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Seg_All_In_One_MMSeg/configs/encnet/README.md
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Seg_All_In_One_MMSeg/configs/encnet/README.md
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# EncNet
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> [Context Encoding for Semantic Segmentation](https://arxiv.org/abs/1803.08904)
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## Introduction
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<!-- [ALGORITHM] -->
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<a href="https://github.com/zhanghang1989/PyTorch-Encoding">Official Repo</a>
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<a href="https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/enc_head.py#L63">Code Snippet</a>
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## Abstract
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<!-- [ABSTRACT] -->
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Recent work has made significant progress in improving spatial resolution for pixelwise labeling with Fully Convolutional Network (FCN) framework by employing Dilated/Atrous convolution, utilizing multi-scale features and refining boundaries. In this paper, we explore the impact of global contextual information in semantic segmentation by introducing the Context Encoding Module, which captures the semantic context of scenes and selectively highlights class-dependent featuremaps. The proposed Context Encoding Module significantly improves semantic segmentation results with only marginal extra computation cost over FCN. Our approach has achieved new state-of-the-art results 51.7% mIoU on PASCAL-Context, 85.9% mIoU on PASCAL VOC 2012. Our single model achieves a final score of 0.5567 on ADE20K test set, which surpass the winning entry of COCO-Place Challenge in 2017. In addition, we also explore how the Context Encoding Module can improve the feature representation of relatively shallow networks for the image classification on CIFAR-10 dataset. Our 14 layer network has achieved an error rate of 3.45%, which is comparable with state-of-the-art approaches with over 10 times more layers. The source code for the complete system are publicly available.
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<!-- [IMAGE] -->
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<div align=center>
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<img src="https://user-images.githubusercontent.com/24582831/142901276-b364fbbf-3bdb-4000-9d31-b9a135e30935.png" width="70%"/>
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</div>
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## Results and models
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### Cityscapes
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| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device | mIoU | mIoU(ms+flip) | config | download |
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| ------ | -------- | --------- | ------: | -------- | -------------- | ------ | ----: | ------------: | ------------------------------------------------------------------------------------------------------------------------------ | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| EncNet | R-50-D8 | 512x1024 | 40000 | 8.6 | 4.58 | V100 | 75.67 | 77.08 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/encnet/encnet_r50-d8_4xb2-40k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_512x1024_40k_cityscapes/encnet_r50-d8_512x1024_40k_cityscapes_20200621_220958-68638a47.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_512x1024_40k_cityscapes/encnet_r50-d8_512x1024_40k_cityscapes-20200621_220958.log.json) |
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| EncNet | R-101-D8 | 512x1024 | 40000 | 12.1 | 2.66 | V100 | 75.81 | 77.21 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/encnet/encnet_r101-d8_4xb2-40k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_512x1024_40k_cityscapes/encnet_r101-d8_512x1024_40k_cityscapes_20200621_220933-35e0a3e8.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_512x1024_40k_cityscapes/encnet_r101-d8_512x1024_40k_cityscapes-20200621_220933.log.json) |
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| EncNet | R-50-D8 | 769x769 | 40000 | 9.8 | 1.82 | V100 | 76.24 | 77.85 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/encnet/encnet_r50-d8_4xb2-40k_cityscapes-769x769.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_769x769_40k_cityscapes/encnet_r50-d8_769x769_40k_cityscapes_20200621_220958-3bcd2884.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_769x769_40k_cityscapes/encnet_r50-d8_769x769_40k_cityscapes-20200621_220958.log.json) |
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| EncNet | R-101-D8 | 769x769 | 40000 | 13.7 | 1.26 | V100 | 74.25 | 76.25 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/encnet/encnet_r101-d8_4xb2-40k_cityscapes-769x769.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_769x769_40k_cityscapes/encnet_r101-d8_769x769_40k_cityscapes_20200621_220933-2fafed55.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_769x769_40k_cityscapes/encnet_r101-d8_769x769_40k_cityscapes-20200621_220933.log.json) |
