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Seg_All_In_One_MMSeg/configs/upernet/README.md
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# UPerNet
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> [Unified Perceptual Parsing for Scene Understanding](https://arxiv.org/pdf/1807.10221.pdf)
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## Introduction
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<!-- [ALGORITHM] -->
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<a href="https://github.com/CSAILVision/unifiedparsing">Official Repo</a>
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<a href="https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/uper_head.py#L13">Code Snippet</a>
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## Abstract
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<!-- [ABSTRACT] -->
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Humans recognize the visual world at multiple levels: we effortlessly categorize scenes and detect objects inside, while also identifying the textures and surfaces of the objects along with their different compositional parts. In this paper, we study a new task called Unified Perceptual Parsing, which requires the machine vision systems to recognize as many visual concepts as possible from a given image. A multi-task framework called UPerNet and a training strategy are developed to learn from heterogeneous image annotations. We benchmark our framework on Unified Perceptual Parsing and show that it is able to effectively segment a wide range of concepts from images. The trained networks are further applied to discover visual knowledge in natural scenes. Models are available at [this https URL](https://github.com/CSAILVision/unifiedparsing).
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<!-- [IMAGE] -->
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<div align=center>
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<img src="https://user-images.githubusercontent.com/24582831/142903077-44e8e0da-7276-4bda-bd2b-0df1680ca845.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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| UPerNet | R-50 | 512x1024 | 40000 | 6.4 | 4.25 | V100 | 77.10 | 78.37 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/upernet/upernet_r50_4xb2-40k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x1024_40k_cityscapes/upernet_r50_512x1024_40k_cityscapes_20200605_094827-aa54cb54.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x1024_40k_cityscapes/upernet_r50_512x1024_40k_cityscapes_20200605_094827.log.json) |
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| UPerNet | R-101 | 512x1024 | 40000 | 7.4 | 3.79 | V100 | 78.69 | 80.11 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/upernet/upernet_r101_4xb2-40k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x1024_40k_cityscapes/upernet_r101_512x1024_40k_cityscapes_20200605_094933-ebce3b10.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x1024_40k_cityscapes/upernet_r101_512x1024_40k_cityscapes_20200605_094933.log.json) |
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| UPerNet | R-50 | 769x769 | 40000 | 7.2 | 1.76 | V100 | 77.98 | 79.70 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/upernet/upernet_r50_4xb2-40k_cityscapes-769x769.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_769x769_40k_cityscapes/upernet_r50_769x769_40k_cityscapes_20200530_033048-92d21539.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_769x769_40k_cityscapes/upernet_r50_769x769_40k_cityscapes_20200530_033048.log.json) |
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| UPerNet | R-101 | 769x769 | 40000 | 8.4 | 1.56 | V100 | 79.03 | 80.77 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/upernet/upernet_r101_4xb2-40k_cityscapes-769x769.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_769x769_40k_cityscapes/upernet_r101_769x769_40k_cityscapes_20200530_040819-83c95d01.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_769x769_40k_cityscapes/upernet_r101_769x769_40k_cityscapes_20200530_040819.log.json) |
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| UPerNet | R-50 | 512x1024 | 80000 | - | - | V100 | 78.19 | 79.19 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/upernet/upernet_r50_4xb2-80k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x1024_80k_cityscapes/upernet_r50_512x1024_80k_cityscapes_20200607_052207-848beca8.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x1024_80k_cityscapes/upernet_r50_512x1024_80k_cityscapes_20200607_052207.log.json) |
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| UPerNet | R-101 | 512x1024 | 80000 | - | - | V100 | 79.40 | 80.46 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/upernet/upernet_r101_4xb2-80k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x1024_80k_cityscapes/upernet_r101_512x1024_80k_cityscapes_20200607_002403-f05f2345.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x1024_80k_cityscapes/upernet_r101_512x1024_80k_cityscapes_20200607_002403.log.json) |
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| UPerNet | R-50 | 769x769 | 80000 | - | - | V100 | 79.39 | 80.92 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/upernet/upernet_r50_4xb2-80k_cityscapes-769x769.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_769x769_80k_cityscapes/upernet_r50_769x769_80k_cityscapes_20200607_005107-82ae7d15.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_769x769_80k_cityscapes/upernet_r50_769x769_80k_cityscapes_20200607_005107.log.json) |
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| UPerNet | R-101 | 769x769 | 80000 | - | - | V100 | 80.10 | 81.49 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/upernet/upernet_r101_4xb2-80k_cityscapes-769x769.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_769x769_80k_cityscapes/upernet_r101_769x769_80k_cityscapes_20200607_001014-082fc334.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_769x769_80k_cityscapes/upernet_r101_769x769_80k_cityscapes_20200607_001014.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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| UPerNet | R-50 | 512x512 | 80000 | 8.1 | 23.40 | V100 | 40.70 | 41.81 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/upernet/upernet_r50_4xb4-80k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_80k_ade20k/upernet_r50_512x512_80k_ade20k_20200614_144127-ecc8377b.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_80k_ade20k/upernet_r50_512x512_80k_ade20k_20200614_144127.log.json) |
