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# ICNet
> [ICNet for Real-time Semantic Segmentation on High-resolution Images](https://arxiv.org/abs/1704.08545)
## Introduction
<!-- [ALGORITHM] -->
<a href="https://github.com/hszhao/ICNet">Official Repo</a>
<a href="https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/necks/ic_neck.py#L77">Code Snippet</a>
## Abstract
<!-- [ABSTRACT] -->
We focus on the challenging task of real-time semantic segmentation in this paper. It finds many practical applications and yet is with fundamental difficulty of reducing a large portion of computation for pixel-wise label inference. We propose an image cascade network (ICNet) that incorporates multi-resolution branches under proper label guidance to address this challenge. We provide in-depth analysis of our framework and introduce the cascade feature fusion unit to quickly achieve high-quality segmentation. Our system yields real-time inference on a single GPU card with decent quality results evaluated on challenging datasets like Cityscapes, CamVid and COCO-Stuff.
<!-- [IMAGE] -->
<div align=center>
<img src="https://user-images.githubusercontent.com/24582831/142901772-4570455d-7b27-44ae-a690-47dd9fde8445.png" width="70%"/>
</div>
## Results and models
### Cityscapes
| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device | mIoU | mIoU(ms+flip) | config | download |
| ---------------- | -------- | --------- | ------: | -------- | -------------- | ------ | ----: | ------------: | ------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| ICNet | R-18-D8 | 832x832 | 80000 | 1.70 | 27.12 | V100 | 68.14 | 70.16 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/icnet/icnet_r18-d8_4xb2-80k_cityscapes-832x832.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r18-d8_832x832_80k_cityscapes/icnet_r18-d8_832x832_80k_cityscapes_20210925_225521-2e36638d.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r18-d8_832x832_80k_cityscapes/icnet_r18-d8_832x832_80k_cityscapes_20210925_225521.log.json) |
| ICNet | R-18-D8 | 832x832 | 160000 | - | - | V100 | 71.64 | 74.18 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/icnet/icnet_r18-d8_4xb2-160k_cityscapes-832x832.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r18-d8_832x832_160k_cityscapes/icnet_r18-d8_832x832_160k_cityscapes_20210925_230153-2c6eb6e0.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r18-d8_832x832_160k_cityscapes/icnet_r18-d8_832x832_160k_cityscapes_20210925_230153.log.json) |
| ICNet (in1k-pre) | R-18-D8 | 832x832 | 80000 | - | - | V100 | 72.51 | 74.78 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/icnet/icnet_r18-d8-in1k-pre_4xb2-80k_cityscapes-832x832.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r18-d8_in1k-pre_832x832_80k_cityscapes/icnet_r18-d8_in1k-pre_832x832_80k_cityscapes_20210925_230354-1cbe3022.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r18-d8_in1k-pre_832x832_80k_cityscapes/icnet_r18-d8_in1k-pre_832x832_80k_cityscapes_20210925_230354.log.json) |
| ICNet (in1k-pre) | R-18-D8 | 832x832 | 160000 | - | - | V100 | 74.43 | 76.72 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/icnet/icnet_r18-d8-in1k-pre_4xb2-160k_cityscapes-832x832.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r18-d8_in1k-pre_832x832_160k_cityscapes/icnet_r18-d8_in1k-pre_832x832_160k_cityscapes_20210926_052702-619c8ae1.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r18-d8_in1k-pre_832x832_160k_cityscapes/icnet_r18-d8_in1k-pre_832x832_160k_cityscapes_20210926_052702.log.json) |
| ICNet | R-50-D8 | 832x832 | 80000 | 2.53 | 20.08 | V100 | 68.91 | 69.72 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/icnet/icnet_r50-d8_4xb2-80k_cityscapes-832x832.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r50-d8_832x832_80k_cityscapes/icnet_r50-d8_832x832_80k_cityscapes_20210926_044625-c6407341.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r50-d8_832x832_80k_cityscapes/icnet_r50-d8_832x832_80k_cityscapes_20210926_044625.log.json) |
| ICNet | R-50-D8 | 832x832 | 160000 | - | - | V100 | 73.82 | 75.67 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/icnet/icnet_r50-d8_4xb2-160k_cityscapes-832x832.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r50-d8_832x832_160k_cityscapes/icnet_r50-d8_832x832_160k_cityscapes_20210925_232612-a95f0d4e.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r50-d8_832x832_160k_cityscapes/icnet_r50-d8_832x832_160k_cityscapes_20210925_232612.log.json) |
| ICNet (in1k-pre) | R-50-D8 | 832x832 | 80000 | - | - | V100 | 74.58 | 76.41 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/icnet/icnet_r50-d8-in1k-pre_4xb2-80k_cityscapes-832x832.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r50-d8_in1k-pre_832x832_80k_cityscapes/icnet_r50-d8_in1k-pre_832x832_80k_cityscapes_20210926_032943-1743dc7b.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r50-d8_in1k-pre_832x832_80k_cityscapes/icnet_r50-d8_in1k-pre_832x832_80k_cityscapes_20210926_032943.log.json) |
| ICNet (in1k-pre) | R-50-D8 | 832x832 | 160000 | - | - | V100 | 76.29 | 78.09 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/icnet/icnet_r50-d8-in1k-pre_4xb2-160k_cityscapes-832x832.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r50-d8_in1k-pre_832x832_160k_cityscapes/icnet_r50-d8_in1k-pre_832x832_160k_cityscapes_20210926_042715-ce310aea.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r50-d8_in1k-pre_832x832_160k_cityscapes/icnet_r50-d8_in1k-pre_832x832_160k_cityscapes_20210926_042715.log.json) |
| ICNet | R-101-D8 | 832x832 | 80000 | 3.08 | 16.95 | V100 | 70.28 | 71.95 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/icnet/icnet_r101-d8_4xb2-80k_cityscapes-832x832.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r101-d8_832x832_80k_cityscapes/icnet_r101-d8_832x832_80k_cityscapes_20210926_072447-b52f936e.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r101-d8_832x832_80k_cityscapes/icnet_r101-d8_832x832_80k_cityscapes_20210926_072447.log.json) |
| ICNet | R-101-D8 | 832x832 | 160000 | - | - | V100 | 73.80 | 76.10 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/icnet/icnet_r101-d8_4xb2-160k_cityscapes-832x832.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r101-d8_832x832_160k_cityscapes/icnet_r101-d8_832x832_160k_cityscapes_20210926_092350-3a1ebf1a.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r101-d8_832x832_160k_cityscapes/icnet_r101-d8_832x832_160k_cityscapes_20210926_092350.log.json) |
| ICNet (in1k-pre) | R-101-D8 | 832x832 | 80000 | - | - | V100 | 75.57 | 77.86 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/icnet/icnet_r101-d8-in1k-pre_4xb2-80k_cityscapes-832x832.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r101-d8_in1k-pre_832x832_80k_cityscapes/icnet_r101-d8_in1k-pre_832x832_80k_cityscapes_20210926_020414-7ceb12c5.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r101-d8_in1k-pre_832x832_80k_cityscapes/icnet_r101-d8_in1k-pre_832x832_80k_cityscapes_20210926_020414.log.json) |
| ICNet (in1k-pre) | R-101-D8 | 832x832 | 160000 | - | - | V100 | 76.15 | 77.98 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/icnet/icnet_r101-d8-in1k-pre_4xb2-160k_cityscapes-832x832.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r101-d8_in1k-pre_832x832_160k_cityscapes/icnet_r101-d8_in1k-pre_832x832_160k_cityscapes_20210925_232612-9484ae8a.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r101-d8_in1k-pre_832x832_160k_cityscapes/icnet_r101-d8_in1k-pre_832x832_160k_cityscapes_20210925_232612.log.json) |
Note: `in1k-pre` means pretrained model is used.
## Citation
```bibtext
@inproceedings{zhao2018icnet,
title={Icnet for real-time semantic segmentation on high-resolution images},
author={Zhao, Hengshuang and Qi, Xiaojuan and Shen, Xiaoyong and Shi, Jianping and Jia, Jiaya},
booktitle={Proceedings of the European conference on computer vision (ECCV)},
pages={405--420},
year={2018}
}
```