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| EncNet | R-50-D8 | 512x1024 | 80000 | - | - | V100 | 77.94 | 79.13 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/encnet/encnet_r50-d8_4xb2-80k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_512x1024_80k_cityscapes/encnet_r50-d8_512x1024_80k_cityscapes_20200622_003554-fc5c5624.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_512x1024_80k_cityscapes/encnet_r50-d8_512x1024_80k_cityscapes-20200622_003554.log.json) |
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| EncNet | R-101-D8 | 512x1024 | 80000 | - | - | V100 | 78.55 | 79.47 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/encnet/encnet_r101-d8_4xb2-80k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_512x1024_80k_cityscapes/encnet_r101-d8_512x1024_80k_cityscapes_20200622_003555-1de64bec.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_512x1024_80k_cityscapes/encnet_r101-d8_512x1024_80k_cityscapes-20200622_003555.log.json) |
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| EncNet | R-50-D8 | 769x769 | 80000 | - | - | V100 | 77.44 | 78.72 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/encnet/encnet_r50-d8_4xb2-80k_cityscapes-769x769.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_769x769_80k_cityscapes/encnet_r50-d8_769x769_80k_cityscapes_20200622_003554-55096dcb.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_769x769_80k_cityscapes/encnet_r50-d8_769x769_80k_cityscapes-20200622_003554.log.json) |
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| EncNet | R-101-D8 | 769x769 | 80000 | - | - | V100 | 76.10 | 76.97 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/encnet/encnet_r101-d8_4xb2-80k_cityscapes-769x769.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_769x769_80k_cityscapes/encnet_r101-d8_769x769_80k_cityscapes_20200622_003555-470ef79d.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_769x769_80k_cityscapes/encnet_r101-d8_769x769_80k_cityscapes-20200622_003555.log.json) |
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### ADE20K
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| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device | mIoU | mIoU(ms+flip) | config | download |
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| ------ | -------- | --------- | ------: | -------- | -------------- | ------ | ----: | ------------: | -------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| EncNet | R-50-D8 | 512x512 | 80000 | 10.1 | 22.81 | V100 | 39.53 | 41.17 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/encnet/encnet_r50-d8_4xb4-80k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_512x512_80k_ade20k/encnet_r50-d8_512x512_80k_ade20k_20200622_042412-44b46b04.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_512x512_80k_ade20k/encnet_r50-d8_512x512_80k_ade20k-20200622_042412.log.json) |
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| EncNet | R-101-D8 | 512x512 | 80000 | 13.6 | 14.87 | V100 | 42.11 | 43.61 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/encnet/encnet_r101-d8_4xb4-80k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_512x512_80k_ade20k/encnet_r101-d8_512x512_80k_ade20k_20200622_101128-dd35e237.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_512x512_80k_ade20k/encnet_r101-d8_512x512_80k_ade20k-20200622_101128.log.json) |
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| EncNet | R-50-D8 | 512x512 | 160000 | - | - | V100 | 40.10 | 41.71 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/encnet/encnet_r50-d8_4xb4-160k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_512x512_160k_ade20k/encnet_r50-d8_512x512_160k_ade20k_20200622_101059-b2db95e0.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_512x512_160k_ade20k/encnet_r50-d8_512x512_160k_ade20k-20200622_101059.log.json) |
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| EncNet | R-101-D8 | 512x512 | 160000 | - | - | V100 | 42.61 | 44.01 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/encnet/encnet_r101-d8_4xb4-160k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_512x512_160k_ade20k/encnet_r101-d8_512x512_160k_ade20k_20200622_073348-7989641f.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_512x512_160k_ade20k/encnet_r101-d8_512x512_160k_ade20k-20200622_073348.log.json) |
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## Citation
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```bibtex
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@InProceedings{Zhang_2018_CVPR,
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author = {Zhang, Hang and Dana, Kristin and Shi, Jianping and Zhang, Zhongyue and Wang, Xiaogang and Tyagi, Ambrish and Agrawal, Amit},
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title = {Context Encoding for Semantic Segmentation},
