k_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_tiny_fp16_512x512_160k_ade20k/upernet_convnext_tiny_fp16_512x512_160k_ade20k_20220227_124553-cad485de.pth
- Name: upernet_convnext_small_fp16_512x512_160k_ade20k
In Collection: UPerNet
Metadata:
backbone: ConvNeXt-S
crop size: (512,512)
lr schd: 160000
inference time (ms/im):
- value: 65.88
hardware: V100
backend: PyTorch
batch size: 1
mode: FP16
resolution: (512,512)
Training Memory (GB): 5.16
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 48.56
mIoU(ms+flip): 49.02
Config: configs/convnext/upernet_convnext_small_fp16_512x512_160k_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_small_fp16_512x512_160k_ade20k/upernet_convnext_small_fp16_512x512_160k_ade20k_20220227_131208-1b1e394f.pth
- Name: upernet_convnext_base_fp16_512x512_160k_ade20k
In Collection: UPerNet
Metadata:
backbone: ConvNeXt-B
crop size: (512,512)
lr schd: 160000
inference time (ms/im):
- value: 69.4
hardware: V100
backend: PyTorch
batch size: 1
mode: FP16
resolution: (512,512)
Training Memory (GB): 6.33
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 48.71
mIoU(ms+flip): 49.54
Config: configs/convnext/upernet_convnext_base_fp16_512x512_160k_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_base_fp16_512x512_160k_ade20k/upernet_convnext_base_fp16_512x512_160k_ade20k_20220227_181227-02a24fc6.pth
- Name: upernet_convnext_base_fp16_640x640_160k_ade20k
In Collection: UPerNet
Metadata:
backbone: ConvNeXt-B
crop size: (640,640)
lr schd: 160000
inference time (ms/im):
- value: 91.91
hardware: V100
backend: PyTorch
batch size: 1
mode: FP16
resolution: (640,640)
Training Memory (GB): 8.53
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 52.13
mIoU(ms+flip): 52.66
Config: configs/convnext/upernet_convnext_base_fp16_640x640_160k_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_base_fp16_640x640_160k_ade20k/upernet_convnext_base_fp16_640x640_160k_ade20k_20220227_182859-9280e39b.pth
- Name: upernet_convnext_large_fp16_640x640_160k_ade20k
In Collection: UPerNet
Metadata:
backbone: ConvNeXt-L
crop size: (640,640)
lr schd: 160000
inference time (ms/im):
- value: 130.04
hardware: V100
backend: PyTorch
batch size: 1
mode: FP16
resolution: (640,640)
Training Memory (GB): 12.08
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 53.16
mIoU(ms+flip): 53.38
Config: configs/convnext/upernet_convnext_large_fp16_640x640_160k_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_large_fp16_640x640_160k_ade20k/upernet_convnext_large_fp16_640x640_160k_ade20k_20220226_040532-e57aa54d.pth
- Name: upernet_convnext_xlarge_fp16_640x640_160k_ade20k
In Collection: UPerNet
Metadata:
backbone: ConvNeXt-XL
crop size: (640,640)
lr schd: 160000
inference time (ms/im):
- value: 157.98
hardware: V100
backend: PyTorch
batch size: 1
mode: FP16
resolution: (640,640)
Training Memory (GB): 26.16
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 53.58
mIoU(ms+flip): 54.11
Config: configs/convnext/upernet_convnext_xlarge_fp16_640x640_160k_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_xlarge_fp16_640x640_160k_ade20k/upernet_convnext_xlarge_fp16_640x640_160k_ade20k_20220226_080344-95fc38c2.pth
| 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_classes=150),
test_cfg=dict(mode='slide', crop_size=crop_size, stride=(341, 341)),
)
optimizer = dict(
constructor='LearningRateDecayOptimizerConstructor',
_delete_=True,
type='AdamW',
lr=0.0001,
betas=(0.9, 0.999),
weight_decay=0.05,
paramwise_cfg={
'decay_rate': 0.9,
'decay_type': 'stage_wise',
'num_layers': 12
})
lr_config = dict(
_delete_=True,
policy='poly',
warmup='linear',
warmup_iters=1500,
warmup_ratio=1e-6,
power=1.0,
min_lr=0.0,
by_epoch=False)
# By default, models are trained on 8 GPUs with 2 images per GPU
data = dict(samples_per_gpu=2)
# fp16 settings
optimizer_config = dict(type='Fp16OptimizerHook', loss_scale='dynamic')
# fp16 placeholder
fp16 = dict()
| 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_20220301-262fd037.pth' # noqa
model = dict(
backbone=dict(
type='mmcls.ConvNeXt',
arch='base',
out_indices=[0, 1, 2, 3],
drop_path_rate=0.4,
layer_scale_init_value=1.0,
gap_before_final_norm=False,
init_cfg=dict(
type='Pretrained', checkpoint=checkpoint_file,
prefix='backbone.')),
decode_head=dict(
in_channels=[128, 256, 512, 1024],
num_classes=150,
),
auxiliary_head=dict(in_channels=512, num_classes=150),
test_cfg=dict(mode='slide', crop_size=crop_size, stride=(426, 426)),
)
optimizer = dict(
constructor='LearningRateDecayOptimizerConstructor',
_delete_=True,
type='AdamW',
lr=0.0001,
betas=(0.9, 0.999),
weight_decay=0.05,
paramwise_cfg={
'decay_rate': 0.9,
'decay_type': 'stage_wise',
'num_layers': 12
})
lr_config = dict(
_delete_=True,
policy='poly',
warmup='linear',
warmup_iters=1500,
warmup_ratio=1e-6,
power=1.0,
min_lr=0.0,
by_epoch=False)
# By default, models are trained on 8 GPUs with 2 images per GPU
data = dict(samples_per_gpu=2)
# fp16 settings
optimizer_config = dict(type='Fp16OptimizerHook', loss_scale='dynamic')
# fp16 placeholder
fp16 = dict()
| 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_20220301-e6e0ea0a.pth' # noqa
model = dict(
backbone=dict(
type='mmcls.ConvNeXt',
arch='large',
out_indices=[0, 1, 2, 3],
drop_path_rate=0.4,
layer_scale_init_value=1.0,
gap_before_final_norm=False,
init_cfg=dict(
type='Pretrained', checkpoint=checkpoint_file,
prefix='backbone.')),
decode_head=dict(
in_channels=[192, 384, 768, 1536],
num_classes=150,
),
auxiliary_head=dict(in_channels=768, num_classes=150),
test_cfg=dict(mode='slide', crop_size=crop_size, stride=(426, 426)),
)
optimizer = dict(
constructor='LearningRateDecayOptimizerConstructor',
_delete_=True,
type='AdamW',
lr=0.0001,
betas=(0.9, 0.999),
weight_decay=0.05,
paramwise_cfg={
'decay_rate': 0.9,
'decay_type': 'stage_wise',
'num_layers': 12
})
lr_config = dict(
_delete_=True,
policy='poly',
warmup='linear',
warmup_iters=1500,
warmup_ratio=1e-6,
power=1.0,
min_lr=0.0,
by_epoch=False)
# By default, models are trained on 8 GPUs with 2 images per GPU
data = dict(samples_per_gpu=2)
# fp16 settings
optimizer_config = dict(type='Fp16OptimizerHook', loss_scale='dynamic')
# fp16 placeholder
fp16 = dict()
| 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_20220301-303e75e3.pth' # noqa
model = dict(
backbone=dict(
type='mmcls.ConvNeXt',
arch='small',
out_indices=[0, 1, 2, 3],
drop_path_rate=0.3,
layer_scale_init_value=1.0,
gap_before_final_norm=False,
init_cfg=dict(
type='Pretrained', checkpoint=checkpoint_file,
prefix='backbone.')),
decode_head=dict(
in_channels=[96, 192, 384, 768],
num_classes=150,
),
auxiliary_head=dict(in_channels=384, num_classes=150),
test_cfg=dict(mode='slide', crop_size=crop_size, stride=(341, 341)),
)
optimizer = dict(
constructor='LearningRateDecayOptimizerConstructor',
_delete_=True,
type='AdamW',
lr=0.0001,
betas=(0.9, 0.999),
weight_decay=0.05,
paramwise_cfg={
'decay_rate': 0.9,
'decay_type': 'stage_wise',
'num_layers': 12
})
lr_config = dict(
_delete_=True,
policy='poly',
warmup='linear',
warmup_iters=1500,
warmup_ratio=1e-6,
power=1.0,
min_lr=0.0,
by_epoch=False)
# By default, models are trained on 8 GPUs with 2 images per GPU
data = dict(samples_per_gpu=2)
# fp16 settings
optimizer_config = dict(type='Fp16OptimizerHook', loss_scale='dynamic')
# fp16 placeholder
fp16 = dict()
| 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_20220301-795e9634.pth' # noqa
model = dict(
backbone=dict(
type='mmcls.ConvNeXt',
arch='tiny',
out_indices=[0, 1, 2, 3],
drop_path_rate=0.4,
layer_scale_init_value=1.0,
gap_before_final_norm=False,
init_cfg=dict(
type='Pretrained', checkpoint=checkpoint_file,
prefix='backbone.')),
decode_head=dict(
in_channels=[96, 192, 384, 768],
num_classes=150,
),
auxiliary_head=dict(in_channels=384, num_classes=150),
test_cfg=dict(mode='slide', crop_size=crop_size, stride=(341, 341)),
)
optimizer = dict(
constructor='LearningRateDecayOptimizerConstructor',
_delete_=True,
type='AdamW',
lr=0.0001,
betas=(0.9, 0.999),
weight_decay=0.05,
paramwise_cfg={
'decay_rate': 0.9,
'decay_type': 'stage_wise',
'num_layers': 6
})
lr_config = dict(
_delete_=True,
policy='poly',
warmup='linear',
warmup_iters=1500,
warmup_ratio=1e-6,
power=1.0,
min_lr=0.0,
by_epoch=False)
# By default, models are trained on 8 GPUs with 2 images per GPU
data = dict(samples_per_gpu=2)
# fp16 settings
optimizer_config = dict(type='Fp16OptimizerHook', loss_scale='dynamic')
# fp16 placeholder
fp16 = dict()
| 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_20220301-08aa5ddc.pth' # noqa
model = dict(
backbone=dict(
type='mmcls.ConvNeXt',
arch='xlarge',
out_indices=[0, 1, 2, 3],
drop_path_rate=0.4,
layer_scale_init_value=1.0,
gap_before_final_norm=False,
init_cfg=dict(
type='Pretrained', checkpoint=checkpoint_file,
prefix='backbone.')),
decode_head=dict(
in_channels=[256, 512, 1024, 2048],
num_classes=150,
),
auxiliary_head=dict(in_channels=1024, num_classes=150),
test_cfg=dict(mode='slide', crop_size=crop_size, stride=(426, 426)),
)
optimizer = dict(
constructor='LearningRateDecayOptimizerConstructor',
_delete_=True,
type='AdamW',
lr=0.00008,
betas=(0.9, 0.999),
weight_decay=0.05,
paramwise_cfg={
'decay_rate': 0.9,
'decay_type': 'stage_wise',
'num_layers': 12
})
lr_config = dict(
_delete_=True,
policy='poly',
warmup='linear',
warmup_iters=1500,
warmup_ratio=1e-6,
power=1.0,
min_lr=0.0,
by_epoch=False)
# By default, models are trained on 8 GPUs with 2 images per GPU
data = dict(samples_per_gpu=2)
# fp16 settings
optimizer_config = dict(type='Fp16OptimizerHook', loss_scale='dynamic')
# fp16 placeholder
fp16 = dict()
| 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>
## Abstract
<!-- [ABSTRACT] -->
In this paper, we address the scene segmentation task by capturing rich contextual dependencies based on the selfattention mechanism. Unlike previous works that capture contexts by multi-scale features fusion, we propose a Dual Attention Networks (DANet) to adaptively integrate local features with their global dependencies. Specifically, we append two types of attention modules on top of traditional dilated FCN, which model the semantic interdependencies in spatial and channel dimensions respectively. The position attention module selectively aggregates the features at each position by a weighted sum of the features at all positions. Similar features would be related to each other regardless of their distances. Meanwhile, the channel attention module selectively emphasizes interdependent channel maps by integrating associated features among all channel maps. We sum the outputs of the two attention modules to further improve feature representation which contributes to more precise segmentation results. We achieve new state-of-the-art segmentation performance on three challenging scene segmentation datasets, i.e., Cityscapes, PASCAL Context and COCO Stuff dataset. In particular, a Mean IoU score of 81.5% on Cityscapes test set is achieved without using coarse data. We make the code and trained model publicly available at [this https URL](https://github.com/junfu1115/DANet).