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_base_ = './icnet_r50-d8_4xb2-160k_cityscapes-832x832.py'
model = dict(
backbone=dict(
backbone_cfg=dict(
depth=101,
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://resnet101_v1c'))))

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_base_ = './icnet_r50-d8_4xb2-80k_cityscapes-832x832.py'
model = dict(
backbone=dict(
backbone_cfg=dict(
depth=101,
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://resnet101_v1c'))))

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_base_ = './icnet_r50-d8_4xb2-160k_cityscapes-832x832.py'
model = dict(backbone=dict(backbone_cfg=dict(depth=101)))

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_base_ = './icnet_r50-d8_4xb2-80k_cityscapes-832x832.py'
model = dict(backbone=dict(backbone_cfg=dict(depth=101)))

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_base_ = './icnet_r50-d8_4xb2-160k_cityscapes-832x832.py'
model = dict(
backbone=dict(
layer_channels=(128, 512),
backbone_cfg=dict(
depth=18,
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://resnet18_v1c'))))

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_base_ = './icnet_r50-d8_4xb2-80k_cityscapes-832x832.py'
model = dict(
backbone=dict(
layer_channels=(128, 512),
backbone_cfg=dict(
depth=18,
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://resnet18_v1c'))))

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_base_ = './icnet_r50-d8_4xb2-160k_cityscapes-832x832.py'
model = dict(
backbone=dict(layer_channels=(128, 512), backbone_cfg=dict(depth=18)))

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_base_ = './icnet_r50-d8_4xb2-80k_cityscapes-832x832.py'
model = dict(
backbone=dict(layer_channels=(128, 512), backbone_cfg=dict(depth=18)))

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_base_ = './icnet_r50-d8_4xb2-160k_cityscapes-832x832.py'
model = dict(
backbone=dict(
backbone_cfg=dict(
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://resnet50_v1c'))))

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Seg_All_In_One_MMSeg/configs/icnet/icnet_r50-d8-in1k-pre_4xb2-80k_cityscapes-832x832.py Normal file
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_base_ = './icnet_r50-d8_4xb2-80k_cityscapes-832x832.py'
model = dict(
backbone=dict(
backbone_cfg=dict(
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://resnet50_v1c'))))

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_base_ = [
'../_base_/models/icnet_r50-d8.py',
'../_base_/datasets/cityscapes_832x832.py', '../_base_/default_runtime.py',
'../_base_/schedules/schedule_160k.py'
]
crop_size = (832, 832)
data_preprocessor = dict(size=crop_size)
model = dict(data_preprocessor=data_preprocessor)

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_base_ = [
'../_base_/models/icnet_r50-d8.py',
'../_base_/datasets/cityscapes_832x832.py', '../_base_/default_runtime.py',
'../_base_/schedules/schedule_80k.py'
]
crop_size = (832, 832)
data_preprocessor = dict(size=crop_size)
model = dict(data_preprocessor=data_preprocessor)