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| UPerNet | R-101 | 512x512 | 80000 | 9.1 | 20.34 | V100 | 42.91 | 43.96 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/upernet/upernet_r101_4xb4-80k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_80k_ade20k/upernet_r101_512x512_80k_ade20k_20200614_185117-32e4db94.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_80k_ade20k/upernet_r101_512x512_80k_ade20k_20200614_185117.log.json) |
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| UPerNet | R-50 | 512x512 | 160000 | - | - | V100 | 42.05 | 42.78 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/upernet/upernet_r50_4xb4-160k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_160k_ade20k/upernet_r50_512x512_160k_ade20k_20200615_184328-8534de8d.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_160k_ade20k/upernet_r50_512x512_160k_ade20k_20200615_184328.log.json) |
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| UPerNet | R-101 | 512x512 | 160000 | - | - | V100 | 43.82 | 44.85 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/upernet/upernet_r101_4xb4-160k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_160k_ade20k/upernet_r101_512x512_160k_ade20k_20200615_161951-91b32684.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_160k_ade20k/upernet_r101_512x512_160k_ade20k_20200615_161951.log.json) |
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### Pascal VOC 2012 + Aug
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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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| UPerNet | R-50 | 512x512 | 20000 | 6.4 | 23.17 | V100 | 74.82 | 76.35 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/upernet/upernet_r50_4xb4-20k_voc12aug-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_20k_voc12aug/upernet_r50_512x512_20k_voc12aug_20200617_165330-5b5890a7.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_20k_voc12aug/upernet_r50_512x512_20k_voc12aug_20200617_165330.log.json) |
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| UPerNet | R-101 | 512x512 | 20000 | 7.5 | 19.98 | V100 | 77.10 | 78.29 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/upernet/upernet_r101_4xb4-20k_voc12aug-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_20k_voc12aug/upernet_r101_512x512_20k_voc12aug_20200617_165629-f14e7f27.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_20k_voc12aug/upernet_r101_512x512_20k_voc12aug_20200617_165629.log.json) |
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| UPerNet | R-50 | 512x512 | 40000 | - | - | V100 | 75.92 | 77.44 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/upernet/upernet_r50_4xb4-40k_voc12aug-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_40k_voc12aug/upernet_r50_512x512_40k_voc12aug_20200613_162257-ca9bcc6b.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_40k_voc12aug/upernet_r50_512x512_40k_voc12aug_20200613_162257.log.json) |
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| UPerNet | R-101 | 512x512 | 40000 | - | - | V100 | 77.43 | 78.56 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/upernet/upernet_r101_4xb4-40k_voc12aug-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_40k_voc12aug/upernet_r101_512x512_40k_voc12aug_20200613_163549-e26476ac.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_40k_voc12aug/upernet_r101_512x512_40k_voc12aug_20200613_163549.log.json) |
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## Citation
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```bibtex
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@inproceedings{xiao2018unified,
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title={Unified perceptual parsing for scene understanding},
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author={Xiao, Tete and Liu, Yingcheng and Zhou, Bolei and Jiang, Yuning and Sun, Jian},
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booktitle={Proceedings of the European Conference on Computer Vision (ECCV)},
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pages={418--434},
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year={2018}
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}
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```
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Seg_All_In_One_MMSeg/configs/upernet/metafile.yaml
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Seg_All_In_One_MMSeg/configs/upernet/metafile.yaml
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Collections:
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- Name: UPerNet
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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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- Pascal VOC 2012 + Aug
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Paper:
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Title: Unified Perceptual Parsing for Scene Understanding
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URL: https://arxiv.org/pdf/1807.10221.pdf
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README: configs/upernet/README.md
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Frameworks:
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- PyTorch
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Models:
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- Name: upernet_r50_4xb2-40k_cityscapes-512x1024
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In Collection: UPerNet
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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: 77.1
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mIoU(ms+flip): 78.37
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Config: configs/upernet/upernet_r50_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
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- UPerNet
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Training Resources: 4x V100 GPUS
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Memory (GB): 6.4
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x1024_40k_cityscapes/upernet_r50_512x1024_40k_cityscapes_20200605_094827-aa54cb54.pth
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Training log: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x1024_40k_cityscapes/upernet_r50_512x1024_40k_cityscapes_20200605_094827.log.json
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Paper:
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Title: Unified Perceptual Parsing for Scene Understanding