<!-- [IMAGE] -->
<div align=center>
<img src="https://user-images.githubusercontent.com/24582831/142900467-f832fdb9-3b7d-47d3-8e80-e6ee9303bdfb.png" width="70%"/>
</div>
## Citation
```bibtex
@article{fu2018dual,
title={Dual Attention Network for Scene Segmentation},
author={Jun Fu, Jing Liu, Haijie Tian, Yong Li, Yongjun Bao, Zhiwei Fang,and Hanqing Lu},
booktitle={The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
year={2019}
}
```
## Results and models
### Cityscapes
| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download |
| ------ | -------- | --------- | ------: | -------- | -------------- | ----: | ------------- | ------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| DANet | R-50-D8 | 512x1024 | 40000 | 7.4 | 2.66 | 78.74 | - | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/danet/danet_r50-d8_512x1024_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x1024_40k_cityscapes/danet_r50-d8_512x1024_40k_cityscapes_20200605_191324-c0dbfa5f.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x1024_40k_cityscapes/danet_r50-d8_512x1024_40k_cityscapes_20200605_191324.log.json) |
| DANet | R-101-D8 | 512x1024 | 40000 | 10.9 | 1.99 | 80.52 | - | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/danet/danet_r101-d8_512x1024_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x1024_40k_cityscapes/danet_r101-d8_512x1024_40k_cityscapes_20200605_200831-c57a7157.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x1024_40k_cityscapes/danet_r101-d8_512x1024_40k_cityscapes_20200605_200831.log.json) |
| DANet | R-50-D8 | 769x769 | 40000 | 8.8 | 1.56 | 78.88 | 80.62 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/danet/danet_r50-d8_769x769_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_769x769_40k_cityscapes/danet_r50-d8_769x769_40k_cityscapes_20200530_025703-76681c60.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_769x769_40k_cityscapes/danet_r50-d8_769x769_40k_cityscapes_20200530_025703.log.json) |
| DANet | R-101-D8 | 769x769 | 40000 | 12.8 | 1.07 | 79.88 | 81.47 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/danet/danet_r101-d8_769x769_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_769x769_40k_cityscapes/danet_r101-d8_769x769_40k_cityscapes_20200530_025717-dcb7fd4e.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_769x769_40k_cityscapes/danet_r101-d8_769x769_40k_cityscapes_20200530_025717.log.json) |
| DANet | R-50-D8 | 512x1024 | 80000 | - | - | 79.34 | - | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/danet/danet_r50-d8_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x1024_80k_cityscapes/danet_r50-d8_512x1024_80k_cityscapes_20200607_133029-2bfa2293.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x1024_80k_cityscapes/danet_r50-d8_512x1024_80k_cityscapes_20200607_133029.log.json) |
| DANet | R-101-D8 | 512x1024 | 80000 | - | - | 80.41 | - | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/danet/danet_r101-d8_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x1024_80k_cityscapes/danet_r101-d8_512x1024_80k_cityscapes_20200607_132918-955e6350.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x1024_80k_cityscapes/danet_r101-d8_512x1024_80k_cityscapes_20200607_132918.log.json) |
| DANet | R-50-D8 | 769x769 | 80000 | - | - | 79.27 | 80.96 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/danet/danet_r50-d8_769x769_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_769x769_80k_cityscapes/danet_r50-d8_769x769_80k_cityscapes_20200607_132954-495689b4.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_769x769_80k_cityscapes/danet_r50-d8_769x769_80k_cityscapes_20200607_132954.log.json) |
| DANet | R-101-D8 | 769x769 | 80000 | - | - | 80.47 | 82.02 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/danet/danet_r101-d8_769x769_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_769x769_80k_cityscapes/danet_r101-d8_769x769_80k_cityscapes_20200607_132918-f3a929e7.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_769x769_80k_cityscapes/danet_r101-d8_769x769_80k_cityscapes_20200607_132918.log.json) |
### ADE20K
| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download |
| ------ | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | --------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| DANet | R-50-D8 | 512x512 | 80000 | 11.5 | 21.20 | 41.66 | 42.90 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/danet/danet_r50-d8_512x512_80k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_80k_ade20k/danet_r50-d8_512x512_80k_ade20k_20200615_015125-edb18e08.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_80k_ade20k/danet_r50-d8_512x512_80k_ade20k_20200615_015125.log.json) |
| DANet | R-101-D8 | 512x512 | 80000 | 15 | 14.18 | 43.64 | 45.19 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/danet/danet_r101-d8_512x512_80k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_80k_ade20k/danet_r101-d8_512x512_80k_ade20k_20200615_015126-d0357c73.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_80k_ade20k/danet_r101-d8_512x512_80k_ade20k_20200615_015126.log.json) |
| DANet | R-50-D8 | 512x512 | 160000 | - | - | 42.45 | 43.25 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/danet/danet_r50-d8_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_160k_ade20k/danet_r50-d8_512x512_160k_ade20k_20200616_082340-9cb35dcd.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_160k_ade20k/danet_r50-d8_512x512_160k_ade20k_20200616_082340.log.json) |
| DANet | R-101-D8 | 512x512 | 160000 | - | - | 44.17 | 45.02 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/danet/danet_r101-d8_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_160k_ade20k/danet_r101-d8_512x512_160k_ade20k_20200616_082348-23bf12f9.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_160k_ade20k/danet_r101-d8_512x512_160k_ade20k_20200616_082348.log.json) |
### Pascal VOC 2012 + Aug
| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download |
| ------ | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ---------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| DANet | R-50-D8 | 512x512 | 20000 | 6.5 | 20.94 | 74.45 | 75.69 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/danet/danet_r50-d8_512x512_20k_voc12aug.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_20k_voc12aug/danet_r50-d8_512x512_20k_voc12aug_20200618_070026-9e9e3ab3.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_20k_voc12aug/danet_r50-d8_512x512_20k_voc12aug_20200618_070026.log.json) |
| DANet | R-101-D8 | 512x512 | 20000 | 9.9 | 13.76 | 76.02 | 77.23 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/danet/danet_r101-d8_512x512_20k_voc12aug.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_20k_voc12aug/danet_r101-d8_512x512_20k_voc12aug_20200618_070026-d48d23b2.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_20k_voc12aug/danet_r101-d8_512x512_20k_voc12aug_20200618_070026.log.json) |
| DANet | R-50-D8 | 512x512 | 40000 | - | - | 76.37 | 77.29 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/danet/danet_r50-d8_512x512_40k_voc12aug.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_40k_voc12aug/danet_r50-d8_512x512_40k_voc12aug_20200613_235526-426e3a64.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_40k_voc12aug/danet_r50-d8_512x512_40k_voc12aug_20200613_235526.log.json) |
| DANet | R-101-D8 | 512x512 | 40000 | - | - | 76.51 | 77.32 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/danet/danet_r101-d8_512x512_40k_voc12aug.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_40k_voc12aug/danet_r101-d8_512x512_40k_voc12aug_20200613_223031-788e232a.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_40k_voc12aug/danet_r101-d8_512x512_40k_voc12aug_20200613_223031.log.json) |
| 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/blob/v0.17.0/mmseg/models/decode_heads/da_head.py#L76
Version: v0.17.0
Converted From:
Code: https://github.com/junfu1115/DANet/
Models:
- Name: danet_r50-d8_512x1024_40k_cityscapes
In Collection: DANet
Metadata:
backbone: R-50-D8
crop size: (512,1024)
lr schd: 40000
inference time (ms/im):
- value: 375.94
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,1024)
Training Memory (GB): 7.4
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 78.74
Config: configs/danet/danet_r50-d8_512x1024_40k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x1024_40k_cityscapes/danet_r50-d8_512x1024_40k_cityscapes_20200605_191324-c0dbfa5f.pth
- Name: danet_r101-d8_512x1024_40k_cityscapes
In Collection: DANet
Metadata:
backbone: R-101-D8
crop size: (512,1024)
lr schd: 40000
inference time (ms/im):
- value: 502.51
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,1024)
Training Memory (GB): 10.9
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 80.52
Config: configs/danet/danet_r101-d8_512x1024_40k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x1024_40k_cityscapes/danet_r101-d8_512x1024_40k_cityscapes_20200605_200831-c57a7157.pth
- Name: danet_r50-d8_769x769_40k_cityscapes
In Collection: DANet
Metadata:
backbone: R-50-D8
crop size: (769,769)
lr schd: 40000
inference time (ms/im):
- value: 641.03
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (769,769)
Training Memory (GB): 8.8
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 78.88
mIoU(ms+flip): 80.62
Config: configs/danet/danet_r50-d8_769x769_40k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_769x769_40k_cityscapes/danet_r50-d8_769x769_40k_cityscapes_20200530_025703-76681c60.pth
- Name: danet_r101-d8_769x769_40k_cityscapes
In Collection: DANet
Metadata:
backbone: R-101-D8
crop size: (769,769)
lr schd: 40000
inference time (ms/im):
- value: 934.58
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (769,769)
Training Memory (GB): 12.8
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.88
mIoU(ms+flip): 81.47
Config: configs/danet/danet_r101-d8_769x769_40k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_769x769_40k_cityscapes/danet_r101-d8_769x769_40k_cityscapes_20200530_025717-dcb7fd4e.pth
- Name: danet_r50-d8_512x1024_80k_cityscapes
In Collection: DANet
Metadata:
backbone: R-50-D8
crop size: (512,1024)
lr schd: 80000
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.34
Config: configs/danet/danet_r50-d8_512x1024_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x1024_80k_cityscapes/danet_r50-d8_512x1024_80k_cityscapes_20200607_133029-2bfa2293.pth
- Name: danet_r101-d8_512x1024_80k_cityscapes
In Collection: DANet
Metadata:
backbone: R-101-D8
crop size: (512,1024)
lr schd: 80000
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 80.41
Config: configs/danet/danet_r101-d8_512x1024_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x1024_80k_cityscapes/danet_r101-d8_512x1024_80k_cityscapes_20200607_132918-955e6350.pth
- Name: danet_r50-d8_769x769_80k_cityscapes
In Collection: DANet
Metadata:
backbone: R-50-D8
crop size: (769,769)
lr schd: 80000
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.27
mIoU(ms+flip): 80.96
Config: configs/danet/danet_r50-d8_769x769_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_769x769_80k_cityscapes/danet_r50-d8_769x769_80k_cityscapes_20200607_132954-495689b4.pth
- Name: danet_r101-d8_769x769_80k_cityscapes
In Collection: DANet
Metadata:
backbone: R-101-D8
crop size: (769,769)
lr schd: 80000
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 80.47
mIoU(ms+flip): 82.02
Config: configs/danet/danet_r101-d8_769x769_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_769x769_80k_cityscapes/danet_r101-d8_769x769_80k_cityscapes_20200607_132918-f3a929e7.pth
- Name: danet_r50-d8_512x512_80k_ade20k
In Collection: DANet
Metadata:
backbone: R-50-D8
crop size: (512,512)
lr schd: 80000
inference time (ms/im):
- value: 47.17
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 11.5
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 41.66
mIoU(ms+flip): 42.9
Config: configs/danet/danet_r50-d8_512x512_80k_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_80k_ade20k/danet_r50-d8_512x512_80k_ade20k_20200615_015125-edb18e08.pth
- Name: danet_r101-d8_512x512_80k_ade20k
In Collection: DANet
Metadata:
backbone: R-101-D8
crop size: (512,512)
lr schd: 80000
inference time (ms/im):
- value: 70.52
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 15.0
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 43.64
mIoU(ms+flip): 45.19
Config: configs/danet/danet_r101-d8_512x512_80k_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_80k_ade20k/danet_r101-d8_512x512_80k_ade20k_20200615_015126-d0357c73.pth
- Name: danet_r50-d8_512x512_160k_ade20k
In Collection: DANet
Metadata:
backbone: R-50-D8
crop size: (512,512)
lr schd: 160000
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 42.45
mIoU(ms+flip): 43.25
Config: configs/danet/danet_r50-d8_512x512_160k_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_160k_ade20k/danet_r50-d8_512x512_160k_ade20k_20200616_082340-9cb35dcd.pth
- Name: danet_r101-d8_512x512_160k_ade20k
In Collection: DANet
Metadata:
backbone: R-101-D8
crop size: (512,512)
lr schd: 160000
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 44.17
mIoU(ms+flip): 45.02
Config: configs/danet/danet_r101-d8_512x512_160k_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_160k_ade20k/danet_r101-d8_512x512_160k_ade20k_20200616_082348-23bf12f9.pth
- Name: danet_r50-d8_512x512_20k_voc12aug
In Collection: DANet
Metadata:
backbone: R-50-D8
crop size: (512,512)
lr schd: 20000
inference time (ms/im):
- value: 47.76
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 6.5
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 74.45
mIoU(ms+flip): 75.69
Config: configs/danet/danet_r50-d8_512x512_20k_voc12aug.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_20k_voc12aug/danet_r50-d8_512x512_20k_voc12aug_20200618_070026-9e9e3ab3.pth
- Name: danet_r101-d8_512x512_20k_voc12aug
In Collection: DANet
Metadata:
backbone: R-101-D8
crop size: (512,512)
lr schd: 20000
inference time (ms/im):
- value: 72.67
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 9.9
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 76.02
mIoU(ms+flip): 77.23
Config: configs/danet/danet_r101-d8_512x512_20k_voc12aug.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_20k_voc12aug/danet_r101-d8_512x512_20k_voc12aug_20200618_070026-d48d23b2.pth
- Name: danet_r50-d8_512x512_40k_voc12aug
In Collection: DANet
Metadata:
backbone: R-50-D8
crop size: (512,512)
lr schd: 40000
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 76.37
mIoU(ms+flip): 77.29
Config: configs/danet/danet_r50-d8_512x512_40k_voc12aug.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_40k_voc12aug/danet_r50-d8_512x512_40k_voc12aug_20200613_235526-426e3a64.pth
- Name: danet_r101-d8_512x512_40k_voc12aug
In Collection: DANet
Metadata:
backbone: R-101-D8
crop size: (512,512)
lr schd: 40000
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 76.51
mIoU(ms+flip): 77.32
Config: configs/danet/danet_r101-d8_512x512_40k_voc12aug.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_40k_voc12aug/danet_r101-d8_512x512_40k_voc12aug_20200613_223031-788e232a.pth
| 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=(769, 769), stride=(513, 513)))
| 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=(769, 769), stride=(513, 513)))
| 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/models/decode_heads/aspp_head.py#L54">Code Snippet</a>
## Abstract
<!-- [ABSTRACT] -->
In this work, we revisit atrous convolution, a powerful tool to explicitly adjust filter's field-of-view as well as control the resolution of feature responses computed by Deep Convolutional Neural Networks, in the application of semantic image segmentation. To handle the problem of segmenting objects at multiple scales, we design modules which employ atrous convolution in cascade or in parallel to capture multi-scale context by adopting multiple atrous rates. Furthermore, we propose to augment our previously proposed Atrous Spatial Pyramid Pooling module, which probes convolutional features at multiple scales, with image-level features encoding global context and further boost performance. We also elaborate on implementation details and share our experience on training our system. The proposed \`DeepLabv3' system significantly improves over our previous DeepLab versions without DenseCRF post-processing and attains comparable performance with other state-of-art models on the PASCAL VOC 2012 semantic image segmentation benchmark.