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Collections:
- Name: ICNet
License: Apache License 2.0
Metadata:
Training Data:
- Cityscapes
Paper:
Title: ICNet for Real-time Semantic Segmentation on High-resolution Images
URL: https://arxiv.org/abs/1704.08545
README: configs/icnet/README.md
Frameworks:
- PyTorch
Models:
- Name: icnet_r18-d8_4xb2-80k_cityscapes-832x832
In Collection: ICNet
Results:
Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 68.14
mIoU(ms+flip): 70.16
Config: configs/icnet/icnet_r18-d8_4xb2-80k_cityscapes-832x832.py
Metadata:
Training Data: Cityscapes
Batch Size: 8
Architecture:
- R-18-D8
- ICNet
Training Resources: 4x V100 GPUS
Memory (GB): 1.7
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r18-d8_832x832_80k_cityscapes/icnet_r18-d8_832x832_80k_cityscapes_20210925_225521-2e36638d.pth
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r18-d8_832x832_80k_cityscapes/icnet_r18-d8_832x832_80k_cityscapes_20210925_225521.log.json
Paper:
Title: ICNet for Real-time Semantic Segmentation on High-resolution Images
URL: https://arxiv.org/abs/1704.08545
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/necks/ic_neck.py#L77
Framework: PyTorch
- Name: icnet_r18-d8_4xb2-160k_cityscapes-832x832
In Collection: ICNet
Results:
Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 71.64
mIoU(ms+flip): 74.18
Config: configs/icnet/icnet_r18-d8_4xb2-160k_cityscapes-832x832.py
Metadata:
Training Data: Cityscapes
Batch Size: 8
Architecture:
- R-18-D8
- ICNet
Training Resources: 4x V100 GPUS
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r18-d8_832x832_160k_cityscapes/icnet_r18-d8_832x832_160k_cityscapes_20210925_230153-2c6eb6e0.pth
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r18-d8_832x832_160k_cityscapes/icnet_r18-d8_832x832_160k_cityscapes_20210925_230153.log.json
Paper:
Title: ICNet for Real-time Semantic Segmentation on High-resolution Images
URL: https://arxiv.org/abs/1704.08545
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/necks/ic_neck.py#L77
Framework: PyTorch
- Name: icnet_r18-d8-in1k-pre_4xb2-80k_cityscapes-832x832
In Collection: ICNet
Results:
Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 72.51
mIoU(ms+flip): 74.78
Config: configs/icnet/icnet_r18-d8-in1k-pre_4xb2-80k_cityscapes-832x832.py
Metadata:
Training Data: Cityscapes
Batch Size: 8
Architecture:
- R-18-D8
- ICNet
- (in1k-pre)
Training Resources: 4x V100 GPUS
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r18-d8_in1k-pre_832x832_80k_cityscapes/icnet_r18-d8_in1k-pre_832x832_80k_cityscapes_20210925_230354-1cbe3022.pth
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r18-d8_in1k-pre_832x832_80k_cityscapes/icnet_r18-d8_in1k-pre_832x832_80k_cityscapes_20210925_230354.log.json
Paper:
Title: ICNet for Real-time Semantic Segmentation on High-resolution Images
URL: https://arxiv.org/abs/1704.08545
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/necks/ic_neck.py#L77
Framework: PyTorch
- Name: icnet_r18-d8-in1k-pre_4xb2-160k_cityscapes-832x832
In Collection: ICNet
Results:
Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 74.43
mIoU(ms+flip): 76.72
Config: configs/icnet/icnet_r18-d8-in1k-pre_4xb2-160k_cityscapes-832x832.py
Metadata:
Training Data: Cityscapes
Batch Size: 8
Architecture:
- R-18-D8
- ICNet
- (in1k-pre)
Training Resources: 4x V100 GPUS
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r18-d8_in1k-pre_832x832_160k_cityscapes/icnet_r18-d8_in1k-pre_832x832_160k_cityscapes_20210926_052702-619c8ae1.pth
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r18-d8_in1k-pre_832x832_160k_cityscapes/icnet_r18-d8_in1k-pre_832x832_160k_cityscapes_20210926_052702.log.json
Paper:
Title: ICNet for Real-time Semantic Segmentation on High-resolution Images
URL: https://arxiv.org/abs/1704.08545
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/necks/ic_neck.py#L77
Framework: PyTorch
- Name: icnet_r50-d8_4xb2-80k_cityscapes-832x832
In Collection: ICNet
Results:
Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 68.91
mIoU(ms+flip): 69.72
Config: configs/icnet/icnet_r50-d8_4xb2-80k_cityscapes-832x832.py
Metadata:
Training Data: Cityscapes
Batch Size: 8
Architecture:
- R-50-D8
- ICNet
Training Resources: 4x V100 GPUS
Memory (GB): 2.53
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r50-d8_832x832_80k_cityscapes/icnet_r50-d8_832x832_80k_cityscapes_20210926_044625-c6407341.pth
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r50-d8_832x832_80k_cityscapes/icnet_r50-d8_832x832_80k_cityscapes_20210926_044625.log.json
Paper:
Title: ICNet for Real-time Semantic Segmentation on High-resolution Images
URL: https://arxiv.org/abs/1704.08545
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/necks/ic_neck.py#L77
Framework: PyTorch
- Name: icnet_r50-d8_4xb2-160k_cityscapes-832x832
In Collection: ICNet
Results:
Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 73.82
mIoU(ms+flip): 75.67
Config: configs/icnet/icnet_r50-d8_4xb2-160k_cityscapes-832x832.py
Metadata:
Training Data: Cityscapes
Batch Size: 8
Architecture:
- R-50-D8
- ICNet
Training Resources: 4x V100 GPUS
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r50-d8_832x832_160k_cityscapes/icnet_r50-d8_832x832_160k_cityscapes_20210925_232612-a95f0d4e.pth
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r50-d8_832x832_160k_cityscapes/icnet_r50-d8_832x832_160k_cityscapes_20210925_232612.log.json
Paper:
Title: ICNet for Real-time Semantic Segmentation on High-resolution Images
URL: https://arxiv.org/abs/1704.08545
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/necks/ic_neck.py#L77
Framework: PyTorch
- Name: icnet_r50-d8-in1k-pre_4xb2-80k_cityscapes-832x832
In Collection: ICNet
Results:
Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 74.58
mIoU(ms+flip): 76.41
Config: configs/icnet/icnet_r50-d8-in1k-pre_4xb2-80k_cityscapes-832x832.py
Metadata:
Training Data: Cityscapes
Batch Size: 8
Architecture:
- R-50-D8
- ICNet
- (in1k-pre)
Training Resources: 4x V100 GPUS
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r50-d8_in1k-pre_832x832_80k_cityscapes/icnet_r50-d8_in1k-pre_832x832_80k_cityscapes_20210926_032943-1743dc7b.pth
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r50-d8_in1k-pre_832x832_80k_cityscapes/icnet_r50-d8_in1k-pre_832x832_80k_cityscapes_20210926_032943.log.json
Paper:
Title: ICNet for Real-time Semantic Segmentation on High-resolution Images
URL: https://arxiv.org/abs/1704.08545
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/necks/ic_neck.py#L77
Framework: PyTorch
- Name: icnet_r50-d8-in1k-pre_4xb2-160k_cityscapes-832x832
In Collection: ICNet
Results:
Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 76.29
mIoU(ms+flip): 78.09
Config: configs/icnet/icnet_r50-d8-in1k-pre_4xb2-160k_cityscapes-832x832.py
Metadata:
Training Data: Cityscapes
Batch Size: 8
Architecture:
- R-50-D8
- ICNet
- (in1k-pre)
Training Resources: 4x V100 GPUS
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r50-d8_in1k-pre_832x832_160k_cityscapes/icnet_r50-d8_in1k-pre_832x832_160k_cityscapes_20210926_042715-ce310aea.pth
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r50-d8_in1k-pre_832x832_160k_cityscapes/icnet_r50-d8_in1k-pre_832x832_160k_cityscapes_20210926_042715.log.json
Paper:
Title: ICNet for Real-time Semantic Segmentation on High-resolution Images
URL: https://arxiv.org/abs/1704.08545
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/necks/ic_neck.py#L77
Framework: PyTorch
- Name: icnet_r101-d8_4xb2-80k_cityscapes-832x832
In Collection: ICNet
Results:
Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 70.28
mIoU(ms+flip): 71.95
Config: configs/icnet/icnet_r101-d8_4xb2-80k_cityscapes-832x832.py
Metadata:
Training Data: Cityscapes
Batch Size: 8
Architecture:
- R-101-D8
- ICNet
Training Resources: 4x V100 GPUS
Memory (GB): 3.08
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r101-d8_832x832_80k_cityscapes/icnet_r101-d8_832x832_80k_cityscapes_20210926_072447-b52f936e.pth
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r101-d8_832x832_80k_cityscapes/icnet_r101-d8_832x832_80k_cityscapes_20210926_072447.log.json
Paper:
Title: ICNet for Real-time Semantic Segmentation on High-resolution Images
URL: https://arxiv.org/abs/1704.08545
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/necks/ic_neck.py#L77
Framework: PyTorch
- Name: icnet_r101-d8_4xb2-160k_cityscapes-832x832
In Collection: ICNet
Results:
Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 73.8
mIoU(ms+flip): 76.1
Config: configs/icnet/icnet_r101-d8_4xb2-160k_cityscapes-832x832.py
Metadata:
Training Data: Cityscapes
Batch Size: 8
Architecture:
- R-101-D8
- ICNet
Training Resources: 4x V100 GPUS
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r101-d8_832x832_160k_cityscapes/icnet_r101-d8_832x832_160k_cityscapes_20210926_092350-3a1ebf1a.pth
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r101-d8_832x832_160k_cityscapes/icnet_r101-d8_832x832_160k_cityscapes_20210926_092350.log.json
Paper:
Title: ICNet for Real-time Semantic Segmentation on High-resolution Images
URL: https://arxiv.org/abs/1704.08545
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/necks/ic_neck.py#L77
Framework: PyTorch
- Name: icnet_r101-d8-in1k-pre_4xb2-80k_cityscapes-832x832
In Collection: ICNet
Results:
Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 75.57
mIoU(ms+flip): 77.86
Config: configs/icnet/icnet_r101-d8-in1k-pre_4xb2-80k_cityscapes-832x832.py
Metadata:
Training Data: Cityscapes
Batch Size: 8
Architecture:
- R-101-D8
- ICNet
- (in1k-pre)
Training Resources: 4x V100 GPUS
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r101-d8_in1k-pre_832x832_80k_cityscapes/icnet_r101-d8_in1k-pre_832x832_80k_cityscapes_20210926_020414-7ceb12c5.pth
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r101-d8_in1k-pre_832x832_80k_cityscapes/icnet_r101-d8_in1k-pre_832x832_80k_cityscapes_20210926_020414.log.json
Paper:
Title: ICNet for Real-time Semantic Segmentation on High-resolution Images
URL: https://arxiv.org/abs/1704.08545
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/necks/ic_neck.py#L77
Framework: PyTorch
- Name: icnet_r101-d8-in1k-pre_4xb2-160k_cityscapes-832x832
In Collection: ICNet
Results:
Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 76.15
mIoU(ms+flip): 77.98
Config: configs/icnet/icnet_r101-d8-in1k-pre_4xb2-160k_cityscapes-832x832.py
Metadata:
Training Data: Cityscapes
Batch Size: 8
Architecture:
- R-101-D8
- ICNet
- (in1k-pre)
Training Resources: 4x V100 GPUS
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r101-d8_in1k-pre_832x832_160k_cityscapes/icnet_r101-d8_in1k-pre_832x832_160k_cityscapes_20210925_232612-9484ae8a.pth
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r101-d8_in1k-pre_832x832_160k_cityscapes/icnet_r101-d8_in1k-pre_832x832_160k_cityscapes_20210925_232612.log.json
Paper:
Title: ICNet for Real-time Semantic Segmentation on High-resolution Images
URL: https://arxiv.org/abs/1704.08545
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/necks/ic_neck.py#L77
Framework: PyTorch