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booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
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month = {June},
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year = {2018}
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}
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```
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_base_ = './encnet_r50-d8_4xb2-40k_cityscapes-512x1024.py'
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model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
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_base_ = './encnet_r50-d8_4xb2-40k_cityscapes-769x769.py'
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model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
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_base_ = './encnet_r50-d8_4xb2-80k_cityscapes-512x1024.py'
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model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
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_base_ = './encnet_r50-d8_4xb2-80k_cityscapes-769x769.py'
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model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
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_base_ = './encnet_r50-d8_4xb4-160k_ade20k-512x512.py'
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model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
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_base_ = './encnet_r50-d8_4xb4-20k_voc12aug-512x512.py'
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model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
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_base_ = './encnet_r50-d8_4xb4-40k_voc12aug-512x512.py'
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model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
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_base_ = './encnet_r50-d8_4xb4-80k_ade20k-512x512.py'
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model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
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_base_ = [
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'../_base_/models/encnet_r50-d8.py', '../_base_/datasets/cityscapes.py',
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'../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py'
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]
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crop_size = (512, 1024)
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data_preprocessor = dict(size=crop_size)
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model = dict(data_preprocessor=data_preprocessor)
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_base_ = [
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'../_base_/models/encnet_r50-d8.py',
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'../_base_/datasets/cityscapes_769x769.py', '../_base_/default_runtime.py',
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'../_base_/schedules/schedule_40k.py'
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]
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crop_size = (769, 769)
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data_preprocessor = dict(size=crop_size)
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model = dict(
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data_preprocessor=data_preprocessor,
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decode_head=dict(align_corners=True),
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auxiliary_head=dict(align_corners=True),
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test_cfg=dict(mode='slide', crop_size=(769, 769), stride=(513, 513)))
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_base_ = [
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'../_base_/models/encnet_r50-d8.py', '../_base_/datasets/cityscapes.py',
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'../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
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]
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crop_size = (512, 1024)
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data_preprocessor = dict(size=crop_size)
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model = dict(data_preprocessor=data_preprocessor)
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_base_ = [
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'../_base_/models/encnet_r50-d8.py',
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'../_base_/datasets/cityscapes_769x769.py', '../_base_/default_runtime.py',
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'../_base_/schedules/schedule_80k.py'
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]
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crop_size = (769, 769)
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data_preprocessor = dict(size=crop_size)