<!-- [IMAGE] -->
<div align=center>
<img src="https://user-images.githubusercontent.com/24582831/142900575-f30a7755-09aa-406a-bf78-45893a61ee9a.png" width="80%"/>
</div>
## Citation
```bibtext
@article{chen2017rethinking,
title={Rethinking atrous convolution for semantic image segmentation},
author={Chen, Liang-Chieh and Papandreou, George and Schroff, Florian and Adam, Hartwig},
journal={arXiv preprint arXiv:1706.05587},
year={2017}
}
```
## Results and models
### Cityscapes
| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download |
| ---------------- | --------------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ---------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| DeepLabV3 | R-50-D8 | 512x1024 | 40000 | 6.1 | 2.57 | 79.09 | 80.45 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3/deeplabv3_r50-d8_512x1024_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x1024_40k_cityscapes/deeplabv3_r50-d8_512x1024_40k_cityscapes_20200605_022449-acadc2f8.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x1024_40k_cityscapes/deeplabv3_r50-d8_512x1024_40k_cityscapes_20200605_022449.log.json) |
| DeepLabV3 | R-101-D8 | 512x1024 | 40000 | 9.6 | 1.92 | 77.12 | 79.61 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3/deeplabv3_r101-d8_512x1024_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x1024_40k_cityscapes/deeplabv3_r101-d8_512x1024_40k_cityscapes_20200605_012241-7fd3f799.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x1024_40k_cityscapes/deeplabv3_r101-d8_512x1024_40k_cityscapes_20200605_012241.log.json) |
| DeepLabV3 | R-50-D8 | 769x769 | 40000 | 6.9 | 1.11 | 78.58 | 79.89 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3/deeplabv3_r50-d8_769x769_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_769x769_40k_cityscapes/deeplabv3_r50-d8_769x769_40k_cityscapes_20200606_113723-7eda553c.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_769x769_40k_cityscapes/deeplabv3_r50-d8_769x769_40k_cityscapes_20200606_113723.log.json) |
| DeepLabV3 | R-101-D8 | 769x769 | 40000 | 10.9 | 0.83 | 79.27 | 80.11 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3/deeplabv3_r101-d8_769x769_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_769x769_40k_cityscapes/deeplabv3_r101-d8_769x769_40k_cityscapes_20200606_113809-c64f889f.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_769x769_40k_cityscapes/deeplabv3_r101-d8_769x769_40k_cityscapes_20200606_113809.log.json) |
| DeepLabV3 | R-18-D8 | 512x1024 | 80000 | 1.7 | 13.78 | 76.70 | 78.27 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3/deeplabv3_r18-d8_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r18-d8_512x1024_80k_cityscapes/deeplabv3_r18-d8_512x1024_80k_cityscapes_20201225_021506-23dffbe2.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r18-d8_512x1024_80k_cityscapes/deeplabv3_r18-d8_512x1024_80k_cityscapes-20201225_021506.log.json) |
| DeepLabV3 | R-50-D8 | 512x1024 | 80000 | - | - | 79.32 | 80.57 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3/deeplabv3_r50-d8_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x1024_80k_cityscapes/deeplabv3_r50-d8_512x1024_80k_cityscapes_20200606_113404-b92cfdd4.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x1024_80k_cityscapes/deeplabv3_r50-d8_512x1024_80k_cityscapes_20200606_113404.log.json) |
| DeepLabV3 | R-101-D8 | 512x1024 | 80000 | - | - | 80.20 | 81.21 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3/deeplabv3_r101-d8_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x1024_80k_cityscapes/deeplabv3_r101-d8_512x1024_80k_cityscapes_20200606_113503-9e428899.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x1024_80k_cityscapes/deeplabv3_r101-d8_512x1024_80k_cityscapes_20200606_113503.log.json) |
| DeepLabV3 (FP16) | R-101-D8 | 512x1024 | 80000 | 5.75 | 3.86 | 80.48 | - | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3/deeplabv3_r101-d8_fp16_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_fp16_512x1024_80k_cityscapes/deeplabv3_r101-d8_fp16_512x1024_80k_cityscapes_20200717_230920-774d9cec.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_fp16_512x1024_80k_cityscapes/deeplabv3_r101-d8_fp16_512x1024_80k_cityscapes_20200717_230920.log.json) |
| DeepLabV3 | R-18-D8 | 769x769 | 80000 | 1.9 | 5.55 | 76.60 | 78.26 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3/deeplabv3_r18-d8_769x769_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r18-d8_769x769_80k_cityscapes/deeplabv3_r18-d8_769x769_80k_cityscapes_20201225_021506-6452126a.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r18-d8_769x769_80k_cityscapes/deeplabv3_r18-d8_769x769_80k_cityscapes-20201225_021506.log.json) |
| DeepLabV3 | R-50-D8 | 769x769 | 80000 | - | - | 79.89 | 81.06 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3/deeplabv3_r50-d8_769x769_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_769x769_80k_cityscapes/deeplabv3_r50-d8_769x769_80k_cityscapes_20200606_221338-788d6228.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_769x769_80k_cityscapes/deeplabv3_r50-d8_769x769_80k_cityscapes_20200606_221338.log.json) |
| DeepLabV3 | R-101-D8 | 769x769 | 80000 | - | - | 79.67 | 80.81 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3/deeplabv3_r101-d8_769x769_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_769x769_80k_cityscapes/deeplabv3_r101-d8_769x769_80k_cityscapes_20200607_013353-60e95418.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_769x769_80k_cityscapes/deeplabv3_r101-d8_769x769_80k_cityscapes_20200607_013353.log.json) |
| DeepLabV3 | R-101-D16-MG124 | 512x1024 | 40000 | 4.7 | - 6.96 | 76.71 | 78.63 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3/deeplabv3_r101-d16-mg124_512x1024_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d16-mg124_512x1024_40k_cityscapes/deeplabv3_r101-d16-mg124_512x1024_40k_cityscapes_20200908_005644-67b0c992.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d16-mg124_512x1024_40k_cityscapes/deeplabv3_r101-d16-mg124_512x1024_40k_cityscapes-20200908_005644.log.json) |
| DeepLabV3 | R-101-D16-MG124 | 512x1024 | 80000 | - | - | 78.36 | 79.84 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3/deeplabv3_r101-d16-mg124_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d16-mg124_512x1024_80k_cityscapes/deeplabv3_r101-d16-mg124_512x1024_80k_cityscapes_20200908_005644-57bb8425.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d16-mg124_512x1024_80k_cityscapes/deeplabv3_r101-d16-mg124_512x1024_80k_cityscapes-20200908_005644.log.json) |
| DeepLabV3 | R-18b-D8 | 512x1024 | 80000 | 1.6 | 13.93 | 76.26 | 77.88 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3/deeplabv3_r18b-d8_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r18b-d8_512x1024_80k_cityscapes/deeplabv3_r18b-d8_512x1024_80k_cityscapes_20201225_094144-46040cef.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r18b-d8_512x1024_80k_cityscapes/deeplabv3_r18b-d8_512x1024_80k_cityscapes-20201225_094144.log.json) |
| DeepLabV3 | R-50b-D8 | 512x1024 | 80000 | 6.0 | 2.74 | 79.63 | 80.98 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3/deeplabv3_r50b-d8_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50b-d8_512x1024_80k_cityscapes/deeplabv3_r50b-d8_512x1024_80k_cityscapes_20201225_155148-ec368954.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50b-d8_512x1024_80k_cityscapes/deeplabv3_r50b-d8_512x1024_80k_cityscapes-20201225_155148.log.json) |
| DeepLabV3 | R-101b-D8 | 512x1024 | 80000 | 9.5 | 1.81 | 80.01 | 81.21 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3/deeplabv3_r101b-d8_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101b-d8_512x1024_80k_cityscapes/deeplabv3_r101b-d8_512x1024_80k_cityscapes_20201226_171821-8fd49503.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101b-d8_512x1024_80k_cityscapes/deeplabv3_r101b-d8_512x1024_80k_cityscapes-20201226_171821.log.json) |
| DeepLabV3 | R-18b-D8 | 769x769 | 80000 | 1.8 | 5.79 | 75.63 | 77.51 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3/deeplabv3_r18b-d8_769x769_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r18b-d8_769x769_80k_cityscapes/deeplabv3_r18b-d8_769x769_80k_cityscapes_20201225_094144-fdc985d9.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r18b-d8_769x769_80k_cityscapes/deeplabv3_r18b-d8_769x769_80k_cityscapes-20201225_094144.log.json) |
| DeepLabV3 | R-50b-D8 | 769x769 | 80000 | 6.8 | 1.16 | 78.80 | 80.27 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3/deeplabv3_r50b-d8_769x769_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50b-d8_769x769_80k_cityscapes/deeplabv3_r50b-d8_769x769_80k_cityscapes_20201225_155404-87fb0cf4.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50b-d8_769x769_80k_cityscapes/deeplabv3_r50b-d8_769x769_80k_cityscapes-20201225_155404.log.json) |
| DeepLabV3 | R-101b-D8 | 769x769 | 80000 | 10.7 | 0.82 | 79.41 | 80.73 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3/deeplabv3_r101b-d8_769x769_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101b-d8_769x769_80k_cityscapes/deeplabv3_r101b-d8_769x769_80k_cityscapes_20201226_190843-9142ee57.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101b-d8_769x769_80k_cityscapes/deeplabv3_r101b-d8_769x769_80k_cityscapes-20201226_190843.log.json) |
### ADE20K
| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download |
| --------- | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ----------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| DeepLabV3 | R-50-D8 | 512x512 | 80000 | 8.9 | 14.76 | 42.42 | 43.28 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3/deeplabv3_r50-d8_512x512_80k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_80k_ade20k/deeplabv3_r50-d8_512x512_80k_ade20k_20200614_185028-0bb3f844.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_80k_ade20k/deeplabv3_r50-d8_512x512_80k_ade20k_20200614_185028.log.json) |
| DeepLabV3 | R-101-D8 | 512x512 | 80000 | 12.4 | 10.14 | 44.08 | 45.19 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3/deeplabv3_r101-d8_512x512_80k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_80k_ade20k/deeplabv3_r101-d8_512x512_80k_ade20k_20200615_021256-d89c7fa4.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_80k_ade20k/deeplabv3_r101-d8_512x512_80k_ade20k_20200615_021256.log.json) |
| DeepLabV3 | R-50-D8 | 512x512 | 160000 | - | - | 42.66 | 44.09 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3/deeplabv3_r50-d8_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_160k_ade20k/deeplabv3_r50-d8_512x512_160k_ade20k_20200615_123227-5d0ee427.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_160k_ade20k/deeplabv3_r50-d8_512x512_160k_ade20k_20200615_123227.log.json) |
| DeepLabV3 | R-101-D8 | 512x512 | 160000 | - | - | 45.00 | 46.66 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3/deeplabv3_r101-d8_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_160k_ade20k/deeplabv3_r101-d8_512x512_160k_ade20k_20200615_105816-b1f72b3b.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_160k_ade20k/deeplabv3_r101-d8_512x512_160k_ade20k_20200615_105816.log.json) |
### Pascal VOC 2012 + Aug
| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download |
| --------- | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ------------------------------------------------------------------------------------------------------------------------------ | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| DeepLabV3 | R-50-D8 | 512x512 | 20000 | 6.1 | 13.88 | 76.17 | 77.42 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3/deeplabv3_r50-d8_512x512_20k_voc12aug.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_20k_voc12aug/deeplabv3_r50-d8_512x512_20k_voc12aug_20200617_010906-596905ef.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_20k_voc12aug/deeplabv3_r50-d8_512x512_20k_voc12aug_20200617_010906.log.json) |
| DeepLabV3 | R-101-D8 | 512x512 | 20000 | 9.6 | 9.81 | 78.70 | 79.95 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3/deeplabv3_r101-d8_512x512_20k_voc12aug.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_20k_voc12aug/deeplabv3_r101-d8_512x512_20k_voc12aug_20200617_010932-8d13832f.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_20k_voc12aug/deeplabv3_r101-d8_512x512_20k_voc12aug_20200617_010932.log.json) |
| DeepLabV3 | R-50-D8 | 512x512 | 40000 | - | - | 77.68 | 78.78 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3/deeplabv3_r50-d8_512x512_40k_voc12aug.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_40k_voc12aug/deeplabv3_r50-d8_512x512_40k_voc12aug_20200613_161546-2ae96e7e.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_40k_voc12aug/deeplabv3_r50-d8_512x512_40k_voc12aug_20200613_161546.log.json) |
| DeepLabV3 | R-101-D8 | 512x512 | 40000 | - | - | 77.92 | 79.18 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3/deeplabv3_r101-d8_512x512_40k_voc12aug.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_40k_voc12aug/deeplabv3_r101-d8_512x512_40k_voc12aug_20200613_161432-0017d784.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_40k_voc12aug/deeplabv3_r101-d8_512x512_40k_voc12aug_20200613_161432.log.json) |
### Pascal Context
| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download |
| --------- | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ------------------------------------------------------------------------------------------------------------------------------------ | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| DeepLabV3 | R-101-D8 | 480x480 | 40000 | 9.2 | 7.09 | 46.55 | 47.81 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context/deeplabv3_r101-d8_480x480_40k_pascal_context_20200911_204118-1aa27336.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context/deeplabv3_r101-d8_480x480_40k_pascal_context-20200911_204118.log.json) |
| DeepLabV3 | R-101-D8 | 480x480 | 80000 | - | - | 46.42 | 47.53 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3/deeplabv3_r101-d8_480x480_80k_pascal_context.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_480x480_80k_pascal_context/deeplabv3_r101-d8_480x480_80k_pascal_context_20200911_170155-2a21fff3.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_480x480_80k_pascal_context/deeplabv3_r101-d8_480x480_80k_pascal_context-20200911_170155.log.json) |
### Pascal Context 59
| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download |
| --------- | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | --------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| DeepLabV3 | R-101-D8 | 480x480 | 40000 | - | - | 52.61 | 54.28 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context_59.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context_59/deeplabv3_r101-d8_480x480_40k_pascal_context_59_20210416_110332-cb08ea46.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context_59/deeplabv3_r101-d8_480x480_40k_pascal_context_59-20210416_110332.log.json) |
| DeepLabV3 | R-101-D8 | 480x480 | 80000 | - | - | 52.46 | 54.09 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3/deeplabv3_r101-d8_480x480_80k_pascal_context_59.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_480x480_80k_pascal_context_59/deeplabv3_r101-d8_480x480_80k_pascal_context_59_20210416_113002-26303993.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_480x480_80k_pascal_context_59/deeplabv3_r101-d8_480x480_80k_pascal_context_59-20210416_113002.log.json) |
### COCO-Stuff 10k
| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download |
| --------- | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ----------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| DeepLabV3 | R-50-D8 | 512x512 | 20000 | 9.6 | 10.8 | 34.66 | 36.08 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3_r50-d8_512x512_4x4_20k_coco-stuff10k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_4x4_20k_coco-stuff10k/deeplabv3_r50-d8_512x512_4x4_20k_coco-stuff10k_20210821_043025-b35f789d.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_4x4_20k_coco-stuff10k/deeplabv3_r50-d8_512x512_4x4_20k_coco-stuff10k_20210821_043025.log.json) |
| DeepLabV3 | R-101-D8 | 512x512 | 20000 | 13.2 | 8.7 | 37.30 | 38.42 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3_r101-d8_512x512_4x4_20k_coco-stuff10k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_4x4_20k_coco-stuff10k/deeplabv3_r101-d8_512x512_4x4_20k_coco-stuff10k_20210821_043025-c49752cb.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_4x4_20k_coco-stuff10k/deeplabv3_r101-d8_512x512_4x4_20k_coco-stuff10k_20210821_043025.log.json) |
| DeepLabV3 | R-50-D8 | 512x512 | 40000 | - | - | 35.73 | 37.09 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3_r50-d8_512x512_4x4_40k_coco-stuff10k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_4x4_40k_coco-stuff10k/deeplabv3_r50-d8_512x512_4x4_40k_coco-stuff10k_20210821_043305-dc76f3ff.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_4x4_40k_coco-stuff10k/deeplabv3_r50-d8_512x512_4x4_40k_coco-stuff10k_20210821_043305.log.json) |
| DeepLabV3 | R-101-D8 | 512x512 | 40000 | - | - | 37.81 | 38.80 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3_r101-d8_512x512_4x4_40k_coco-stuff10k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_4x4_40k_coco-stuff10k/deeplabv3_r101-d8_512x512_4x4_40k_coco-stuff10k_20210821_043305-636cb433.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_4x4_40k_coco-stuff10k/deeplabv3_r101-d8_512x512_4x4_40k_coco-stuff10k_20210821_043305.log.json) |
### COCO-Stuff 164k
| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download |
| --------- | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| DeepLabV3 | R-50-D8 | 512x512 | 80000 | 9.6 | 10.8 | 39.38 | 40.03 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3_r50-d8_512x512_4x4_80k_coco-stuff164k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_4x4_80k_coco-stuff164k/deeplabv3_r50-d8_512x512_4x4_80k_coco-stuff164k_20210709_163016-88675c24.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_4x4_80k_coco-stuff164k/deeplabv3_r50-d8_512x512_4x4_80k_coco-stuff164k_20210709_163016.log.json) |
| DeepLabV3 | R-101-D8 | 512x512 | 80000 | 13.2 | 8.7 | 40.87 | 41.50 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3_r101-d8_512x512_4x4_80k_coco-stuff164k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_4x4_80k_coco-stuff164k/deeplabv3_r101-d8_512x512_4x4_80k_coco-stuff164k_20210709_201252-13600dc2.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_4x4_80k_coco-stuff164k/deeplabv3_r101-d8_512x512_4x4_80k_coco-stuff164k_20210709_201252.log.json) |
| DeepLabV3 | R-50-D8 | 512x512 | 160000 | - | - | 41.09 | 41.69 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3_r50-d8_512x512_4x4_160k_coco-stuff164k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_4x4_160k_coco-stuff164k/deeplabv3_r50-d8_512x512_4x4_160k_coco-stuff164k_20210709_163016-49f2812b.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_4x4_160k_coco-stuff164k/deeplabv3_r50-d8_512x512_4x4_160k_coco-stuff164k_20210709_163016.log.json) |
| DeepLabV3 | R-101-D8 | 512x512 | 160000 | - | - | 41.82 | 42.49 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3_r101-d8_512x512_4x4_160k_coco-stuff164k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_4x4_160k_coco-stuff164k/deeplabv3_r101-d8_512x512_4x4_160k_coco-stuff164k_20210709_155402-f035acfd.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_4x4_160k_coco-stuff164k/deeplabv3_r101-d8_512x512_4x4_160k_coco-stuff164k_20210709_155402.log.json) |
| DeepLabV3 | R-50-D8 | 512x512 | 320000 | - | - | 41.37 | 42.22 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3_r50-d8_512x512_4x4_320k_coco-stuff164k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_4x4_320k_coco-stuff164k/deeplabv3_r50-d8_512x512_4x4_320k_coco-stuff164k_20210709_155403-51b21115.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_4x4_320k_coco-stuff164k/deeplabv3_r50-d8_512x512_4x4_320k_coco-stuff164k_20210709_155403.log.json) |
| DeepLabV3 | R-101-D8 | 512x512 | 320000 | - | - | 42.61 | 43.42 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3_r101-d8_512x512_4x4_320k_coco-stuff164k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_4x4_320k_coco-stuff164k/deeplabv3_r101-d8_512x512_4x4_320k_coco-stuff164k_20210709_155402-3cbca14d.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_4x4_320k_coco-stuff164k/deeplabv3_r101-d8_512x512_4x4_320k_coco-stuff164k_20210709_155402.log.json) |
Note:
- `D-8` here corresponding to the output stride 8 setting for DeepLab series.