144
Seg_All_In_One_MMSeg/configs/icnet/my_icnet_r18_r18_Pre_g1-300e_val1_check10_publicdataset_autolaparo-512x512-no_testslide.py Normal file
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_base_ = [
'../_base_/models/icnet_r50-d8.py',
'../_base_/datasets/publicdataset_autolaparo.py',
'../_base_/default_runtime.py',
'../_base_/schedules/schedule_300e_val1_check10.py',
]
norm_cfg = dict(
type='BN',
)
crop_size = (512, 512)
data_preprocessor = dict(
size=(512, 512),
mean=[
123.62464353460942,
85.34836259209033,
82.31539425671558,
],
std=[
47.172211618459315,
47.08256715323592,
48.135121265163605,
],
bgr_to_rgb=False,
)
model = dict(
backbone=dict(
backbone_cfg=dict(
depth=18,
init_cfg=dict(
type='Pretrained',
checkpoint='./My_Local_Model/open_mmlab/resnet18_v1c.pth',
),
),
layer_channels=(128, 512),
norm_cfg=dict(
type='BN',
),
align_corners=False,
),
data_preprocessor=dict(
size=(512, 512),
mean=[
123.62464353460942,
85.34836259209033,
82.31539425671558,
],
std=[
47.172211618459315,
47.08256715323592,
48.135121265163605,
],
bgr_to_rgb=False,
),
decode_head=dict(
num_classes=10,
loss_decode=dict(
type='DiceLoss',
use_sigmoid=False,
loss_weight=1.0,
),
align_corners=False,
),
auxiliary_head=[
dict(
type='FCNHead',
in_channels=128,
channels=128,
num_convs=1,
num_classes=10,
in_index=0,
norm_cfg=dict(
type='SyncBN',
requires_grad=True,
),
concat_input=False,
align_corners=False,
loss_decode=dict(
type='DiceLoss',
use_sigmoid=False,
loss_weight=0.4,
),
),
dict(
type='FCNHead',
in_channels=128,
channels=128,
num_convs=1,
num_classes=10,
in_index=1,
norm_cfg=dict(
type='SyncBN',
requires_grad=True,
),
concat_input=False,
align_corners=False,
loss_decode=dict(
type='DiceLoss',
use_sigmoid=False,
loss_weight=0.4,
),
),
],
)
test_cfg = dict(
crop_size=(512, 512),
)
optim_wrapper = dict(
type='OptimWrapper',
_delete_=True,
optimizer=dict(
type='AdamW',
lr=0.0001,
weight_decay=0.0005,
),
clip_grad=dict(
max_norm=1,
norm_type=2,
),
)
param_scheduler = [
dict(
type='LinearLR',
start_factor=1e-06,
by_epoch=True,
begin=0,
end=10,
),
dict(
type='PolyLR',
power=0.9,
begin=10,
end=300,
eta_min=1e-05,
by_epoch=True,
),
]

144
Seg_All_In_One_MMSeg/configs/icnet/my_icnet_r18_r18_Pre_g1-300e_val1_check10_publicdataset_cholecseg8k-512x512-no_testslide.py Normal file
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_base_ = [
'../_base_/models/icnet_r50-d8.py',
'../_base_/datasets/publicdataset_cholecseg8k.py',
'../_base_/default_runtime.py',
'../_base_/schedules/schedule_300e_val1_check10.py',
]
norm_cfg = dict(
type='BN',
)
crop_size = (512, 512)
data_preprocessor = dict(
size=(512, 512),
mean=[
85.65740418979115,
53.99282220050495,
46.074045888534535,
],
std=[
72.24589167201978,
56.76979155397199,
49.056637115061775,
],
bgr_to_rgb=False,
)
model = dict(
backbone=dict(
backbone_cfg=dict(
depth=18,
init_cfg=dict(
type='Pretrained',
checkpoint='./My_Local_Model/open_mmlab/resnet18_v1c.pth',
),
),
layer_channels=(128, 512),
norm_cfg=dict(
type='BN',
),
align_corners=False,
),
data_preprocessor=dict(
size=(512, 512),
mean=[
85.65740418979115,
53.99282220050495,
46.074045888534535,
],
std=[
72.24589167201978,
56.76979155397199,
49.056637115061775,
],
bgr_to_rgb=False,
),
decode_head=dict(
num_classes=13,
loss_decode=dict(
type='DiceLoss',
use_sigmoid=False,
loss_weight=1.0,
),
align_corners=False,
),
auxiliary_head=[
dict(
type='FCNHead',
in_channels=128,
channels=128,
num_convs=1,
num_classes=13,
in_index=0,
norm_cfg=dict(
type='SyncBN',
requires_grad=True,
),
concat_input=False,
align_corners=False,
loss_decode=dict(
type='DiceLoss',
use_sigmoid=False,
loss_weight=0.4,
),
),
dict(
type='FCNHead',
in_channels=128,
channels=128,
num_convs=1,
num_classes=13,
in_index=1,
norm_cfg=dict(
type='SyncBN',
requires_grad=True,
),
concat_input=False,
align_corners=False,
loss_decode=dict(
type='DiceLoss',
use_sigmoid=False,
loss_weight=0.4,
),
),
],
)
test_cfg = dict(
crop_size=(512, 512),
)
optim_wrapper = dict(
type='OptimWrapper',
_delete_=True,
optimizer=dict(
type='AdamW',
lr=0.0001,
weight_decay=0.0005,
),
clip_grad=dict(
max_norm=1,
norm_type=2,
),
)
param_scheduler = [
dict(
type='LinearLR',
start_factor=1e-06,
by_epoch=True,
begin=0,
end=10,
),
dict(
type='PolyLR',
power=0.9,
begin=10,
end=300,
eta_min=1e-05,
by_epoch=True,
),
]