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model = dict(
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data_preprocessor=data_preprocessor,
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decode_head=dict(align_corners=True),
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auxiliary_head=dict(align_corners=True),
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test_cfg=dict(mode='slide', crop_size=(769, 769), stride=(513, 513)))
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_base_ = [
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'../_base_/models/encnet_r50-d8.py', '../_base_/datasets/ade20k.py',
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'../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py'
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]
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crop_size = (512, 512)
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data_preprocessor = dict(size=crop_size)
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model = dict(
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data_preprocessor=data_preprocessor,
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decode_head=dict(num_classes=150),
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auxiliary_head=dict(num_classes=150))
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_base_ = [
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'../_base_/models/encnet_r50-d8.py',
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'../_base_/datasets/pascal_voc12_aug.py', '../_base_/default_runtime.py',
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'../_base_/schedules/schedule_20k.py'
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]
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crop_size = (512, 512)
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data_preprocessor = dict(size=crop_size)
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model = dict(
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data_preprocessor=data_preprocessor,
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decode_head=dict(num_classes=21),
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auxiliary_head=dict(num_classes=21))
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_base_ = [
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'../_base_/models/encnet_r50-d8.py',
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'../_base_/datasets/pascal_voc12_aug.py', '../_base_/default_runtime.py',
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'../_base_/schedules/schedule_40k.py'
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]
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crop_size = (512, 512)
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data_preprocessor = dict(size=crop_size)
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model = dict(
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data_preprocessor=data_preprocessor,
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decode_head=dict(num_classes=21),
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auxiliary_head=dict(num_classes=21))
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_base_ = [
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'../_base_/models/encnet_r50-d8.py', '../_base_/datasets/ade20k.py',
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'../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
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]
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crop_size = (512, 512)
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data_preprocessor = dict(size=crop_size)
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model = dict(
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data_preprocessor=data_preprocessor,
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decode_head=dict(num_classes=150),
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auxiliary_head=dict(num_classes=150))
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_base_ = [
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'../_base_/models/encnet_r50-d8.py', '../_base_/datasets/ade20k.py',
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'../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
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]
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crop_size = (512, 512)
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data_preprocessor = dict(size=crop_size)
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model = dict(
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data_preprocessor=data_preprocessor,
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backbone=dict(stem_channels=128),
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decode_head=dict(num_classes=150),
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auxiliary_head=dict(num_classes=150))