- `FP16` means Mixed Precision (FP16) is adopted in training.
| 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 segmentation
README: configs/deeplabv3/README.md
Code:
URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/aspp_head.py#L54
Version: v0.17.0
Converted From:
Code: https://github.com/tensorflow/models/tree/master/research/deeplab
Models:
- Name: deeplabv3_r50-d8_512x1024_40k_cityscapes
In Collection: DeepLabV3
Metadata:
backbone: R-50-D8
crop size: (512,1024)
lr schd: 40000
inference time (ms/im):
- value: 389.11
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,1024)
Training Memory (GB): 6.1
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.09
mIoU(ms+flip): 80.45
Config: configs/deeplabv3/deeplabv3_r50-d8_512x1024_40k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x1024_40k_cityscapes/deeplabv3_r50-d8_512x1024_40k_cityscapes_20200605_022449-acadc2f8.pth
- Name: deeplabv3_r101-d8_512x1024_40k_cityscapes
In Collection: DeepLabV3
Metadata:
backbone: R-101-D8
crop size: (512,1024)
lr schd: 40000
inference time (ms/im):
- value: 520.83
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,1024)
Training Memory (GB): 9.6
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 77.12
mIoU(ms+flip): 79.61
Config: configs/deeplabv3/deeplabv3_r101-d8_512x1024_40k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x1024_40k_cityscapes/deeplabv3_r101-d8_512x1024_40k_cityscapes_20200605_012241-7fd3f799.pth
- Name: deeplabv3_r50-d8_769x769_40k_cityscapes
In Collection: DeepLabV3
Metadata:
backbone: R-50-D8
crop size: (769,769)
lr schd: 40000
inference time (ms/im):
- value: 900.9
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (769,769)
Training Memory (GB): 6.9
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 78.58
mIoU(ms+flip): 79.89
Config: configs/deeplabv3/deeplabv3_r50-d8_769x769_40k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_769x769_40k_cityscapes/deeplabv3_r50-d8_769x769_40k_cityscapes_20200606_113723-7eda553c.pth
- Name: deeplabv3_r101-d8_769x769_40k_cityscapes
In Collection: DeepLabV3
Metadata:
backbone: R-101-D8
crop size: (769,769)
lr schd: 40000
inference time (ms/im):
- value: 1204.82
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (769,769)
Training Memory (GB): 10.9
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.27
mIoU(ms+flip): 80.11
Config: configs/deeplabv3/deeplabv3_r101-d8_769x769_40k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_769x769_40k_cityscapes/deeplabv3_r101-d8_769x769_40k_cityscapes_20200606_113809-c64f889f.pth
- Name: deeplabv3_r18-d8_512x1024_80k_cityscapes
In Collection: DeepLabV3
Metadata:
backbone: R-18-D8
crop size: (512,1024)
lr schd: 80000
inference time (ms/im):
- value: 72.57
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,1024)
Training Memory (GB): 1.7
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 76.7
mIoU(ms+flip): 78.27
Config: configs/deeplabv3/deeplabv3_r18-d8_512x1024_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r18-d8_512x1024_80k_cityscapes/deeplabv3_r18-d8_512x1024_80k_cityscapes_20201225_021506-23dffbe2.pth
- Name: deeplabv3_r50-d8_512x1024_80k_cityscapes
In Collection: DeepLabV3
Metadata:
backbone: R-50-D8
crop size: (512,1024)
lr schd: 80000
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.32
mIoU(ms+flip): 80.57
Config: configs/deeplabv3/deeplabv3_r50-d8_512x1024_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x1024_80k_cityscapes/deeplabv3_r50-d8_512x1024_80k_cityscapes_20200606_113404-b92cfdd4.pth
- Name: deeplabv3_r101-d8_512x1024_80k_cityscapes
In Collection: DeepLabV3
Metadata:
backbone: R-101-D8
crop size: (512,1024)
lr schd: 80000
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 80.2
mIoU(ms+flip): 81.21
Config: configs/deeplabv3/deeplabv3_r101-d8_512x1024_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x1024_80k_cityscapes/deeplabv3_r101-d8_512x1024_80k_cityscapes_20200606_113503-9e428899.pth
- Name: deeplabv3_r101-d8_fp16_512x1024_80k_cityscapes
In Collection: DeepLabV3
Metadata:
backbone: R-101-D8
crop size: (512,1024)
lr schd: 80000
inference time (ms/im):
- value: 259.07
hardware: V100
backend: PyTorch
batch size: 1
mode: FP16
resolution: (512,1024)
Training Memory (GB): 5.75
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 80.48
Config: configs/deeplabv3/deeplabv3_r101-d8_fp16_512x1024_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_fp16_512x1024_80k_cityscapes/deeplabv3_r101-d8_fp16_512x1024_80k_cityscapes_20200717_230920-774d9cec.pth
- Name: deeplabv3_r18-d8_769x769_80k_cityscapes
In Collection: DeepLabV3
Metadata:
backbone: R-18-D8
crop size: (769,769)
lr schd: 80000
inference time (ms/im):
- value: 180.18
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (769,769)
Training Memory (GB): 1.9
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 76.6
mIoU(ms+flip): 78.26
Config: configs/deeplabv3/deeplabv3_r18-d8_769x769_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r18-d8_769x769_80k_cityscapes/deeplabv3_r18-d8_769x769_80k_cityscapes_20201225_021506-6452126a.pth
- Name: deeplabv3_r50-d8_769x769_80k_cityscapes
In Collection: DeepLabV3
Metadata:
backbone: R-50-D8
crop size: (769,769)
lr schd: 80000
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.89
mIoU(ms+flip): 81.06
Config: configs/deeplabv3/deeplabv3_r50-d8_769x769_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_769x769_80k_cityscapes/deeplabv3_r50-d8_769x769_80k_cityscapes_20200606_221338-788d6228.pth
- Name: deeplabv3_r101-d8_769x769_80k_cityscapes
In Collection: DeepLabV3
Metadata:
backbone: R-101-D8
crop size: (769,769)
lr schd: 80000
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.67
mIoU(ms+flip): 80.81
Config: configs/deeplabv3/deeplabv3_r101-d8_769x769_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_769x769_80k_cityscapes/deeplabv3_r101-d8_769x769_80k_cityscapes_20200607_013353-60e95418.pth
- Name: deeplabv3_r101-d16-mg124_512x1024_80k_cityscapes
In Collection: DeepLabV3
Metadata:
backbone: R-101-D16-MG124
crop size: (512,1024)
lr schd: 80000
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 78.36
mIoU(ms+flip): 79.84
Config: configs/deeplabv3/deeplabv3_r101-d16-mg124_512x1024_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d16-mg124_512x1024_80k_cityscapes/deeplabv3_r101-d16-mg124_512x1024_80k_cityscapes_20200908_005644-57bb8425.pth
- Name: deeplabv3_r18b-d8_512x1024_80k_cityscapes
In Collection: DeepLabV3
Metadata:
backbone: R-18b-D8
crop size: (512,1024)
lr schd: 80000
inference time (ms/im):
- value: 71.79
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,1024)
Training Memory (GB): 1.6
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 76.26
mIoU(ms+flip): 77.88
Config: configs/deeplabv3/deeplabv3_r18b-d8_512x1024_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r18b-d8_512x1024_80k_cityscapes/deeplabv3_r18b-d8_512x1024_80k_cityscapes_20201225_094144-46040cef.pth
- Name: deeplabv3_r50b-d8_512x1024_80k_cityscapes
In Collection: DeepLabV3
Metadata:
backbone: R-50b-D8
crop size: (512,1024)
lr schd: 80000
inference time (ms/im):
- value: 364.96
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,1024)
Training Memory (GB): 6.0
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.63
mIoU(ms+flip): 80.98
Config: configs/deeplabv3/deeplabv3_r50b-d8_512x1024_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50b-d8_512x1024_80k_cityscapes/deeplabv3_r50b-d8_512x1024_80k_cityscapes_20201225_155148-ec368954.pth
- Name: deeplabv3_r101b-d8_512x1024_80k_cityscapes
In Collection: DeepLabV3
Metadata:
backbone: R-101b-D8
crop size: (512,1024)
lr schd: 80000
inference time (ms/im):
- value: 552.49
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,1024)
Training Memory (GB): 9.5
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 80.01
mIoU(ms+flip): 81.21
Config: configs/deeplabv3/deeplabv3_r101b-d8_512x1024_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101b-d8_512x1024_80k_cityscapes/deeplabv3_r101b-d8_512x1024_80k_cityscapes_20201226_171821-8fd49503.pth
- Name: deeplabv3_r18b-d8_769x769_80k_cityscapes
In Collection: DeepLabV3
Metadata:
backbone: R-18b-D8
crop size: (769,769)
lr schd: 80000
inference time (ms/im):
- value: 172.71
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (769,769)
Training Memory (GB): 1.8
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 75.63
mIoU(ms+flip): 77.51
Config: configs/deeplabv3/deeplabv3_r18b-d8_769x769_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r18b-d8_769x769_80k_cityscapes/deeplabv3_r18b-d8_769x769_80k_cityscapes_20201225_094144-fdc985d9.pth
- Name: deeplabv3_r50b-d8_769x769_80k_cityscapes
In Collection: DeepLabV3
Metadata:
backbone: R-50b-D8
crop size: (769,769)
lr schd: 80000
inference time (ms/im):
- value: 862.07
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (769,769)
Training Memory (GB): 6.8
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 78.8
mIoU(ms+flip): 80.27
Config: configs/deeplabv3/deeplabv3_r50b-d8_769x769_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50b-d8_769x769_80k_cityscapes/deeplabv3_r50b-d8_769x769_80k_cityscapes_20201225_155404-87fb0cf4.pth
- Name: deeplabv3_r101b-d8_769x769_80k_cityscapes
In Collection: DeepLabV3
Metadata:
backbone: R-101b-D8
crop size: (769,769)
lr schd: 80000
inference time (ms/im):
- value: 1219.51
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (769,769)
Training Memory (GB): 10.7
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.41
mIoU(ms+flip): 80.73
Config: configs/deeplabv3/deeplabv3_r101b-d8_769x769_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101b-d8_769x769_80k_cityscapes/deeplabv3_r101b-d8_769x769_80k_cityscapes_20201226_190843-9142ee57.pth
- Name: deeplabv3_r50-d8_512x512_80k_ade20k
In Collection: DeepLabV3
Metadata:
backbone: R-50-D8
crop size: (512,512)
lr schd: 80000
inference time (ms/im):
- value: 67.75
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 8.9
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 42.42
mIoU(ms+flip): 43.28
Config: configs/deeplabv3/deeplabv3_r50-d8_512x512_80k_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_80k_ade20k/deeplabv3_r50-d8_512x512_80k_ade20k_20200614_185028-0bb3f844.pth
- Name: deeplabv3_r101-d8_512x512_80k_ade20k
In Collection: DeepLabV3
Metadata:
backbone: R-101-D8
crop size: (512,512)
lr schd: 80000
inference time (ms/im):
- value: 98.62
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 12.4
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 44.08
mIoU(ms+flip): 45.19
Config: configs/deeplabv3/deeplabv3_r101-d8_512x512_80k_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_80k_ade20k/deeplabv3_r101-d8_512x512_80k_ade20k_20200615_021256-d89c7fa4.pth
- Name: deeplabv3_r50-d8_512x512_160k_ade20k
In Collection: DeepLabV3
Metadata:
backbone: R-50-D8
crop size: (512,512)
lr schd: 160000
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 42.66
mIoU(ms+flip): 44.09
Config: configs/deeplabv3/deeplabv3_r50-d8_512x512_160k_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_160k_ade20k/deeplabv3_r50-d8_512x512_160k_ade20k_20200615_123227-5d0ee427.pth
- Name: deeplabv3_r101-d8_512x512_160k_ade20k
In Collection: DeepLabV3
Metadata:
backbone: R-101-D8
crop size: (512,512)
lr schd: 160000
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 45.0
mIoU(ms+flip): 46.66
Config: configs/deeplabv3/deeplabv3_r101-d8_512x512_160k_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_160k_ade20k/deeplabv3_r101-d8_512x512_160k_ade20k_20200615_105816-b1f72b3b.pth