144
Seg_All_In_One_MMSeg/configs/icnet/my_icnet_r18_r18_Pre_g1-300e_val1_check10_publicdataset_dresden-512x512-no_testslide.py Normal file
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_base_ = [
'../_base_/models/icnet_r50-d8.py',
'../_base_/datasets/publicdataset_dresden.py',
'../_base_/default_runtime.py',
'../_base_/schedules/schedule_300e_val1_check10.py',
]
norm_cfg = dict(
type='BN',
)
crop_size = (512, 512)
data_preprocessor = dict(
size=(512, 512),
mean=[
103.172638338208,
61.44762740851152,
51.407770213021976,
],
std=[
75.77031253622098,
54.63616729031377,
49.45572239497569,
],
bgr_to_rgb=False,
)
model = dict(
backbone=dict(
backbone_cfg=dict(
depth=18,
init_cfg=dict(
type='Pretrained',
checkpoint='./My_Local_Model/open_mmlab/resnet18_v1c.pth',
),
),
layer_channels=(128, 512),
norm_cfg=dict(
type='BN',
),
align_corners=False,
),
data_preprocessor=dict(
size=(512, 512),
mean=[
103.172638338208,
61.44762740851152,
51.407770213021976,
],
std=[
75.77031253622098,
54.63616729031377,
49.45572239497569,
],
bgr_to_rgb=False,
),
decode_head=dict(
num_classes=11,
loss_decode=dict(
type='DiceLoss',
use_sigmoid=False,
loss_weight=1.0,
),
align_corners=False,
),
auxiliary_head=[
dict(
type='FCNHead',
in_channels=128,
channels=128,
num_convs=1,
num_classes=11,
in_index=0,
norm_cfg=dict(
type='SyncBN',
requires_grad=True,
),
concat_input=False,
align_corners=False,
loss_decode=dict(
type='DiceLoss',
use_sigmoid=False,
loss_weight=0.4,
),
),
dict(
type='FCNHead',
in_channels=128,
channels=128,
num_convs=1,
num_classes=11,
in_index=1,
norm_cfg=dict(
type='SyncBN',
requires_grad=True,
),
concat_input=False,
align_corners=False,
loss_decode=dict(
type='DiceLoss',
use_sigmoid=False,
loss_weight=0.4,
),
),
],
)
test_cfg = dict(
crop_size=(512, 512),
)
optim_wrapper = dict(
type='OptimWrapper',
_delete_=True,
optimizer=dict(
type='AdamW',
lr=0.0001,
weight_decay=0.0005,
),
clip_grad=dict(
max_norm=1,
norm_type=2,
),
)
param_scheduler = [
dict(
type='LinearLR',
start_factor=1e-06,
by_epoch=True,
begin=0,
end=10,
),
dict(
type='PolyLR',
power=0.9,
begin=10,
end=300,
eta_min=1e-05,
by_epoch=True,
),
]

144
Seg_All_In_One_MMSeg/configs/icnet/my_icnet_r18_r18_Pre_g1-300e_val1_check10_publicdataset_endovis_2017-512x512-no_testslide.py Normal file
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@@ -0,0 +1,144 @@
_base_ = [
'../_base_/models/icnet_r50-d8.py',
'../_base_/datasets/publicdataset_endovis_2017.py',
'../_base_/default_runtime.py',
'../_base_/schedules/schedule_300e_val1_check10.py',
]
norm_cfg = dict(
type='BN',
)
crop_size = (512, 512)
data_preprocessor = dict(
size=(512, 512),
mean=[
122.21429912990676,
77.0821859677977,
87.03836664626716,
],
std=[
50.53335800365262,
42.895340354037465,
47.739426483390446,
],
bgr_to_rgb=False,
)
model = dict(
backbone=dict(
backbone_cfg=dict(
depth=18,
init_cfg=dict(
type='Pretrained',
checkpoint='./My_Local_Model/open_mmlab/resnet18_v1c.pth',
),
),
layer_channels=(128, 512),
norm_cfg=dict(
type='BN',
),
align_corners=False,
),
data_preprocessor=dict(
size=(512, 512),
mean=[
122.21429912990676,
77.0821859677977,
87.03836664626716,
],
std=[
50.53335800365262,
42.895340354037465,
47.739426483390446,
],
bgr_to_rgb=False,
),
decode_head=dict(
num_classes=8,
loss_decode=dict(
type='DiceLoss',
use_sigmoid=False,
loss_weight=1.0,
),
align_corners=False,
),
auxiliary_head=[
dict(
type='FCNHead',
in_channels=128,
channels=128,
num_convs=1,
num_classes=8,
in_index=0,
norm_cfg=dict(
type='SyncBN',
requires_grad=True,
),
concat_input=False,
align_corners=False,
loss_decode=dict(
type='DiceLoss',
use_sigmoid=False,
loss_weight=0.4,
),
),
dict(
type='FCNHead',
in_channels=128,
channels=128,
num_convs=1,
num_classes=8,
in_index=1,
norm_cfg=dict(
type='SyncBN',
requires_grad=True,
),
concat_input=False,
align_corners=False,
loss_decode=dict(
type='DiceLoss',
use_sigmoid=False,
loss_weight=0.4,
),
),
],
)
test_cfg = dict(
crop_size=(512, 512),
)
optim_wrapper = dict(
type='OptimWrapper',
_delete_=True,
optimizer=dict(
type='AdamW',
lr=0.0001,
weight_decay=0.0005,
),
clip_grad=dict(
max_norm=1,
norm_type=2,
),
)
param_scheduler = [
dict(
type='LinearLR',
start_factor=1e-06,
by_epoch=True,
begin=0,
end=10,
),
dict(
type='PolyLR',
power=0.9,
begin=10,
end=300,
eta_min=1e-05,
by_epoch=True,
),
]