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296
Seg_All_In_One_MMSeg/configs/encnet/metafile.yaml
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296
Seg_All_In_One_MMSeg/configs/encnet/metafile.yaml
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Collections:
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- Name: EncNet
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License: Apache License 2.0
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Metadata:
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Training Data:
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- Cityscapes
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- ADE20K
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Paper:
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Title: Context Encoding for Semantic Segmentation
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URL: https://arxiv.org/abs/1803.08904
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README: configs/encnet/README.md
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Frameworks:
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- PyTorch
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Models:
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- Name: encnet_r50-d8_4xb2-40k_cityscapes-512x1024
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In Collection: EncNet
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Results:
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Task: Semantic Segmentation
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Dataset: Cityscapes
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Metrics:
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mIoU: 75.67
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mIoU(ms+flip): 77.08
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Config: configs/encnet/encnet_r50-d8_4xb2-40k_cityscapes-512x1024.py
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Metadata:
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Training Data: Cityscapes
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Batch Size: 8
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Architecture:
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- R-50-D8
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- EncNet
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Training Resources: 4x V100 GPUS
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Memory (GB): 8.6
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_512x1024_40k_cityscapes/encnet_r50-d8_512x1024_40k_cityscapes_20200621_220958-68638a47.pth
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Training log: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_512x1024_40k_cityscapes/encnet_r50-d8_512x1024_40k_cityscapes-20200621_220958.log.json
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Paper:
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Title: Context Encoding for Semantic Segmentation
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URL: https://arxiv.org/abs/1803.08904
|
||||
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/enc_head.py#L63
|
||||
Framework: PyTorch
|
||||
- Name: encnet_r101-d8_4xb2-40k_cityscapes-512x1024
|
||||
In Collection: EncNet
|
||||
Results:
|
||||
Task: Semantic Segmentation
|
||||
Dataset: Cityscapes
|
||||
Metrics:
|
||||
mIoU: 75.81
|
||||
mIoU(ms+flip): 77.21
|
||||
Config: configs/encnet/encnet_r101-d8_4xb2-40k_cityscapes-512x1024.py
|
||||
Metadata:
|
||||
Training Data: Cityscapes
|
||||
Batch Size: 8
|
||||
Architecture:
|
||||
- R-101-D8
|
||||
- EncNet
|
||||
Training Resources: 4x V100 GPUS
|
||||
Memory (GB): 12.1
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_512x1024_40k_cityscapes/encnet_r101-d8_512x1024_40k_cityscapes_20200621_220933-35e0a3e8.pth
|
||||
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_512x1024_40k_cityscapes/encnet_r101-d8_512x1024_40k_cityscapes-20200621_220933.log.json
|
||||
Paper:
|
||||
Title: Context Encoding for Semantic Segmentation
|
||||
URL: https://arxiv.org/abs/1803.08904
|
||||
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/enc_head.py#L63
|
||||
Framework: PyTorch
|
||||
- Name: encnet_r50-d8_4xb2-40k_cityscapes-769x769
|
||||
In Collection: EncNet
|
||||
Results:
|
||||
Task: Semantic Segmentation
|
||||
Dataset: Cityscapes
|
||||
Metrics:
|
||||
mIoU: 76.24
|
||||
mIoU(ms+flip): 77.85
|
||||
Config: configs/encnet/encnet_r50-d8_4xb2-40k_cityscapes-769x769.py
|
||||
Metadata:
|
||||
Training Data: Cityscapes
|
||||
Batch Size: 8
|
||||
Architecture:
|
||||
- R-50-D8
|
||||
- EncNet
|
||||
Training Resources: 4x V100 GPUS
|
||||
Memory (GB): 9.8
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_769x769_40k_cityscapes/encnet_r50-d8_769x769_40k_cityscapes_20200621_220958-3bcd2884.pth
|
||||
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_769x769_40k_cityscapes/encnet_r50-d8_769x769_40k_cityscapes-20200621_220958.log.json
|
||||
Paper:
|
||||