- Name: deeplabv3_r50-d8_512x512_20k_voc12aug
In Collection: DeepLabV3
Metadata:
backbone: R-50-D8
crop size: (512,512)
lr schd: 20000
inference time (ms/im):
- value: 72.05
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 6.1
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 76.17
mIoU(ms+flip): 77.42
Config: configs/deeplabv3/deeplabv3_r50-d8_512x512_20k_voc12aug.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_20k_voc12aug/deeplabv3_r50-d8_512x512_20k_voc12aug_20200617_010906-596905ef.pth
- Name: deeplabv3_r101-d8_512x512_20k_voc12aug
In Collection: DeepLabV3
Metadata:
backbone: R-101-D8
crop size: (512,512)
lr schd: 20000
inference time (ms/im):
- value: 101.94
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 9.6
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 78.7
mIoU(ms+flip): 79.95
Config: configs/deeplabv3/deeplabv3_r101-d8_512x512_20k_voc12aug.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_20k_voc12aug/deeplabv3_r101-d8_512x512_20k_voc12aug_20200617_010932-8d13832f.pth
- Name: deeplabv3_r50-d8_512x512_40k_voc12aug
In Collection: DeepLabV3
Metadata:
backbone: R-50-D8
crop size: (512,512)
lr schd: 40000
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 77.68
mIoU(ms+flip): 78.78
Config: configs/deeplabv3/deeplabv3_r50-d8_512x512_40k_voc12aug.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_40k_voc12aug/deeplabv3_r50-d8_512x512_40k_voc12aug_20200613_161546-2ae96e7e.pth
- Name: deeplabv3_r101-d8_512x512_40k_voc12aug
In Collection: DeepLabV3
Metadata:
backbone: R-101-D8
crop size: (512,512)
lr schd: 40000
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 77.92
mIoU(ms+flip): 79.18
Config: configs/deeplabv3/deeplabv3_r101-d8_512x512_40k_voc12aug.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_40k_voc12aug/deeplabv3_r101-d8_512x512_40k_voc12aug_20200613_161432-0017d784.pth
- Name: deeplabv3_r101-d8_480x480_40k_pascal_context
In Collection: DeepLabV3
Metadata:
backbone: R-101-D8
crop size: (480,480)
lr schd: 40000
inference time (ms/im):
- value: 141.04
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (480,480)
Training Memory (GB): 9.2
Results:
- Task: Semantic Segmentation
Dataset: Pascal Context
Metrics:
mIoU: 46.55
mIoU(ms+flip): 47.81
Config: configs/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context/deeplabv3_r101-d8_480x480_40k_pascal_context_20200911_204118-1aa27336.pth
- Name: deeplabv3_r101-d8_480x480_80k_pascal_context
In Collection: DeepLabV3
Metadata:
backbone: R-101-D8
crop size: (480,480)
lr schd: 80000
Results:
- Task: Semantic Segmentation
Dataset: Pascal Context
Metrics:
mIoU: 46.42
mIoU(ms+flip): 47.53
Config: configs/deeplabv3/deeplabv3_r101-d8_480x480_80k_pascal_context.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_480x480_80k_pascal_context/deeplabv3_r101-d8_480x480_80k_pascal_context_20200911_170155-2a21fff3.pth
- Name: deeplabv3_r101-d8_480x480_40k_pascal_context_59
In Collection: DeepLabV3
Metadata:
backbone: R-101-D8
crop size: (480,480)
lr schd: 40000
Results:
- Task: Semantic Segmentation
Dataset: Pascal Context 59
Metrics:
mIoU: 52.61
mIoU(ms+flip): 54.28
Config: configs/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context_59.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context_59/deeplabv3_r101-d8_480x480_40k_pascal_context_59_20210416_110332-cb08ea46.pth
- Name: deeplabv3_r101-d8_480x480_80k_pascal_context_59
In Collection: DeepLabV3
Metadata:
backbone: R-101-D8
crop size: (480,480)
lr schd: 80000
Results:
- Task: Semantic Segmentation
Dataset: Pascal Context 59
Metrics:
mIoU: 52.46
mIoU(ms+flip): 54.09
Config: configs/deeplabv3/deeplabv3_r101-d8_480x480_80k_pascal_context_59.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_480x480_80k_pascal_context_59/deeplabv3_r101-d8_480x480_80k_pascal_context_59_20210416_113002-26303993.pth
- Name: deeplabv3_r50-d8_512x512_4x4_20k_coco-stuff10k
In Collection: DeepLabV3
Metadata:
backbone: R-50-D8
crop size: (512,512)
lr schd: 20000
inference time (ms/im):
- value: 92.59
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 9.6
Results:
- Task: Semantic Segmentation
Dataset: COCO-Stuff 10k
Metrics:
mIoU: 34.66
mIoU(ms+flip): 36.08
Config: configs/deeplabv3/deeplabv3_r50-d8_512x512_4x4_20k_coco-stuff10k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_4x4_20k_coco-stuff10k/deeplabv3_r50-d8_512x512_4x4_20k_coco-stuff10k_20210821_043025-b35f789d.pth
- Name: deeplabv3_r101-d8_512x512_4x4_20k_coco-stuff10k
In Collection: DeepLabV3
Metadata:
backbone: R-101-D8
crop size: (512,512)
lr schd: 20000
inference time (ms/im):
- value: 114.94
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 13.2
Results:
- Task: Semantic Segmentation
Dataset: COCO-Stuff 10k
Metrics:
mIoU: 37.3
mIoU(ms+flip): 38.42
Config: configs/deeplabv3/deeplabv3_r101-d8_512x512_4x4_20k_coco-stuff10k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_4x4_20k_coco-stuff10k/deeplabv3_r101-d8_512x512_4x4_20k_coco-stuff10k_20210821_043025-c49752cb.pth
- Name: deeplabv3_r50-d8_512x512_4x4_40k_coco-stuff10k
In Collection: DeepLabV3
Metadata:
backbone: R-50-D8
crop size: (512,512)
lr schd: 40000
Results:
- Task: Semantic Segmentation
Dataset: COCO-Stuff 10k
Metrics:
mIoU: 35.73
mIoU(ms+flip): 37.09
Config: configs/deeplabv3/deeplabv3_r50-d8_512x512_4x4_40k_coco-stuff10k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_4x4_40k_coco-stuff10k/deeplabv3_r50-d8_512x512_4x4_40k_coco-stuff10k_20210821_043305-dc76f3ff.pth
- Name: deeplabv3_r101-d8_512x512_4x4_40k_coco-stuff10k
In Collection: DeepLabV3
Metadata:
backbone: R-101-D8
crop size: (512,512)
lr schd: 40000
Results:
- Task: Semantic Segmentation
Dataset: COCO-Stuff 10k
Metrics:
mIoU: 37.81
mIoU(ms+flip): 38.8
Config: configs/deeplabv3/deeplabv3_r101-d8_512x512_4x4_40k_coco-stuff10k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_4x4_40k_coco-stuff10k/deeplabv3_r101-d8_512x512_4x4_40k_coco-stuff10k_20210821_043305-636cb433.pth
- Name: deeplabv3_r50-d8_512x512_4x4_80k_coco-stuff164k
In Collection: DeepLabV3
Metadata:
backbone: R-50-D8
crop size: (512,512)
lr schd: 80000
inference time (ms/im):
- value: 92.59
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 9.6
Results:
- Task: Semantic Segmentation
Dataset: COCO-Stuff 164k
Metrics:
mIoU: 39.38
mIoU(ms+flip): 40.03
Config: configs/deeplabv3/deeplabv3_r50-d8_512x512_4x4_80k_coco-stuff164k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_4x4_80k_coco-stuff164k/deeplabv3_r50-d8_512x512_4x4_80k_coco-stuff164k_20210709_163016-88675c24.pth
- Name: deeplabv3_r101-d8_512x512_4x4_80k_coco-stuff164k
In Collection: DeepLabV3
Metadata:
backbone: R-101-D8
crop size: (512,512)
lr schd: 80000
inference time (ms/im):
- value: 114.94
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 13.2
Results:
- Task: Semantic Segmentation
Dataset: COCO-Stuff 164k
Metrics:
mIoU: 40.87
mIoU(ms+flip): 41.5
Config: configs/deeplabv3/deeplabv3_r101-d8_512x512_4x4_80k_coco-stuff164k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_4x4_80k_coco-stuff164k/deeplabv3_r101-d8_512x512_4x4_80k_coco-stuff164k_20210709_201252-13600dc2.pth
- Name: deeplabv3_r50-d8_512x512_4x4_160k_coco-stuff164k
In Collection: DeepLabV3
Metadata:
backbone: R-50-D8
crop size: (512,512)
lr schd: 160000
Results:
- Task: Semantic Segmentation
Dataset: COCO-Stuff 164k
Metrics:
mIoU: 41.09
mIoU(ms+flip): 41.69
Config: configs/deeplabv3/deeplabv3_r50-d8_512x512_4x4_160k_coco-stuff164k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_4x4_160k_coco-stuff164k/deeplabv3_r50-d8_512x512_4x4_160k_coco-stuff164k_20210709_163016-49f2812b.pth
- Name: deeplabv3_r101-d8_512x512_4x4_160k_coco-stuff164k
In Collection: DeepLabV3
Metadata:
backbone: R-101-D8
crop size: (512,512)
lr schd: 160000
Results:
- Task: Semantic Segmentation
Dataset: COCO-Stuff 164k
Metrics:
mIoU: 41.82
mIoU(ms+flip): 42.49
Config: configs/deeplabv3/deeplabv3_r101-d8_512x512_4x4_160k_coco-stuff164k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_4x4_160k_coco-stuff164k/deeplabv3_r101-d8_512x512_4x4_160k_coco-stuff164k_20210709_155402-f035acfd.pth
- Name: deeplabv3_r50-d8_512x512_4x4_320k_coco-stuff164k
In Collection: DeepLabV3
Metadata:
backbone: R-50-D8
crop size: (512,512)
lr schd: 320000
Results:
- Task: Semantic Segmentation
Dataset: COCO-Stuff 164k
Metrics:
mIoU: 41.37
mIoU(ms+flip): 42.22
Config: configs/deeplabv3/deeplabv3_r50-d8_512x512_4x4_320k_coco-stuff164k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_4x4_320k_coco-stuff164k/deeplabv3_r50-d8_512x512_4x4_320k_coco-stuff164k_20210709_155403-51b21115.pth
- Name: deeplabv3_r101-d8_512x512_4x4_320k_coco-stuff164k
In Collection: DeepLabV3
Metadata:
backbone: R-101-D8
crop size: (512,512)
lr schd: 320000
Results:
- Task: Semantic Segmentation
Dataset: COCO-Stuff 164k
Metrics:
mIoU: 42.61
mIoU(ms+flip): 43.42
Config: configs/deeplabv3/deeplabv3_r101-d8_512x512_4x4_320k_coco-stuff164k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_4x4_320k_coco-stuff164k/deeplabv3_r101-d8_512x512_4x4_320k_coco-stuff164k_20210709_155402-3cbca14d.pth
| 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=dict(type='OHEMPixelSampler', min_kept=100000)))
| 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=dict(type='OHEMPixelSampler', min_kept=100000)))
| 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), stride=(320, 320)))
optimizer = dict(type='SGD', lr=0.004, momentum=0.9, weight_decay=0.0001)
| 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, 480), stride=(320, 320)))
optimizer = dict(type='SGD', lr=0.004, momentum=0.9, weight_decay=0.0001)
| 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), stride=(320, 320)))
optimizer = dict(type='SGD', lr=0.004, momentum=0.9, weight_decay=0.0001)
| 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, 480), stride=(320, 320)))
optimizer = dict(type='SGD', lr=0.004, momentum=0.9, weight_decay=0.0001)
| 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_size=(769, 769), stride=(513, 513)))
| 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_size=(769, 769), stride=(513, 513)))
| 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/blob/v0.17.0/mmseg/models/decode_heads/sep_aspp_head.py#L30">Code Snippet</a>
## Abstract
<!-- [ABSTRACT] -->
Spatial pyramid pooling module or encode-decoder structure are used in deep neural networks for semantic segmentation task. The former networks are able to encode multi-scale contextual information by probing the incoming features with filters or pooling operations at multiple rates and multiple effective fields-of-view, while the latter networks can capture sharper object boundaries by gradually recovering the spatial information. In this work, we propose to combine the advantages from both methods. Specifically, our proposed model, DeepLabv3+, extends DeepLabv3 by adding a simple yet effective decoder module to refine the segmentation results especially along object boundaries. We further explore the Xception model and apply the depthwise separable convolution to both Atrous Spatial Pyramid Pooling and decoder modules, resulting in a faster and stronger encoder-decoder network. We demonstrate the effectiveness of the proposed model on PASCAL VOC 2012 and Cityscapes datasets, achieving the test set performance of 89.0% and 82.1% without any post-processing. Our paper is accompanied with a publicly available reference implementation of the proposed models in Tensorflow at [this https URL](https://github.com/tensorflow/models/tree/master/research/deeplab).