144
Seg_All_In_One_MMSeg/configs/icnet/my_icnet_r18_r18_Pre_g1-300e_val1_check10_publicdataset_endovis_2018-512x512-no_testslide.py Normal file
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@@ -0,0 +1,144 @@
_base_ = [
'../_base_/models/icnet_r50-d8.py',
'../_base_/datasets/publicdataset_endovis_2018.py',
'../_base_/default_runtime.py',
'../_base_/schedules/schedule_300e_val1_check10.py',
]
norm_cfg = dict(
type='BN',
)
crop_size = (512, 512)
data_preprocessor = dict(
size=(512, 512),
mean=[
122.21429912990676,
77.0821859677977,
87.03836664626716,
],
std=[
50.53335800365262,
42.895340354037465,
47.739426483390446,
],
bgr_to_rgb=False,
)
model = dict(
backbone=dict(
backbone_cfg=dict(
depth=18,
init_cfg=dict(
type='Pretrained',
checkpoint='./My_Local_Model/open_mmlab/resnet18_v1c.pth',
),
),
layer_channels=(128, 512),
norm_cfg=dict(
type='BN',
),
align_corners=False,
),
data_preprocessor=dict(
size=(512, 512),
mean=[
122.21429912990676,
77.0821859677977,
87.03836664626716,
],
std=[
50.53335800365262,
42.895340354037465,
47.739426483390446,
],
bgr_to_rgb=False,
),
decode_head=dict(
num_classes=8,
loss_decode=dict(
type='DiceLoss',
use_sigmoid=False,
loss_weight=1.0,
),
align_corners=False,
),
auxiliary_head=[
dict(
type='FCNHead',
in_channels=128,
channels=128,
num_convs=1,
num_classes=8,
in_index=0,
norm_cfg=dict(
type='SyncBN',
requires_grad=True,
),
concat_input=False,
align_corners=False,
loss_decode=dict(
type='DiceLoss',
use_sigmoid=False,
loss_weight=0.4,
),
),
dict(
type='FCNHead',
in_channels=128,
channels=128,
num_convs=1,
num_classes=8,
in_index=1,
norm_cfg=dict(
type='SyncBN',
requires_grad=True,
),
concat_input=False,
align_corners=False,
loss_decode=dict(
type='DiceLoss',
use_sigmoid=False,
loss_weight=0.4,
),
),
],
)
test_cfg = dict(
crop_size=(512, 512),
)
optim_wrapper = dict(
type='OptimWrapper',
_delete_=True,
optimizer=dict(
type='AdamW',
lr=0.0001,
weight_decay=0.0005,
),
clip_grad=dict(
max_norm=1,
norm_type=2,
),
)
param_scheduler = [
dict(
type='LinearLR',
start_factor=1e-06,
by_epoch=True,
begin=0,
end=10,
),
dict(
type='PolyLR',
power=0.9,
begin=10,
end=300,
eta_min=1e-05,
by_epoch=True,
),
]

138
Seg_All_In_One_MMSeg/configs/icnet/my_icnet_r18_r18_Pre_g1-40k_check_4000_my_dataset_model-832x832-no_testslide.py Normal file
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_base_ = [
'../_base_/models/icnet_r50-d8.py',
'../_base_/datasets/my_dataset_model.py',
'../_base_/default_runtime.py',
'../_base_/schedules/schedule_40k_check_4000.py',
]
norm_cfg = dict(
type='BN',
)
crop_size = (832, 832)
data_preprocessor = dict(
size=(832, 832),
mean=[
94.94709810464303,
61.72942233949928,
75.93763705236906,
],
std=[
44.005506081132594,
42.69595666984776,
44.99354156225523,
],
bgr_to_rgb=False,
)
model = dict(
backbone=dict(
backbone_cfg=(128, 512),
layer_channels=(128, 512),
norm_cfg=dict(
type='BN',
),
align_corners=False,
),
data_preprocessor=dict(
size=(832, 832),
mean=[
94.94709810464303,
61.72942233949928,
75.93763705236906,
],
std=[
44.005506081132594,
42.69595666984776,
44.99354156225523,
],
bgr_to_rgb=False,
),
decode_head=dict(
num_classes=36,
loss_decode=dict(
type='DiceLoss',
use_sigmoid=False,
loss_weight=1.0,
),
align_corners=False,
),
auxiliary_head=[
dict(
type='FCNHead',
in_channels=128,
channels=128,
num_convs=1,
num_classes=36,
in_index=0,
norm_cfg=dict(
type='SyncBN',
requires_grad=True,
),
concat_input=False,
align_corners=False,
loss_decode=dict(
type='DiceLoss',
use_sigmoid=False,
loss_weight=0.4,
),
),
dict(
type='FCNHead',
in_channels=128,
channels=128,
num_convs=1,
num_classes=36,
in_index=1,
norm_cfg=dict(
type='SyncBN',
requires_grad=True,
),
concat_input=False,
align_corners=False,
loss_decode=dict(
type='DiceLoss',
use_sigmoid=False,
loss_weight=0.4,
),
),
],
)
test_cfg = dict(
crop_size=(832, 832),
)
optim_wrapper = dict(
type='OptimWrapper',
_delete_=True,
optimizer=dict(
type='AdamW',
lr=0.0001,
weight_decay=0.0005,
),
clip_grad=dict(
max_norm=1,
norm_type=2,
),
)
param_scheduler = [
dict(
type='LinearLR',
start_factor=1e-06,
by_epoch=False,
begin=0,
end=1500,
),
dict(
type='PolyLR',
power=0.9,
begin=1500,
end=40000,
eta_min=1e-05,
by_epoch=False,
),
]

146
Seg_All_In_One_MMSeg/configs/icnet/my_icnet_r18_r18_Pre_g1-40k_check_4000_my_dataset_model-832x832-testslide.py Normal file
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_base_ = [
'../_base_/models/icnet_r50-d8.py',
'../_base_/datasets/my_dataset_model.py',
'../_base_/default_runtime.py',
'../_base_/schedules/schedule_40k_check_4000.py',
]
norm_cfg = dict(
type='BN',
)
crop_size = (832, 832)
data_preprocessor = dict(
size=(832, 832),
mean=[
94.94709810464303,
61.72942233949928,
75.93763705236906,
],
std=[
44.005506081132594,
42.69595666984776,
44.99354156225523,
],
bgr_to_rgb=False,
)
model = dict(
backbone=dict(
backbone_cfg=dict(
depth=18,
init_cfg=dict(
type='Pretrained',
checkpoint='./My_Local_Model/open_mmlab/resnet18_v1c.pth',
),
),
layer_channels=(128, 512),
norm_cfg=dict(
type='BN',
),
align_corners=True,
),
data_preprocessor=dict(
size=(832, 832),
mean=[
94.94709810464303,
61.72942233949928,
75.93763705236906,
],
std=[
44.005506081132594,
42.69595666984776,
44.99354156225523,
],
bgr_to_rgb=False,
),
decode_head=dict(
num_classes=36,
loss_decode=dict(
type='DiceLoss',
use_sigmoid=False,
loss_weight=1.0,
),
align_corners=True,
),
auxiliary_head=[
dict(
type='FCNHead',
in_channels=128,
channels=128,
num_convs=1,
num_classes=36,
in_index=0,
norm_cfg=dict(
type='SyncBN',
requires_grad=True,
),
concat_input=False,
align_corners=True,
loss_decode=dict(
type='DiceLoss',
use_sigmoid=False,
loss_weight=0.4,
),
),
dict(
type='FCNHead',
in_channels=128,
channels=128,
num_convs=1,
num_classes=36,
in_index=1,
norm_cfg=dict(
type='SyncBN',
requires_grad=True,
),
concat_input=False,
align_corners=True,
loss_decode=dict(
type='DiceLoss',
use_sigmoid=False,
loss_weight=0.4,
),
),
],
)
test_cfg = dict(
mode='slide',
crop_size=(832, 832),
stride=(554, 554),
)
optim_wrapper = dict(
type='OptimWrapper',
_delete_=True,
optimizer=dict(
type='AdamW',
lr=0.0001,
weight_decay=0.0005,
),
clip_grad=dict(
max_norm=1,
norm_type=2,
),
)
param_scheduler = [
dict(
type='LinearLR',
start_factor=1e-06,
by_epoch=False,
begin=0,
end=1500,
),
dict(
type='PolyLR',
power=0.9,
begin=1500,
end=40000,
eta_min=1e-05,
by_epoch=False,
),
]