Title: Context Encoding for Semantic Segmentation
|
||||
URL: https://arxiv.org/abs/1803.08904
|
||||
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/enc_head.py#L63
|
||||
Framework: PyTorch
|
||||
- Name: encnet_r101-d8_4xb2-40k_cityscapes-769x769
|
||||
In Collection: EncNet
|
||||
Results:
|
||||
Task: Semantic Segmentation
|
||||
Dataset: Cityscapes
|
||||
Metrics:
|
||||
mIoU: 74.25
|
||||
mIoU(ms+flip): 76.25
|
||||
Config: configs/encnet/encnet_r101-d8_4xb2-40k_cityscapes-769x769.py
|
||||
Metadata:
|
||||
Training Data: Cityscapes
|
||||
Batch Size: 8
|
||||
Architecture:
|
||||
- R-101-D8
|
||||
- EncNet
|
||||
Training Resources: 4x V100 GPUS
|
||||
Memory (GB): 13.7
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_769x769_40k_cityscapes/encnet_r101-d8_769x769_40k_cityscapes_20200621_220933-2fafed55.pth
|
||||
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_769x769_40k_cityscapes/encnet_r101-d8_769x769_40k_cityscapes-20200621_220933.log.json
|
||||
Paper:
|
||||
Title: Context Encoding for Semantic Segmentation
|
||||
URL: https://arxiv.org/abs/1803.08904
|
||||
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/enc_head.py#L63
|
||||
Framework: PyTorch
|
||||
- Name: encnet_r50-d8_4xb2-80k_cityscapes-512x1024
|
||||
In Collection: EncNet
|
||||
Results:
|
||||
Task: Semantic Segmentation
|
||||
Dataset: Cityscapes
|
||||
Metrics:
|
||||
mIoU: 77.94
|
||||
mIoU(ms+flip): 79.13
|
||||
Config: configs/encnet/encnet_r50-d8_4xb2-80k_cityscapes-512x1024.py
|
||||
Metadata:
|
||||
Training Data: Cityscapes
|
||||
Batch Size: 8
|
||||
Architecture:
|
||||
- R-50-D8
|
||||
- EncNet
|
||||
Training Resources: 4x V100 GPUS
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_512x1024_80k_cityscapes/encnet_r50-d8_512x1024_80k_cityscapes_20200622_003554-fc5c5624.pth
|
||||
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_512x1024_80k_cityscapes/encnet_r50-d8_512x1024_80k_cityscapes-20200622_003554.log.json
|
||||
Paper:
|
||||
Title: Context Encoding for Semantic Segmentation
|
||||
URL: https://arxiv.org/abs/1803.08904
|
||||
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/enc_head.py#L63
|
||||
Framework: PyTorch
|
||||
- Name: encnet_r101-d8_4xb2-80k_cityscapes-512x1024
|
||||
In Collection: EncNet
|
||||
Results:
|
||||
Task: Semantic Segmentation
|
||||
Dataset: Cityscapes
|
||||
Metrics:
|
||||
mIoU: 78.55
|
||||
mIoU(ms+flip): 79.47
|
||||
Config: configs/encnet/encnet_r101-d8_4xb2-80k_cityscapes-512x1024.py
|
||||
Metadata:
|
||||
Training Data: Cityscapes
|
||||
Batch Size: 8
|
||||
Architecture:
|
||||
- R-101-D8
|
||||
- EncNet
|
||||
Training Resources: 4x V100 GPUS
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_512x1024_80k_cityscapes/encnet_r101-d8_512x1024_80k_cityscapes_20200622_003555-1de64bec.pth
|
||||
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_512x1024_80k_cityscapes/encnet_r101-d8_512x1024_80k_cityscapes-20200622_003555.log.json
|
||||
Paper:
|
||||
Title: Context Encoding for Semantic Segmentation
|
||||
URL: https://arxiv.org/abs/1803.08904
|
||||
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/enc_head.py#L63
|
||||
Framework: PyTorch
|
||||
- Name: encnet_r50-d8_4xb2-80k_cityscapes-769x769
|
||||
In Collection: EncNet
|
||||
Results:
|
||||
Task: Semantic Segmentation
|
||||
Dataset: Cityscapes
|
||||
Metrics:
|
||||
mIoU: 77.44
|
||||
mIoU(ms+flip): 78.72
|
||||
Config: configs/encnet/encnet_r50-d8_4xb2-80k_cityscapes-769x769.py
|
||||
Metadata:
|
||||
Training Data: Cityscapes
|
||||
Batch Size: 8
|
||||
Architecture:
|
||||
- R-50-D8
|
||||
- EncNet
|
||||
Training Resources: 4x V100 GPUS
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_769x769_80k_cityscapes/encnet_r50-d8_769x769_80k_cityscapes_20200622_003554-55096dcb.pth
|
||||
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_769x769_80k_cityscapes/encnet_r50-d8_769x769_80k_cityscapes-20200622_003554.log.json
|
||||
Paper:
|
||||
Title: Context Encoding for Semantic Segmentation
|
||||
URL: https://arxiv.org/abs/1803.08904
|
||||
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/enc_head.py#L63
|
||||
Framework: PyTorch
|
||||
- Name: encnet_r101-d8_4xb2-80k_cityscapes-769x769
|
||||
In Collection: EncNet
|
||||
Results:
|
||||
Task: Semantic Segmentation
|
||||
Dataset: Cityscapes
|
||||
Metrics:
|
||||
mIoU: 76.1
|
||||
mIoU(ms+flip): 76.97
|
||||
Config: configs/encnet/encnet_r101-d8_4xb2-80k_cityscapes-769x769.py
|
||||
Metadata:
|
||||
Training Data: Cityscapes
|
||||
Batch Size: 8
|
||||
Architecture:
|
||||
- R-101-D8
|
||||
- EncNet
|
||||
Training Resources: 4x V100 GPUS
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_769x769_80k_cityscapes/encnet_r101-d8_769x769_80k_cityscapes_20200622_003555-470ef79d.pth
|
||||
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_769x769_80k_cityscapes/encnet_r101-d8_769x769_80k_cityscapes-20200622_003555.log.json
|
||||
Paper:
|
||||
Title: Context Encoding for Semantic Segmentation
|
||||
URL: https://arxiv.org/abs/1803.08904
|
||||