<!-- [IMAGE] -->
<div align=center>
<img src="https://user-images.githubusercontent.com/24582831/142900680-3e2c3098-8341-4760-bbfd-b1d7d29968ea.png" width="70%"/>
</div>
## Citation
```bibtex
@inproceedings{deeplabv3plus2018,
title={Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation},
author={Liang-Chieh Chen and Yukun Zhu and George Papandreou and Florian Schroff and Hartwig Adam},
booktitle={ECCV},
year={2018}
}
```
## Results and models
### Cityscapes
| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download |
| ----------------- | --------------- | --------- | ------: | -------- | -------------- | ----: | ------------: | -------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| DeepLabV3+ | R-50-D8 | 512x1024 | 40000 | 7.5 | 3.94 | 79.61 | 81.01 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r50-d8_512x1024_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x1024_40k_cityscapes/deeplabv3plus_r50-d8_512x1024_40k_cityscapes_20200605_094610-d222ffcd.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x1024_40k_cityscapes/deeplabv3plus_r50-d8_512x1024_40k_cityscapes_20200605_094610.log.json) |
| DeepLabV3+ | R-101-D8 | 512x1024 | 40000 | 11 | 2.60 | 80.21 | 81.82 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r101-d8_512x1024_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x1024_40k_cityscapes/deeplabv3plus_r101-d8_512x1024_40k_cityscapes_20200605_094614-3769eecf.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x1024_40k_cityscapes/deeplabv3plus_r101-d8_512x1024_40k_cityscapes_20200605_094614.log.json) |
| DeepLabV3+ | R-50-D8 | 769x769 | 40000 | 8.5 | 1.72 | 78.97 | 80.46 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r50-d8_769x769_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_769x769_40k_cityscapes/deeplabv3plus_r50-d8_769x769_40k_cityscapes_20200606_114143-1dcb0e3c.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_769x769_40k_cityscapes/deeplabv3plus_r50-d8_769x769_40k_cityscapes_20200606_114143.log.json) |
| DeepLabV3+ | R-101-D8 | 769x769 | 40000 | 12.5 | 1.15 | 79.46 | 80.50 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r101-d8_769x769_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_769x769_40k_cityscapes/deeplabv3plus_r101-d8_769x769_40k_cityscapes_20200606_114304-ff414b9e.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_769x769_40k_cityscapes/deeplabv3plus_r101-d8_769x769_40k_cityscapes_20200606_114304.log.json) |
| DeepLabV3+ | R-18-D8 | 512x1024 | 80000 | 2.2 | 14.27 | 76.89 | 78.76 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r18-d8_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_512x1024_80k_cityscapes/deeplabv3plus_r18-d8_512x1024_80k_cityscapes_20201226_080942-cff257fe.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_512x1024_80k_cityscapes/deeplabv3plus_r18-d8_512x1024_80k_cityscapes-20201226_080942.log.json) |
| DeepLabV3+ | R-50-D8 | 512x1024 | 80000 | - | - | 80.09 | 81.13 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r50-d8_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x1024_80k_cityscapes/deeplabv3plus_r50-d8_512x1024_80k_cityscapes_20200606_114049-f9fb496d.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x1024_80k_cityscapes/deeplabv3plus_r50-d8_512x1024_80k_cityscapes_20200606_114049.log.json) |
| DeepLabV3+ | R-101-D8 | 512x1024 | 80000 | - | - | 80.97 | 82.03 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r101-d8_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x1024_80k_cityscapes/deeplabv3plus_r101-d8_512x1024_80k_cityscapes_20200606_114143-068fcfe9.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x1024_80k_cityscapes/deeplabv3plus_r101-d8_512x1024_80k_cityscapes_20200606_114143.log.json) |
| DeepLabV3+ (FP16) | R-101-D8 | 512x1024 | 80000 | 6.35 | 7.87 | 80.46 | - | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r101-d8_fp16_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_fp16_512x1024_80k_cityscapes/deeplabv3plus_r101-d8_fp16_512x1024_80k_cityscapes_20200717_230920-f1104f4b.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_fp16_512x1024_80k_cityscapes/deeplabv3plus_r101-d8_fp16_512x1024_80k_cityscapes_20200717_230920.log.json) |
| DeepLabV3+ | R-18-D8 | 769x769 | 80000 | 2.5 | 5.74 | 76.26 | 77.91 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r18-d8_769x769_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_769x769_80k_cityscapes/deeplabv3plus_r18-d8_769x769_80k_cityscapes_20201226_083346-f326e06a.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_769x769_80k_cityscapes/deeplabv3plus_r18-d8_769x769_80k_cityscapes-20201226_083346.log.json) |
| DeepLabV3+ | R-50-D8 | 769x769 | 80000 | - | - | 79.83 | 81.48 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r50-d8_769x769_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_769x769_80k_cityscapes/deeplabv3plus_r50-d8_769x769_80k_cityscapes_20200606_210233-0e9dfdc4.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_769x769_80k_cityscapes/deeplabv3plus_r50-d8_769x769_80k_cityscapes_20200606_210233.log.json) |
| DeepLabV3+ | R-101-D8 | 769x769 | 80000 | - | - | 80.65 | 81.47 | [config<sup>\[1\]</sup>](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r101-d8_769x769_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_769x769_80k_cityscapes/deeplabv3plus_r101-d8_769x769_80k_cityscapes_20220406_154720-dfcc0b68.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_769x769_80k_cityscapes/deeplabv3plus_r101-d8_769x769_80k_cityscapes_20220406_154720.log.json) |
| DeepLabV3+ | R-101-D16-MG124 | 512x1024 | 40000 | 5.8 | 7.48 | 79.09 | 80.36 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r101-d16-mg124_512x1024_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d16-mg124_512x1024_40k_cityscapes/deeplabv3plus_r101-d16-mg124_512x1024_40k_cityscapes_20200908_005644-cf9ce186.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d16-mg124_512x1024_40k_cityscapes/deeplabv3plus_r101-d16-mg124_512x1024_40k_cityscapes-20200908_005644.log.json) |
| DeepLabV3+ | R-101-D16-MG124 | 512x1024 | 80000 | 9.9 | - | 79.90 | 81.33 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r101-d16-mg124_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d16-mg124_512x1024_80k_cityscapes/deeplabv3plus_r101-d16-mg124_512x1024_80k_cityscapes_20200908_005644-ee6158e0.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d16-mg124_512x1024_80k_cityscapes/deeplabv3plus_r101-d16-mg124_512x1024_80k_cityscapes-20200908_005644.log.json) |
| DeepLabV3+ | R-18b-D8 | 512x1024 | 80000 | 2.1 | 14.95 | 75.87 | 77.52 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r18b-d8_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18b-d8_512x1024_80k_cityscapes/deeplabv3plus_r18b-d8_512x1024_80k_cityscapes_20201226_090828-e451abd9.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18b-d8_512x1024_80k_cityscapes/deeplabv3plus_r18b-d8_512x1024_80k_cityscapes-20201226_090828.log.json) |
| DeepLabV3+ | R-50b-D8 | 512x1024 | 80000 | 7.4 | 3.94 | 80.28 | 81.44 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r50b-d8_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50b-d8_512x1024_80k_cityscapes/deeplabv3plus_r50b-d8_512x1024_80k_cityscapes_20201225_213645-a97e4e43.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50b-d8_512x1024_80k_cityscapes/deeplabv3plus_r50b-d8_512x1024_80k_cityscapes-20201225_213645.log.json) |
| DeepLabV3+ | R-101b-D8 | 512x1024 | 80000 | 10.9 | 2.60 | 80.16 | 81.41 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r101b-d8_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101b-d8_512x1024_80k_cityscapes/deeplabv3plus_r101b-d8_512x1024_80k_cityscapes_20201226_190843-9c3c93a4.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101b-d8_512x1024_80k_cityscapes/deeplabv3plus_r101b-d8_512x1024_80k_cityscapes-20201226_190843.log.json) |
| DeepLabV3+ | R-18b-D8 | 769x769 | 80000 | 2.4 | 5.96 | 76.36 | 78.24 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r18b-d8_769x769_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18b-d8_769x769_80k_cityscapes/deeplabv3plus_r18b-d8_769x769_80k_cityscapes_20201226_151312-2c868aff.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18b-d8_769x769_80k_cityscapes/deeplabv3plus_r18b-d8_769x769_80k_cityscapes-20201226_151312.log.json) |
| DeepLabV3+ | R-50b-D8 | 769x769 | 80000 | 8.4 | 1.72 | 79.41 | 80.56 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r50b-d8_769x769_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50b-d8_769x769_80k_cityscapes/deeplabv3plus_r50b-d8_769x769_80k_cityscapes_20201225_224655-8b596d1c.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50b-d8_769x769_80k_cityscapes/deeplabv3plus_r50b-d8_769x769_80k_cityscapes-20201225_224655.log.json) |
| DeepLabV3+ | R-101b-D8 | 769x769 | 80000 | 12.3 | 1.10 | 79.88 | 81.46 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r101b-d8_769x769_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101b-d8_769x769_80k_cityscapes/deeplabv3plus_r101b-d8_769x769_80k_cityscapes_20201226_205041-227cdf7c.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101b-d8_769x769_80k_cityscapes/deeplabv3plus_r101b-d8_769x769_80k_cityscapes-20201226_205041.log.json) |
\[1\] The training of the model is sensitive to random seed, and the seed to train it is 1111.
### ADE20K
| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download |
| ---------- | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| DeepLabV3+ | R-50-D8 | 512x512 | 80000 | 10.6 | 21.01 | 42.72 | 43.75 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r50-d8_512x512_80k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_80k_ade20k/deeplabv3plus_r50-d8_512x512_80k_ade20k_20200614_185028-bf1400d8.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_80k_ade20k/deeplabv3plus_r50-d8_512x512_80k_ade20k_20200614_185028.log.json) |
| DeepLabV3+ | R-101-D8 | 512x512 | 80000 | 14.1 | 14.16 | 44.60 | 46.06 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r101-d8_512x512_80k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_80k_ade20k/deeplabv3plus_r101-d8_512x512_80k_ade20k_20200615_014139-d5730af7.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_80k_ade20k/deeplabv3plus_r101-d8_512x512_80k_ade20k_20200615_014139.log.json) |
| DeepLabV3+ | R-50-D8 | 512x512 | 160000 | - | - | 43.95 | 44.93 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r50-d8_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_160k_ade20k/deeplabv3plus_r50-d8_512x512_160k_ade20k_20200615_124504-6135c7e0.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_160k_ade20k/deeplabv3plus_r50-d8_512x512_160k_ade20k_20200615_124504.log.json) |
| DeepLabV3+ | R-101-D8 | 512x512 | 160000 | - | - | 45.47 | 46.35 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r101-d8_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_160k_ade20k/deeplabv3plus_r101-d8_512x512_160k_ade20k_20200615_123232-38ed86bb.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_160k_ade20k/deeplabv3plus_r101-d8_512x512_160k_ade20k_20200615_123232.log.json) |
### Pascal VOC 2012 + Aug
| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download |
| ---------- | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | -------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| DeepLabV3+ | R-50-D8 | 512x512 | 20000 | 7.6 | 21 | 75.93 | 77.50 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r50-d8_512x512_20k_voc12aug.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_20k_voc12aug/deeplabv3plus_r50-d8_512x512_20k_voc12aug_20200617_102323-aad58ef1.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_20k_voc12aug/deeplabv3plus_r50-d8_512x512_20k_voc12aug_20200617_102323.log.json) |
| DeepLabV3+ | R-101-D8 | 512x512 | 20000 | 11 | 13.88 | 77.22 | 78.59 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r101-d8_512x512_20k_voc12aug.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_20k_voc12aug/deeplabv3plus_r101-d8_512x512_20k_voc12aug_20200617_102345-c7ff3d56.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_20k_voc12aug/deeplabv3plus_r101-d8_512x512_20k_voc12aug_20200617_102345.log.json) |
| DeepLabV3+ | R-50-D8 | 512x512 | 40000 | - | - | 76.81 | 77.57 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r50-d8_512x512_40k_voc12aug.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_40k_voc12aug/deeplabv3plus_r50-d8_512x512_40k_voc12aug_20200613_161759-e1b43aa9.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_40k_voc12aug/deeplabv3plus_r50-d8_512x512_40k_voc12aug_20200613_161759.log.json) |
| DeepLabV3+ | R-101-D8 | 512x512 | 40000 | - | - | 78.62 | 79.53 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r101-d8_512x512_40k_voc12aug.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_40k_voc12aug/deeplabv3plus_r101-d8_512x512_40k_voc12aug_20200613_205333-faf03387.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_40k_voc12aug/deeplabv3plus_r101-d8_512x512_40k_voc12aug_20200613_205333.log.json) |
### Pascal Context
| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download |
| ---------- | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | -------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| DeepLabV3+ | R-101-D8 | 480x480 | 40000 | - | 9.09 | 47.30 | 48.47 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r101-d8_480x480_40k_pascal_context.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_480x480_40k_pascal_context/deeplabv3plus_r101-d8_480x480_40k_pascal_context_20200911_165459-d3c8a29e.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_480x480_40k_pascal_context/deeplabv3plus_r101-d8_480x480_40k_pascal_context-20200911_165459.log.json) |
| DeepLabV3+ | R-101-D8 | 480x480 | 80000 | - | - | 47.23 | 48.26 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r101-d8_480x480_80k_pascal_context.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_480x480_80k_pascal_context/deeplabv3plus_r101-d8_480x480_80k_pascal_context_20200911_155322-145d3ee8.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_480x480_80k_pascal_context/deeplabv3plus_r101-d8_480x480_80k_pascal_context-20200911_155322.log.json) |
### Pascal Context 59
| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download |
| ---------- | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ----------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| DeepLabV3+ | R-101-D8 | 480x480 | 40000 | - | - | 52.86 | 54.54 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r101-d8_480x480_40k_pascal_context_59.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_480x480_40k_pascal_context_59/deeplabv3plus_r101-d8_480x480_40k_pascal_context_59_20210416_111233-ed937f15.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_480x480_40k_pascal_context_59/deeplabv3plus_r101-d8_480x480_40k_pascal_context_59-20210416_111233.log.json) |
| DeepLabV3+ | R-101-D8 | 480x480 | 80000 | - | - | 53.2 | 54.67 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r101-d8_480x480_80k_pascal_context_59.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_480x480_80k_pascal_context_59/deeplabv3plus_r101-d8_480x480_80k_pascal_context_59_20210416_111127-7ca0331d.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_480x480_80k_pascal_context_59/deeplabv3plus_r101-d8_480x480_80k_pascal_context_59-20210416_111127.log.json) |
### LoveDA
| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download |
| ---------- | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ------------------------------------------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| DeepLabV3+ | R-18-D8 | 512x512 | 80000 | 1.93 | 25.57 | 50.28 | 50.47 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r18-d8_512x512_80k_loveda.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_512x512_80k_loveda/deeplabv3plus_r18-d8_512x512_80k_loveda_20211104_132800-ce0fa0ca.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_512x512_80k_loveda/deeplabv3plus_r18-d8_512x512_80k_loveda_20211104_132800.log.json) |
| DeepLabV3+ | R-50-D8 | 512x512 | 80000 | 7.37 | 6.00 | 50.99 | 50.65 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r50-d8_512x512_80k_loveda.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_80k_loveda/deeplabv3plus_r50-d8_512x512_80k_loveda_20211105_080442-f0720392.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_80k_loveda/deeplabv3plus_r50-d8_512x512_80k_loveda_20211105_080442.log.json) |
| DeepLabV3+ | R-101-D8 | 512x512 | 80000 | 10.84 | 4.33 | 51.47 | 51.32 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r101-d8_512x512_80k_loveda.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_80k_loveda/deeplabv3plus_r101-d8_512x512_80k_loveda_20211105_110759-4c1f297e.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_80k_loveda/deeplabv3plus_r101-d8_512x512_80k_loveda_20211105_110759.log.json) |
### Potsdam
| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download |
| ---------- | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| DeepLabV3+ | R-18-D8 | 512x512 | 80000 | 1.91 | 81.68 | 77.09 | 78.44 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r18-d8_512x512_80k_potsdam.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_512x512_80k_potsdam/deeplabv3plus_r18-d8_512x512_80k_potsdam_20211219_020601-75fd5bc3.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_512x512_80k_potsdam/deeplabv3plus_r18-d8_512x512_80k_potsdam_20211219_020601.log.json) |
| DeepLabV3+ | R-50-D8 | 512x512 | 80000 | 7.36 | 26.44 | 78.33 | 79.27 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r50-d8_512x512_80k_potsdam.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_80k_potsdam/deeplabv3plus_r50-d8_512x512_80k_potsdam_20211219_031508-7e7a2b24.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_80k_potsdam/deeplabv3plus_r50-d8_512x512_80k_potsdam_20211219_031508.log.json) |
| DeepLabV3+ | R-101-D8 | 512x512 | 80000 | 10.83 | 17.56 | 78.7 | 79.47 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r101-d8_512x512_80k_potsdam.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_80k_potsdam/deeplabv3plus_r101-d8_512x512_80k_potsdam_20211219_031508-8b112708.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_80k_potsdam/deeplabv3plus_r101-d8_512x512_80k_potsdam_20211219_031508.log.json) |
### Vaihingen
| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download |
| ---------- | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| DeepLabV3+ | R-18-D8 | 512x512 | 80000 | 1.91 | 72.79 | 72.50 | 74.13 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r18-d8_4x4_512x512_80k_vaihingen.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_4x4_512x512_80k_vaihingen/deeplabv3plus_r18-d8_4x4_512x512_80k_vaihingen_20211231_230805-7626a263.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_4x4_512x512_80k_vaihingen/deeplabv3plus_r18-d8_4x4_512x512_80k_vaihingen_20211231_230805.log.json) |
| DeepLabV3+ | R-50-D8 | 512x512 | 80000 | 7.36 | 26.91 | 73.97 | 75.05 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r50-d8_4x4_512x512_80k_vaihingen.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_4x4_512x512_80k_vaihingen/deeplabv3plus_r50-d8_4x4_512x512_80k_vaihingen_20211231_230816-5040938d.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_4x4_512x512_80k_vaihingen/deeplabv3plus_r50-d8_4x4_512x512_80k_vaihingen_20211231_230816.log.json) |
| DeepLabV3+ | R-101-D8 | 512x512 | 80000 | 10.83 | 18.59 | 73.06 | 74.14 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r101-d8_4x4_512x512_80k_vaihingen.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_4x4_512x512_80k_vaihingen/deeplabv3plus_r101-d8_4x4_512x512_80k_vaihingen_20211231_230816-8a095afa.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_4x4_512x512_80k_vaihingen/deeplabv3plus_r101-d8_4x4_512x512_80k_vaihingen_20211231_230816.log.json) |
### iSAID
| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download |
| ---------- | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | -------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| DeepLabV3+ | R-18-D8 | 896x896 | 80000 | 6.19 | 24.81 | 61.35 | 62.61 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r18-d8_4x4_896x896_80k_isaid.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_4x4_896x896_80k_isaid/deeplabv3plus_r18-d8_4x4_896x896_80k_isaid_20220110_180526-7059991d.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_4x4_896x896_80k_isaid/deeplabv3plus_r18-d8_4x4_896x896_80k_isaid_20220110_180526.log.json) |
| DeepLabV3+ | R-50-D8 | 896x896 | 80000 | 21.45 | 8.42 | 67.06 | 68.02 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r50-d8_4x4_896x896_80k_isaid.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_4x4_896x896_80k_isaid/deeplabv3plus_r50-d8_4x4_896x896_80k_isaid_20220110_180526-598be439.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_4x4_896x896_80k_isaid/deeplabv3plus_r50-d8_4x4_896x896_80k_isaid_20220110_180526.log.json) |
Note:
- `D-8`/`D-16` here corresponding to the output stride 8/16 setting for DeepLab series.