144
Seg_All_In_One_MMSeg/configs/icnet/my_icnet_r18_r50_Pre_g1-300e_val1_check10_publicdataset_autolaparo-512x512-no_testslide.py Normal file
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_base_ = [
'../_base_/models/icnet_r50-d8.py',
'../_base_/datasets/publicdataset_autolaparo.py',
'../_base_/default_runtime.py',
'../_base_/schedules/schedule_300e_val1_check10.py',
]
norm_cfg = dict(
type='BN',
)
crop_size = (512, 512)
data_preprocessor = dict(
size=(512, 512),
mean=[
123.62464353460942,
85.34836259209033,
82.31539425671558,
],
std=[
47.172211618459315,
47.08256715323592,
48.135121265163605,
],
bgr_to_rgb=False,
)
model = dict(
backbone=dict(
backbone_cfg=dict(
depth=50,
init_cfg=dict(
type='Pretrained',
checkpoint='./My_Local_Model/open_mmlab/resnet50_v1c.pth',
),
),
layer_channels=(512, 2048),
norm_cfg=dict(
type='BN',
),
align_corners=False,
),
data_preprocessor=dict(
size=(512, 512),
mean=[
123.62464353460942,
85.34836259209033,
82.31539425671558,
],
std=[
47.172211618459315,
47.08256715323592,
48.135121265163605,
],
bgr_to_rgb=False,
),
decode_head=dict(
num_classes=10,
loss_decode=dict(
type='DiceLoss',
use_sigmoid=False,
loss_weight=1.0,
),
align_corners=False,
),
auxiliary_head=[
dict(
type='FCNHead',
in_channels=128,
channels=128,
num_convs=1,
num_classes=10,
in_index=0,
norm_cfg=dict(
type='SyncBN',
requires_grad=True,
),
concat_input=False,
align_corners=False,
loss_decode=dict(
type='DiceLoss',
use_sigmoid=False,
loss_weight=0.4,
),
),
dict(
type='FCNHead',
in_channels=128,
channels=128,
num_convs=1,
num_classes=10,
in_index=1,
norm_cfg=dict(
type='SyncBN',
requires_grad=True,
),
concat_input=False,
align_corners=False,
loss_decode=dict(
type='DiceLoss',
use_sigmoid=False,
loss_weight=0.4,
),
),
],
)
test_cfg = dict(
crop_size=(512, 512),
)
optim_wrapper = dict(
type='OptimWrapper',
_delete_=True,
optimizer=dict(
type='AdamW',
lr=0.0001,
weight_decay=0.0005,
),
clip_grad=dict(
max_norm=1,
norm_type=2,
),
)
param_scheduler = [
dict(
type='LinearLR',
start_factor=1e-06,
by_epoch=True,
begin=0,
end=10,
),
dict(
type='PolyLR',
power=0.9,
begin=10,
end=300,
eta_min=1e-05,
by_epoch=True,
),
]

144
Seg_All_In_One_MMSeg/configs/icnet/my_icnet_r18_r50_Pre_g1-300e_val1_check10_publicdataset_cholecseg8k-512x512-no_testslide.py Normal file
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@@ -0,0 +1,144 @@
_base_ = [
'../_base_/models/icnet_r50-d8.py',
'../_base_/datasets/publicdataset_cholecseg8k.py',
'../_base_/default_runtime.py',
'../_base_/schedules/schedule_300e_val1_check10.py',
]
norm_cfg = dict(
type='BN',
)
crop_size = (512, 512)
data_preprocessor = dict(
size=(512, 512),
mean=[
85.65740418979115,
53.99282220050495,
46.074045888534535,
],
std=[
72.24589167201978,
56.76979155397199,
49.056637115061775,
],
bgr_to_rgb=False,
)
model = dict(
backbone=dict(
backbone_cfg=dict(
depth=50,
init_cfg=dict(
type='Pretrained',
checkpoint='./My_Local_Model/open_mmlab/resnet50_v1c.pth',
),
),
layer_channels=(512, 2048),
norm_cfg=dict(
type='BN',
),
align_corners=False,
),
data_preprocessor=dict(
size=(512, 512),
mean=[
85.65740418979115,
53.99282220050495,
46.074045888534535,
],
std=[
72.24589167201978,
56.76979155397199,
49.056637115061775,
],
bgr_to_rgb=False,
),
decode_head=dict(
num_classes=13,
loss_decode=dict(
type='DiceLoss',
use_sigmoid=False,
loss_weight=1.0,
),
align_corners=False,
),
auxiliary_head=[
dict(
type='FCNHead',
in_channels=128,
channels=128,
num_convs=1,
num_classes=13,
in_index=0,
norm_cfg=dict(
type='SyncBN',
requires_grad=True,
),
concat_input=False,
align_corners=False,
loss_decode=dict(
type='DiceLoss',
use_sigmoid=False,
loss_weight=0.4,
),
),
dict(
type='FCNHead',
in_channels=128,
channels=128,
num_convs=1,
num_classes=13,
in_index=1,
norm_cfg=dict(
type='SyncBN',
requires_grad=True,
),
concat_input=False,
align_corners=False,
loss_decode=dict(
type='DiceLoss',
use_sigmoid=False,
loss_weight=0.4,
),
),
],
)
test_cfg = dict(
crop_size=(512, 512),
)
optim_wrapper = dict(
type='OptimWrapper',
_delete_=True,
optimizer=dict(
type='AdamW',
lr=0.0001,
weight_decay=0.0005,
),
clip_grad=dict(
max_norm=1,
norm_type=2,
),
)
param_scheduler = [
dict(
type='LinearLR',
start_factor=1e-06,
by_epoch=True,
begin=0,
end=10,
),
dict(
type='PolyLR',
power=0.9,
begin=10,
end=300,
eta_min=1e-05,
by_epoch=True,
),
]

144
Seg_All_In_One_MMSeg/configs/icnet/my_icnet_r18_r50_Pre_g1-300e_val1_check10_publicdataset_dresden-512x512-no_testslide.py Normal file
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@@ -0,0 +1,144 @@
_base_ = [
'../_base_/models/icnet_r50-d8.py',
'../_base_/datasets/publicdataset_dresden.py',
'../_base_/default_runtime.py',
'../_base_/schedules/schedule_300e_val1_check10.py',
]
norm_cfg = dict(
type='BN',
)
crop_size = (512, 512)
data_preprocessor = dict(
size=(512, 512),
mean=[
103.172638338208,
61.44762740851152,
51.407770213021976,
],
std=[
75.77031253622098,
54.63616729031377,
49.45572239497569,
],
bgr_to_rgb=False,
)
model = dict(
backbone=dict(
backbone_cfg=dict(
depth=50,
init_cfg=dict(
type='Pretrained',
checkpoint='./My_Local_Model/open_mmlab/resnet50_v1c.pth',
),
),
layer_channels=(512, 2048),
norm_cfg=dict(
type='BN',
),
align_corners=False,
),
data_preprocessor=dict(
size=(512, 512),
mean=[
103.172638338208,
61.44762740851152,
51.407770213021976,
],
std=[
75.77031253622098,
54.63616729031377,
49.45572239497569,
],
bgr_to_rgb=False,
),
decode_head=dict(
num_classes=11,
loss_decode=dict(
type='DiceLoss',
use_sigmoid=False,
loss_weight=1.0,
),
align_corners=False,
),
auxiliary_head=[
dict(
type='FCNHead',
in_channels=128,
channels=128,
num_convs=1,
num_classes=11,
in_index=0,
norm_cfg=dict(
type='SyncBN',
requires_grad=True,
),
concat_input=False,
align_corners=False,
loss_decode=dict(
type='DiceLoss',
use_sigmoid=False,
loss_weight=0.4,
),
),
dict(
type='FCNHead',
in_channels=128,
channels=128,
num_convs=1,
num_classes=11,
in_index=1,
norm_cfg=dict(
type='SyncBN',
requires_grad=True,
),
concat_input=False,
align_corners=False,
loss_decode=dict(
type='DiceLoss',
use_sigmoid=False,
loss_weight=0.4,
),
),
],
)
test_cfg = dict(
crop_size=(512, 512),
)
optim_wrapper = dict(
type='OptimWrapper',
_delete_=True,
optimizer=dict(
type='AdamW',
lr=0.0001,
weight_decay=0.0005,
),
clip_grad=dict(
max_norm=1,
norm_type=2,
),
)
param_scheduler = [
dict(
type='LinearLR',
start_factor=1e-06,
by_epoch=True,
begin=0,
end=10,
),
dict(
type='PolyLR',
power=0.9,
begin=10,
end=300,
eta_min=1e-05,
by_epoch=True,
),
]