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/enc_head.py#L63
|
||||
Framework: PyTorch
|
||||
- Name: encnet_r50-d8_4xb4-80k_ade20k-512x512
|
||||
In Collection: EncNet
|
||||
Results:
|
||||
Task: Semantic Segmentation
|
||||
Dataset: ADE20K
|
||||
Metrics:
|
||||
mIoU: 39.53
|
||||
mIoU(ms+flip): 41.17
|
||||
Config: configs/encnet/encnet_r50-d8_4xb4-80k_ade20k-512x512.py
|
||||
Metadata:
|
||||
Training Data: ADE20K
|
||||
Batch Size: 16
|
||||
Architecture:
|
||||
- R-50-D8
|
||||
- EncNet
|
||||
Training Resources: 4x V100 GPUS
|
||||
Memory (GB): 10.1
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_512x512_80k_ade20k/encnet_r50-d8_512x512_80k_ade20k_20200622_042412-44b46b04.pth
|
||||
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_512x512_80k_ade20k/encnet_r50-d8_512x512_80k_ade20k-20200622_042412.log.json
|
||||
Paper:
|
||||
Title: Context Encoding for Semantic Segmentation
|
||||
URL: https://arxiv.org/abs/1803.08904
|
||||
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/enc_head.py#L63
|
||||
Framework: PyTorch
|
||||
- Name: encnet_r101-d8_4xb4-80k_ade20k-512x512
|
||||
In Collection: EncNet
|
||||
Results:
|
||||
Task: Semantic Segmentation
|
||||
Dataset: ADE20K
|
||||
Metrics:
|
||||
mIoU: 42.11
|
||||
mIoU(ms+flip): 43.61
|
||||
Config: configs/encnet/encnet_r101-d8_4xb4-80k_ade20k-512x512.py
|
||||
Metadata:
|
||||
Training Data: ADE20K
|
||||
Batch Size: 16
|
||||
Architecture:
|
||||
- R-101-D8
|
||||
- EncNet
|
||||
Training Resources: 4x V100 GPUS
|
||||
Memory (GB): 13.6
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_512x512_80k_ade20k/encnet_r101-d8_512x512_80k_ade20k_20200622_101128-dd35e237.pth
|
||||
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_512x512_80k_ade20k/encnet_r101-d8_512x512_80k_ade20k-20200622_101128.log.json
|
||||
Paper:
|
||||
Title: Context Encoding for Semantic Segmentation
|
||||
URL: https://arxiv.org/abs/1803.08904
|
||||
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/enc_head.py#L63
|
||||
Framework: PyTorch
|
||||
- Name: encnet_r50-d8_4xb4-160k_ade20k-512x512
|
||||
In Collection: EncNet
|
||||
Results:
|
||||
Task: Semantic Segmentation
|
||||
Dataset: ADE20K
|
||||
Metrics:
|
||||
mIoU: 40.1
|
||||
mIoU(ms+flip): 41.71
|
||||
Config: configs/encnet/encnet_r50-d8_4xb4-160k_ade20k-512x512.py
|
||||
Metadata:
|
||||
Training Data: ADE20K
|
||||
Batch Size: 16
|
||||
Architecture:
|
||||
- R-50-D8
|
||||
- EncNet
|
||||
Training Resources: 4x V100 GPUS
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_512x512_160k_ade20k/encnet_r50-d8_512x512_160k_ade20k_20200622_101059-b2db95e0.pth
|
||||
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_512x512_160k_ade20k/encnet_r50-d8_512x512_160k_ade20k-20200622_101059.log.json
|
||||
Paper:
|
||||
Title: Context Encoding for Semantic Segmentation
|
||||
URL: https://arxiv.org/abs/1803.08904
|
||||
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/enc_head.py#L63
|
||||
Framework: PyTorch
|
||||
- Name: encnet_r101-d8_4xb4-160k_ade20k-512x512
|
||||
In Collection: EncNet
|
||||
Results:
|
||||
Task: Semantic Segmentation
|
||||
Dataset: ADE20K
|
||||
Metrics:
|
||||
mIoU: 42.61
|
||||
mIoU(ms+flip): 44.01
|
||||
Config: configs/encnet/encnet_r101-d8_4xb4-160k_ade20k-512x512.py
|
||||
Metadata:
|
||||
Training Data: ADE20K
|
||||
Batch Size: 16
|
||||
Architecture:
|
||||
- R-101-D8
|
||||
- EncNet
|
||||
Training Resources: 4x V100 GPUS
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_512x512_160k_ade20k/encnet_r101-d8_512x512_160k_ade20k_20200622_073348-7989641f.pth
|
||||
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_512x512_160k_ade20k/encnet_r101-d8_512x512_160k_ade20k-20200622_073348.log.json
|
||||
Paper:
|
||||
Title: Context Encoding for Semantic Segmentation
|
||||
URL: https://arxiv.org/abs/1803.08904
|
||||
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/enc_head.py#L63
|
||||
Framework: PyTorch
|
||||
@@ -0,0 +1,103 @@
|
||||
_base_ = [
|
||||
'../_base_/models/encnet_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 = (512, 1024)
|
||||
|
||||
data_preprocessor = dict(
|
||||
size=(512, 1024),
|
||||
mean=[
|
||||
94.94709810464303,
|
||||
61.72942233949928,
|
||||
75.93763705236906,
|
||||
],
|
||||
std=[
|
||||
44.005506081132594,
|
||||
42.69595666984776,
|
||||
44.99354156225523,
|
||||
],
|
||||
bgr_to_rgb=False,
|
||||
)
|
||||
|
||||
model = dict(
|
||||
pretrained='./My_Local_Model/open_mmlab/resnet50_v1c.pth',
|
||||
backbone=dict(
|
||||
depth=50,
|
||||
),
|
||||
data_preprocessor=dict(
|
||||
size=(512, 1024),
|
||||
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(
|
||||
num_classes=36,
|
||||
loss_decode=dict(
|
||||
type='DiceLoss',
|
||||
use_sigmoid=False,
|
||||
loss_weight=0.4,
|
||||
),
|
||||
align_corners=False,
|
||||
),
|
||||
)
|
||||
|
||||
test_cfg = dict(
|
||||
crop_size=(512, 1024),
|
||||
)
|
||||
|
||||
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,
|
||||
),
|
||||
]
|
||||
|
||||
Reference in New Issue
Block a user