- `MG-124` stands for multi-grid dilation in the last stage of ResNet.
- `FP16` means Mixed Precision (FP16) is adopted in training.
- `896x896` is the Crop Size of iSAID dataset, which is followed by the implementation of [PointFlow: Flowing Semantics Through Points for Aerial Image Segmentation](https://arxiv.org/pdf/2103.06564.pdf)
| 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 Convolution for Semantic Image Segmentation
README: configs/deeplabv3plus/README.md
Code:
URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/sep_aspp_head.py#L30
Version: v0.17.0
Converted From:
Code: https://github.com/tensorflow/models/tree/master/research/deeplab
Models:
- Name: deeplabv3plus_r50-d8_512x1024_40k_cityscapes
In Collection: DeepLabV3+
Metadata:
backbone: R-50-D8
crop size: (512,1024)
lr schd: 40000
inference time (ms/im):
- value: 253.81
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,1024)
Training Memory (GB): 7.5
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.61
mIoU(ms+flip): 81.01
Config: configs/deeplabv3plus/deeplabv3plus_r50-d8_512x1024_40k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x1024_40k_cityscapes/deeplabv3plus_r50-d8_512x1024_40k_cityscapes_20200605_094610-d222ffcd.pth
- Name: deeplabv3plus_r101-d8_512x1024_40k_cityscapes
In Collection: DeepLabV3+
Metadata:
backbone: R-101-D8
crop size: (512,1024)
lr schd: 40000
inference time (ms/im):
- value: 384.62
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,1024)
Training Memory (GB): 11.0
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 80.21
mIoU(ms+flip): 81.82
Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_512x1024_40k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x1024_40k_cityscapes/deeplabv3plus_r101-d8_512x1024_40k_cityscapes_20200605_094614-3769eecf.pth
- Name: deeplabv3plus_r50-d8_769x769_40k_cityscapes
In Collection: DeepLabV3+
Metadata:
backbone: R-50-D8
crop size: (769,769)
lr schd: 40000
inference time (ms/im):
- value: 581.4
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (769,769)
Training Memory (GB): 8.5
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 78.97
mIoU(ms+flip): 80.46
Config: configs/deeplabv3plus/deeplabv3plus_r50-d8_769x769_40k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_769x769_40k_cityscapes/deeplabv3plus_r50-d8_769x769_40k_cityscapes_20200606_114143-1dcb0e3c.pth
- Name: deeplabv3plus_r101-d8_769x769_40k_cityscapes
In Collection: DeepLabV3+
Metadata:
backbone: R-101-D8
crop size: (769,769)
lr schd: 40000
inference time (ms/im):
- value: 869.57
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (769,769)
Training Memory (GB): 12.5
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.46
mIoU(ms+flip): 80.5
Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_769x769_40k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_769x769_40k_cityscapes/deeplabv3plus_r101-d8_769x769_40k_cityscapes_20200606_114304-ff414b9e.pth
- Name: deeplabv3plus_r18-d8_512x1024_80k_cityscapes
In Collection: DeepLabV3+
Metadata:
backbone: R-18-D8
crop size: (512,1024)
lr schd: 80000
inference time (ms/im):
- value: 70.08
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,1024)
Training Memory (GB): 2.2
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 76.89
mIoU(ms+flip): 78.76
Config: configs/deeplabv3plus/deeplabv3plus_r18-d8_512x1024_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_512x1024_80k_cityscapes/deeplabv3plus_r18-d8_512x1024_80k_cityscapes_20201226_080942-cff257fe.pth
- Name: deeplabv3plus_r50-d8_512x1024_80k_cityscapes
In Collection: DeepLabV3+
Metadata:
backbone: R-50-D8
crop size: (512,1024)
lr schd: 80000
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 80.09
mIoU(ms+flip): 81.13
Config: configs/deeplabv3plus/deeplabv3plus_r50-d8_512x1024_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x1024_80k_cityscapes/deeplabv3plus_r50-d8_512x1024_80k_cityscapes_20200606_114049-f9fb496d.pth
- Name: deeplabv3plus_r101-d8_512x1024_80k_cityscapes
In Collection: DeepLabV3+
Metadata:
backbone: R-101-D8
crop size: (512,1024)
lr schd: 80000
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 80.97
mIoU(ms+flip): 82.03
Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_512x1024_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x1024_80k_cityscapes/deeplabv3plus_r101-d8_512x1024_80k_cityscapes_20200606_114143-068fcfe9.pth
- Name: deeplabv3plus_r101-d8_fp16_512x1024_80k_cityscapes
In Collection: DeepLabV3+
Metadata:
backbone: R-101-D8
crop size: (512,1024)
lr schd: 80000
inference time (ms/im):
- value: 127.06
hardware: V100
backend: PyTorch
batch size: 1
mode: FP16
resolution: (512,1024)
Training Memory (GB): 6.35
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 80.46
Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_fp16_512x1024_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_fp16_512x1024_80k_cityscapes/deeplabv3plus_r101-d8_fp16_512x1024_80k_cityscapes_20200717_230920-f1104f4b.pth
- Name: deeplabv3plus_r18-d8_769x769_80k_cityscapes
In Collection: DeepLabV3+
Metadata:
backbone: R-18-D8
crop size: (769,769)
lr schd: 80000
inference time (ms/im):
- value: 174.22
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (769,769)
Training Memory (GB): 2.5
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 76.26
mIoU(ms+flip): 77.91
Config: configs/deeplabv3plus/deeplabv3plus_r18-d8_769x769_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_769x769_80k_cityscapes/deeplabv3plus_r18-d8_769x769_80k_cityscapes_20201226_083346-f326e06a.pth
- Name: deeplabv3plus_r50-d8_769x769_80k_cityscapes
In Collection: DeepLabV3+
Metadata:
backbone: R-50-D8
crop size: (769,769)
lr schd: 80000
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.83
mIoU(ms+flip): 81.48
Config: configs/deeplabv3plus/deeplabv3plus_r50-d8_769x769_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_769x769_80k_cityscapes/deeplabv3plus_r50-d8_769x769_80k_cityscapes_20200606_210233-0e9dfdc4.pth
- Name: deeplabv3plus_r101-d8_769x769_80k_cityscapes
In Collection: DeepLabV3+
Metadata:
backbone: R-101-D8
crop size: (769,769)
lr schd: 80000
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 80.65
mIoU(ms+flip): 81.47
Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_769x769_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_769x769_80k_cityscapes/deeplabv3plus_r101-d8_769x769_80k_cityscapes_20220406_154720-dfcc0b68.pth
- Name: deeplabv3plus_r101-d16-mg124_512x1024_40k_cityscapes
In Collection: DeepLabV3+
Metadata:
backbone: R-101-D16-MG124
crop size: (512,1024)
lr schd: 40000
inference time (ms/im):
- value: 133.69
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,1024)
Training Memory (GB): 5.8
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.09
mIoU(ms+flip): 80.36
Config: configs/deeplabv3plus/deeplabv3plus_r101-d16-mg124_512x1024_40k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d16-mg124_512x1024_40k_cityscapes/deeplabv3plus_r101-d16-mg124_512x1024_40k_cityscapes_20200908_005644-cf9ce186.pth
- Name: deeplabv3plus_r101-d16-mg124_512x1024_80k_cityscapes
In Collection: DeepLabV3+
Metadata:
backbone: R-101-D16-MG124
crop size: (512,1024)
lr schd: 80000
Training Memory (GB): 9.9
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.9
mIoU(ms+flip): 81.33
Config: configs/deeplabv3plus/deeplabv3plus_r101-d16-mg124_512x1024_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d16-mg124_512x1024_80k_cityscapes/deeplabv3plus_r101-d16-mg124_512x1024_80k_cityscapes_20200908_005644-ee6158e0.pth
- Name: deeplabv3plus_r18b-d8_512x1024_80k_cityscapes
In Collection: DeepLabV3+
Metadata:
backbone: R-18b-D8
crop size: (512,1024)
lr schd: 80000
inference time (ms/im):
- value: 66.89
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,1024)
Training Memory (GB): 2.1
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 75.87
mIoU(ms+flip): 77.52
Config: configs/deeplabv3plus/deeplabv3plus_r18b-d8_512x1024_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18b-d8_512x1024_80k_cityscapes/deeplabv3plus_r18b-d8_512x1024_80k_cityscapes_20201226_090828-e451abd9.pth
- Name: deeplabv3plus_r50b-d8_512x1024_80k_cityscapes
In Collection: DeepLabV3+
Metadata:
backbone: R-50b-D8
crop size: (512,1024)
lr schd: 80000
inference time (ms/im):
- value: 253.81
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,1024)
Training Memory (GB): 7.4
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 80.28
mIoU(ms+flip): 81.44
Config: configs/deeplabv3plus/deeplabv3plus_r50b-d8_512x1024_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50b-d8_512x1024_80k_cityscapes/deeplabv3plus_r50b-d8_512x1024_80k_cityscapes_20201225_213645-a97e4e43.pth
- Name: deeplabv3plus_r101b-d8_512x1024_80k_cityscapes
In Collection: DeepLabV3+
Metadata:
backbone: R-101b-D8
crop size: (512,1024)
lr schd: 80000
inference time (ms/im):
- value: 384.62
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,1024)
Training Memory (GB): 10.9
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 80.16
mIoU(ms+flip): 81.41
Config: configs/deeplabv3plus/deeplabv3plus_r101b-d8_512x1024_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101b-d8_512x1024_80k_cityscapes/deeplabv3plus_r101b-d8_512x1024_80k_cityscapes_20201226_190843-9c3c93a4.pth
- Name: deeplabv3plus_r18b-d8_769x769_80k_cityscapes
In Collection: DeepLabV3+
Metadata:
backbone: R-18b-D8
crop size: (769,769)
lr schd: 80000
inference time (ms/im):
- value: 167.79
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (769,769)
Training Memory (GB): 2.4
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 76.36
mIoU(ms+flip): 78.24
Config: configs/deeplabv3plus/deeplabv3plus_r18b-d8_769x769_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18b-d8_769x769_80k_cityscapes/deeplabv3plus_r18b-d8_769x769_80k_cityscapes_20201226_151312-2c868aff.pth
- Name: deeplabv3plus_r50b-d8_769x769_80k_cityscapes
In Collection: DeepLabV3+
Metadata:
backbone: R-50b-D8
crop size: (769,769)
lr schd: 80000
inference time (ms/im):
- value: 581.4
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (769,769)
Training Memory (GB): 8.4
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.41
mIoU(ms+flip): 80.56
Config: configs/deeplabv3plus/deeplabv3plus_r50b-d8_769x769_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50b-d8_769x769_80k_cityscapes/deeplabv3plus_r50b-d8_769x769_80k_cityscapes_20201225_224655-8b596d1c.pth
- Name: deeplabv3plus_r101b-d8_769x769_80k_cityscapes
In Collection: DeepLabV3+
Metadata:
backbone: R-101b-D8
crop size: (769,769)
lr schd: 80000
inference time (ms/im):
- value: 909.09
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (769,769)
Training Memory (GB): 12.3
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.88
mIoU(ms+flip): 81.46
Config: configs/deeplabv3plus/deeplabv3plus_r101b-d8_769x769_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101b-d8_769x769_80k_cityscapes/deeplabv3plus_r101b-d8_769x769_80k_cityscapes_20201226_205041-227cdf7c.pth
- Name: deeplabv3plus_r50-d8_512x512_80k_ade20k
In Collection: DeepLabV3+
Metadata:
backbone: R-50-D8
crop size: (512,512)
lr schd: 80000
inference time (ms/im):
- value: 47.6
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 10.6
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 42.72
mIoU(ms+flip): 43.75
Config: configs/deeplabv3plus/deeplabv3plus_r50-d8_512x512_80k_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_80k_ade20k/deeplabv3plus_r50-d8_512x512_80k_ade20k_20200614_185028-bf1400d8.pth
- Name: deeplabv3plus_r101-d8_512x512_80k_ade20k
In Collection: DeepLabV3+
Metadata:
backbone: R-101-D8
crop size: (512,512)
lr schd: 80000
inference time (ms/im):
- value: 70.62
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 14.1
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 44.6
mIoU(ms+flip): 46.06
Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_512x512_80k_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_80k_ade20k/deeplabv3plus_r101-d8_512x512_80k_ade20k_20200615_014139-d5730af7.pth
- Name: deeplabv3plus_r50-d8_512x512_160k_ade20k
In Collection: DeepLabV3+
Metadata:
backbone: R-50-D8
crop size: (512,512)
lr schd: 160000
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 43.95
mIoU(ms+flip): 44.93
Config: configs/deeplabv3plus/deeplabv3plus_r50-d8_512x512_160k_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_160k_ade20k/deeplabv3plus_r50-d8_512x512_160k_ade20k_20200615_124504-6135c7e0.pth
- Name: deeplabv3plus_r101-d8_512x512_160k_ade20k
In Collection: DeepLabV3+
Metadata:
backbone: R-101-D8
crop size: (512,512)
lr schd: 160000
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 45.47
mIoU(ms+flip): 46.35
Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_512x512_160k_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_160k_ade20k/deeplabv3plus_r101-d8_512x512_160k_ade20k_20200615_123232-38ed86bb.pth
- Name: deeplabv3plus_r50-d8_512x512_20k_voc12aug
In Collection: DeepLabV3+
Metadata:
backbone: R-50-D8
crop size: (512,512)
lr schd: 20000
inference time (ms/im):
- value: 47.62
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 7.6
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 75.93
mIoU(ms+flip): 77.5
Config: configs/deeplabv3plus/deeplabv3plus_r50-d8_512x512_20k_voc12aug.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_20k_voc12aug/deeplabv3plus_r50-d8_512x512_20k_voc12aug_20200617_102323-aad58ef1.pth
- Name: deeplabv3plus_r101-d8_512x512_20k_voc12aug
In Collection: DeepLabV3+
Metadata:
backbone: R-101-D8
crop size: (512,512)
lr schd: 20000
inference time (ms/im):
- value: 72.05
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 11.0
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 77.22
mIoU(ms+flip): 78.59
Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_512x512_20k_voc12aug.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_20k_voc12aug/deeplabv3plus_r101-d8_512x512_20k_voc12aug_20200617_102345-c7ff3d56.pth
- Name: deeplabv3plus_r50-d8_512x512_40k_voc12aug
In Collection: DeepLabV3+
Metadata:
backbone: R-50-D8
crop size: (512,512)
lr schd: 40000
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 76.81
mIoU(ms+flip): 77.57
Config: configs/deeplabv3plus/deeplabv3plus_r50-d8_512x512_40k_voc12aug.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_40k_voc12aug/deeplabv3plus_r50-d8_512x512_40k_voc12aug_20200613_161759-e1b43aa9.pth
- Name: deeplabv3plus_r101-d8_512x512_40k_voc12aug
In Collection: DeepLabV3+
Metadata:
backbone: R-101-D8
crop size: (512,512)
lr schd: 40000
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 78.62
mIoU(ms+flip): 79.53
Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_512x512_40k_voc12aug.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_40k_voc12aug/deeplabv3plus_r101-d8_512x512_40k_voc12aug_20200613_205333-faf03387.pth
- Name: deeplabv3plus_r101-d8_480x480_40k_pascal_context
In Collection: DeepLabV3+
Metadata:
backbone: R-101-D8
crop size: (480,480)
lr schd: 40000
inference time (ms/im):
- value: 110.01
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (480,480)
Results:
- Task: Semantic Segmentation
Dataset: Pascal Context
Metrics:
mIoU: 47.3
mIoU(ms+flip): 48.47
Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_480x480_40k_pascal_context.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_480x480_40k_pascal_context/deeplabv3plus_r101-d8_480x480_40k_pascal_context_20200911_165459-d3c8a29e.pth
- Name: deeplabv3plus_r101-d8_480x480_80k_pascal_context
In Collection: DeepLabV3+
Metadata:
backbone: R-101-D8
crop size: (480,480)
lr schd: 80000
Results:
- Task: Semantic Segmentation
Dataset: Pascal Context
Metrics:
mIoU: 47.23
mIoU(ms+flip): 48.26
Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_480x480_80k_pascal_context.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_480x480_80k_pascal_context/deeplabv3plus_r101-d8_480x480_80k_pascal_context_20200911_155322-145d3ee8.pth
- Name: deeplabv3plus_r101-d8_480x480_40k_pascal_context_59
In Collection: DeepLabV3+
Metadata:
backbone: R-101-D8
crop size: (480,480)
lr schd: 40000
Results:
- Task: Semantic Segmentation
Dataset: Pascal Context 59
Metrics:
mIoU: 52.86
mIoU(ms+flip): 54.54
Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_480x480_40k_pascal_context_59.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_480x480_40k_pascal_context_59/deeplabv3plus_r101-d8_480x480_40k_pascal_context_59_20210416_111233-ed937f15.pth
- Name: deeplabv3plus_r101-d8_480x480_80k_pascal_context_59
In Collection: DeepLabV3+
Metadata:
backbone: R-101-D8
crop size: (480,480)
lr schd: 80000
Results:
- Task: Semantic Segmentation
Dataset: Pascal Context 59
Metrics:
mIoU: 53.2
mIoU(ms+flip): 54.67
Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_480x480_80k_pascal_context_59.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_480x480_80k_pascal_context_59/deeplabv3plus_r101-d8_480x480_80k_pascal_context_59_20210416_111127-7ca0331d.pth
- Name: deeplabv3plus_r18-d8_512x512_80k_loveda
In Collection: DeepLabV3+
Metadata:
backbone: R-18-D8
crop size: (512,512)
lr schd: 80000
inference time (ms/im):
- value: 39.11
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 1.93
Results:
- Task: Semantic Segmentation
Dataset: LoveDA
Metrics:
mIoU: 50.28
mIoU(ms+flip): 50.47
Config: configs/deeplabv3plus/deeplabv3plus_r18-d8_512x512_80k_loveda.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_512x512_80k_loveda/deeplabv3plus_r18-d8_512x512_80k_loveda_20211104_132800-ce0fa0ca.pth
- Name: deeplabv3plus_r50-d8_512x512_80k_loveda
In Collection: DeepLabV3+
Metadata:
backbone: R-50-D8
crop size: (512,512)
lr schd: 80000
inference time (ms/im):
- value: 166.67
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 7.37
Results:
- Task: Semantic Segmentation
Dataset: LoveDA
Metrics:
mIoU: 50.99
mIoU(ms+flip): 50.65
Config: configs/deeplabv3plus/deeplabv3plus_r50-d8_512x512_80k_loveda.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_80k_loveda/deeplabv3plus_r50-d8_512x512_80k_loveda_20211105_080442-f0720392.pth
- Name: deeplabv3plus_r101-d8_512x512_80k_loveda
In Collection: DeepLabV3+
Metadata:
backbone: R-101-D8
crop size: (512,512)
lr schd: 80000
inference time (ms/im):
- value: 230.95
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 10.84
Results:
- Task: Semantic Segmentation
Dataset: LoveDA
Metrics:
mIoU: 51.47
mIoU(ms+flip): 51.32
Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_512x512_80k_loveda.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_80k_loveda/deeplabv3plus_r101-d8_512x512_80k_loveda_20211105_110759-4c1f297e.pth
- Name: deeplabv3plus_r18-d8_512x512_80k_potsdam
In Collection: DeepLabV3+
Metadata:
backbone: R-18-D8
crop size: (512,512)
lr schd: 80000
inference time (ms/im):
- value: 12.24
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 1.91
Results:
- Task: Semantic Segmentation
Dataset: Potsdam
Metrics:
mIoU: 77.09
mIoU(ms+flip): 78.44
Config: configs/deeplabv3plus/deeplabv3plus_r18-d8_512x512_80k_potsdam.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_512x512_80k_potsdam/deeplabv3plus_r18-d8_512x512_80k_potsdam_20211219_020601-75fd5bc3.pth
- Name: deeplabv3plus_r50-d8_512x512_80k_potsdam
In Collection: DeepLabV3+
Metadata:
backbone: R-50-D8
crop size: (512,512)
lr schd: 80000
inference time (ms/im):
- value: 37.82
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 7.36
Results:
- Task: Semantic Segmentation
Dataset: Potsdam
Metrics:
mIoU: 78.33
mIoU(ms+flip): 79.27
Config: configs/deeplabv3plus/deeplabv3plus_r50-d8_512x512_80k_potsdam.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_80k_potsdam/deeplabv3plus_r50-d8_512x512_80k_potsdam_20211219_031508-7e7a2b24.pth
- Name: deeplabv3plus_r101-d8_512x512_80k_potsdam
In Collection: DeepLabV3+
Metadata:
backbone: R-101-D8
crop size: (512,512)
lr schd: 80000
inference time (ms/im):
- value: 56.95
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 10.83
Results:
- Task: Semantic Segmentation
Dataset: Potsdam
Metrics:
mIoU: 78.7
mIoU(ms+flip): 79.47
Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_512x512_80k_potsdam.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_80k_potsdam/deeplabv3plus_r101-d8_512x512_80k_potsdam_20211219_031508-8b112708.pth
- Name: deeplabv3plus_r18-d8_4x4_512x512_80k_vaihingen
In Collection: DeepLabV3+
Metadata:
backbone: R-18-D8
crop size: (512,512)
lr schd: 80000
inference time (ms/im):
- value: 13.74
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 1.91
Results:
- Task: Semantic Segmentation
Dataset: Vaihingen
Metrics:
mIoU: 72.5
mIoU(ms+flip): 74.13
Config: configs/deeplabv3plus/deeplabv3plus_r18-d8_4x4_512x512_80k_vaihingen.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_4x4_512x512_80k_vaihingen/deeplabv3plus_r18-d8_4x4_512x512_80k_vaihingen_20211231_230805-7626a263.pth
- Name: deeplabv3plus_r50-d8_4x4_512x512_80k_vaihingen
In Collection: DeepLabV3+
Metadata:
backbone: R-50-D8
crop size: (512,512)
lr schd: 80000
inference time (ms/im):
- value: 37.16
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 7.36
Results:
- Task: Semantic Segmentation
Dataset: Vaihingen
Metrics:
mIoU: 73.97
mIoU(ms+flip): 75.05
Config: configs/deeplabv3plus/deeplabv3plus_r50-d8_4x4_512x512_80k_vaihingen.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_4x4_512x512_80k_vaihingen/deeplabv3plus_r50-d8_4x4_512x512_80k_vaihingen_20211231_230816-5040938d.pth
- Name: deeplabv3plus_r101-d8_4x4_512x512_80k_vaihingen
In Collection: DeepLabV3+
Metadata:
backbone: R-101-D8
crop size: (512,512)
lr schd: 80000
inference time (ms/im):
- value: 53.79
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 10.83
Results:
- Task: Semantic Segmentation
Dataset: Vaihingen
Metrics:
mIoU: 73.06
mIoU(ms+flip): 74.14
Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_4x4_512x512_80k_vaihingen.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_4x4_512x512_80k_vaihingen/deeplabv3plus_r101-d8_4x4_512x512_80k_vaihingen_20211231_230816-8a095afa.pth
- Name: deeplabv3plus_r18-d8_4x4_896x896_80k_isaid
In Collection: DeepLabV3+
Metadata:
backbone: R-18-D8
crop size: (896,896)
lr schd: 80000
inference time (ms/im):
- value: 40.31
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (896,896)
Training Memory (GB): 6.19
Results:
- Task: Semantic Segmentation
Dataset: iSAID
Metrics:
mIoU: 61.35
mIoU(ms+flip): 62.61
Config: configs/deeplabv3plus/deeplabv3plus_r18-d8_4x4_896x896_80k_isaid.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_4x4_896x896_80k_isaid/deeplabv3plus_r18-d8_4x4_896x896_80k_isaid_20220110_180526-7059991d.pth
- Name: deeplabv3plus_r50-d8_4x4_896x896_80k_isaid
In Collection: DeepLabV3+
Metadata:
backbone: R-50-D8
crop size: (896,896)
lr schd: 80000
inference time (ms/im):
- value: 118.76
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (896,896)
Training Memory (GB): 21.45
Results:
- Task: Semantic Segmentation
Dataset: iSAID
Metrics:
mIoU: 67.06
mIoU(ms+flip): 68.02
Config: configs/deeplabv3plus/deeplabv3plus_r50-d8_4x4_896x896_80k_isaid.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_4x4_896x896_80k_isaid/deeplabv3plus_r50-d8_4x4_896x896_80k_isaid_20220110_180526-598be439.pth
| 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),
sampler=dict(type='OHEMPixelSampler', min_kept=100000)))
| 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),
sampler=dict(type='OHEMPixelSampler', min_kept=100000)))
| 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),
dilations=(1, 1, 2, 4),
strides=(1, 2, 1, 1),
norm_cfg=dict(type='SyncBN', requires_grad=True),
norm_eval=False,
style='pytorch',
contract_dilation=True),
decode_head=dict(
type='DepthwiseSeparableASPPHead',
in_channels=2048,
in_index=3,
channels=512,
dilations=(1, 12, 24, 36),
c1_in_channels=256,
c1_channels=48,
dropout_ratio=0.1,
num_classes=2,
norm_cfg=dict(type='SyncBN', requires_grad=True),
align_corners=False,
loss_decode=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
sampler=dict(type='OHEMPixelSampler', thresh=0.7, min_kept=10000)),
auxiliary_head=dict(
type='FCNHead',
in_channels=1024,
in_index=2,
channels=256,
num_convs=1,
concat_input=False,
dropout_ratio=0.1,
num_classes=2,
norm_cfg=dict(type='SyncBN', requires_grad=True),
align_corners=False,
loss_decode=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
train_cfg=dict(),
test_cfg=dict(mode='whole'))
log_config = dict(
interval=50, hooks=[dict(type='TextLoggerHook', by_epoch=False)])
dist_params = dict(backend='nccl')
log_level = 'INFO'
load_from = None
resume_from = None
workflow = [('train', 1)]
cudnn_benchmark = True
optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0005)
optimizer_config = dict()
lr_config = dict(policy='poly', power=0.9, min_lr=0.0001, by_epoch=False)
runner = dict(type='IterBasedRunner', max_iters=30000)
checkpoint_config = dict(by_epoch=False, interval=400)
evaluation = dict(
interval=400, metric=['mIoU', 'mDice', 'mFscore'], pre_eval=True)
auto_resume = False
| 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, channels=64))
| 332 | 26.75 | 62 | py |
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