144
Seg_All_In_One_MMSeg/configs/icnet/my_icnet_r18_r50_Pre_g1-300e_val1_check10_publicdataset_endovis_2017-512x512-no_testslide.py Normal file
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@@ -0,0 +1,144 @@
_base_ = [
'../_base_/models/icnet_r50-d8.py',
'../_base_/datasets/publicdataset_endovis_2017.py',
'../_base_/default_runtime.py',
'../_base_/schedules/schedule_300e_val1_check10.py',
]
norm_cfg = dict(
type='BN',
)
crop_size = (512, 512)
data_preprocessor = dict(
size=(512, 512),
mean=[
122.21429912990676,
77.0821859677977,
87.03836664626716,
],
std=[
50.53335800365262,
42.895340354037465,
47.739426483390446,
],
bgr_to_rgb=False,
)
model = dict(
backbone=dict(
backbone_cfg=dict(
depth=50,
init_cfg=dict(
type='Pretrained',
checkpoint='./My_Local_Model/open_mmlab/resnet50_v1c.pth',
),
),
layer_channels=(512, 2048),
norm_cfg=dict(
type='BN',
),
align_corners=False,
),
data_preprocessor=dict(
size=(512, 512),
mean=[
122.21429912990676,
77.0821859677977,
87.03836664626716,
],
std=[
50.53335800365262,
42.895340354037465,
47.739426483390446,
],
bgr_to_rgb=False,
),
decode_head=dict(
num_classes=8,
loss_decode=dict(
type='DiceLoss',
use_sigmoid=False,
loss_weight=1.0,
),
align_corners=False,
),
auxiliary_head=[
dict(
type='FCNHead',
in_channels=128,
channels=128,
num_convs=1,
num_classes=8,
in_index=0,
norm_cfg=dict(
type='SyncBN',
requires_grad=True,
),
concat_input=False,
align_corners=False,
loss_decode=dict(
type='DiceLoss',
use_sigmoid=False,
loss_weight=0.4,
),
),
dict(
type='FCNHead',
in_channels=128,
channels=128,
num_convs=1,
num_classes=8,
in_index=1,
norm_cfg=dict(
type='SyncBN',
requires_grad=True,
),
concat_input=False,
align_corners=False,
loss_decode=dict(
type='DiceLoss',
use_sigmoid=False,
loss_weight=0.4,
),
),
],
)
test_cfg = dict(
crop_size=(512, 512),
)
optim_wrapper = dict(
type='OptimWrapper',
_delete_=True,
optimizer=dict(
type='AdamW',
lr=0.0001,
weight_decay=0.0005,
),
clip_grad=dict(
max_norm=1,
norm_type=2,
),
)
param_scheduler = [
dict(
type='LinearLR',
start_factor=1e-06,
by_epoch=True,
begin=0,
end=10,
),
dict(
type='PolyLR',
power=0.9,
begin=10,
end=300,
eta_min=1e-05,
by_epoch=True,
),
]

144
Seg_All_In_One_MMSeg/configs/icnet/my_icnet_r18_r50_Pre_g1-300e_val1_check10_publicdataset_endovis_2018-512x512-no_testslide.py Normal file
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@@ -0,0 +1,144 @@
_base_ = [
'../_base_/models/icnet_r50-d8.py',
'../_base_/datasets/publicdataset_endovis_2018.py',
'../_base_/default_runtime.py',
'../_base_/schedules/schedule_300e_val1_check10.py',
]
norm_cfg = dict(
type='BN',
)
crop_size = (512, 512)
data_preprocessor = dict(
size=(512, 512),
mean=[
122.21429912990676,
77.0821859677977,
87.03836664626716,
],
std=[
50.53335800365262,
42.895340354037465,
47.739426483390446,
],
bgr_to_rgb=False,
)
model = dict(
backbone=dict(
backbone_cfg=dict(
depth=50,
init_cfg=dict(
type='Pretrained',
checkpoint='./My_Local_Model/open_mmlab/resnet50_v1c.pth',
),
),
layer_channels=(512, 2048),
norm_cfg=dict(
type='BN',
),
align_corners=False,
),
data_preprocessor=dict(
size=(512, 512),
mean=[
122.21429912990676,
77.0821859677977,
87.03836664626716,
],
std=[
50.53335800365262,
42.895340354037465,
47.739426483390446,
],
bgr_to_rgb=False,
),
decode_head=dict(
num_classes=8,
loss_decode=dict(
type='DiceLoss',
use_sigmoid=False,
loss_weight=1.0,
),
align_corners=False,
),
auxiliary_head=[
dict(
type='FCNHead',
in_channels=128,
channels=128,
num_convs=1,
num_classes=8,
in_index=0,
norm_cfg=dict(
type='SyncBN',
requires_grad=True,
),
concat_input=False,
align_corners=False,
loss_decode=dict(
type='DiceLoss',
use_sigmoid=False,
loss_weight=0.4,
),
),
dict(
type='FCNHead',
in_channels=128,
channels=128,
num_convs=1,
num_classes=8,
in_index=1,
norm_cfg=dict(
type='SyncBN',
requires_grad=True,
),
concat_input=False,
align_corners=False,
loss_decode=dict(
type='DiceLoss',
use_sigmoid=False,
loss_weight=0.4,
),
),
],
)
test_cfg = dict(
crop_size=(512, 512),
)
optim_wrapper = dict(
type='OptimWrapper',
_delete_=True,
optimizer=dict(
type='AdamW',
lr=0.0001,
weight_decay=0.0005,
),
clip_grad=dict(
max_norm=1,
norm_type=2,
),
)
param_scheduler = [
dict(
type='LinearLR',
start_factor=1e-06,
by_epoch=True,
begin=0,
end=10,
),
dict(
type='PolyLR',
power=0.9,
begin=10,
end=300,
eta_min=1e-05,
by_epoch=True,
),
]