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URL: https://arxiv.org/pdf/1807.10221.pdf
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Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/uper_head.py#L13
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Framework: PyTorch
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- Name: upernet_r101_4xb2-40k_cityscapes-512x1024
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In Collection: UPerNet
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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: 78.69
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mIoU(ms+flip): 80.11
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Config: configs/upernet/upernet_r101_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-101
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- UPerNet
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Training Resources: 4x V100 GPUS
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Memory (GB): 7.4
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x1024_40k_cityscapes/upernet_r101_512x1024_40k_cityscapes_20200605_094933-ebce3b10.pth
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Training log: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x1024_40k_cityscapes/upernet_r101_512x1024_40k_cityscapes_20200605_094933.log.json
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Paper:
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Title: Unified Perceptual Parsing for Scene Understanding
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URL: https://arxiv.org/pdf/1807.10221.pdf
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Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/uper_head.py#L13
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Framework: PyTorch
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- Name: upernet_r50_4xb2-40k_cityscapes-769x769
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In Collection: UPerNet
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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: 77.98
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mIoU(ms+flip): 79.7
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Config: configs/upernet/upernet_r50_4xb2-40k_cityscapes-769x769.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
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- UPerNet
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Training Resources: 4x V100 GPUS
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Memory (GB): 7.2
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_769x769_40k_cityscapes/upernet_r50_769x769_40k_cityscapes_20200530_033048-92d21539.pth
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Training log: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_769x769_40k_cityscapes/upernet_r50_769x769_40k_cityscapes_20200530_033048.log.json
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Paper:
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Title: Unified Perceptual Parsing for Scene Understanding
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URL: https://arxiv.org/pdf/1807.10221.pdf
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Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/uper_head.py#L13
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Framework: PyTorch
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- Name: upernet_r101_4xb2-40k_cityscapes-769x769
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In Collection: UPerNet
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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: 79.03
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mIoU(ms+flip): 80.77
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Config: configs/upernet/upernet_r101_4xb2-40k_cityscapes-769x769.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-101
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- UPerNet
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Training Resources: 4x V100 GPUS
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Memory (GB): 8.4
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_769x769_40k_cityscapes/upernet_r101_769x769_40k_cityscapes_20200530_040819-83c95d01.pth
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Training log: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_769x769_40k_cityscapes/upernet_r101_769x769_40k_cityscapes_20200530_040819.log.json
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Paper:
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Title: Unified Perceptual Parsing for Scene Understanding
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URL: https://arxiv.org/pdf/1807.10221.pdf
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Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/uper_head.py#L13
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Framework: PyTorch
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- Name: upernet_r50_4xb2-80k_cityscapes-512x1024
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In Collection: UPerNet
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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: 78.19
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mIoU(ms+flip): 79.19
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Config: configs/upernet/upernet_r50_4xb2-80k_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
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- UPerNet
|
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Training Resources: 4x V100 GPUS
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x1024_80k_cityscapes/upernet_r50_512x1024_80k_cityscapes_20200607_052207-848beca8.pth
|
||||
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x1024_80k_cityscapes/upernet_r50_512x1024_80k_cityscapes_20200607_052207.log.json
|
||||
Paper:
|
||||
Title: Unified Perceptual Parsing for Scene Understanding
|
||||
URL: https://arxiv.org/pdf/1807.10221.pdf
|
||||
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/uper_head.py#L13
|
||||
Framework: PyTorch
|
||||
- Name: upernet_r101_4xb2-80k_cityscapes-512x1024
|
||||
In Collection: UPerNet
|
||||
Results:
|
||||
Task: Semantic Segmentation
|
||||
Dataset: Cityscapes
|
||||
Metrics:
|
||||
mIoU: 79.4
|
||||
mIoU(ms+flip): 80.46
|
||||
Config: configs/upernet/upernet_r101_4xb2-80k_cityscapes-512x1024.py
|
||||
Metadata:
|
||||
Training Data: Cityscapes
|
||||
Batch Size: 8
|
||||
Architecture:
|
||||
- R-101
|
||||
- UPerNet
|
||||
Training Resources: 4x V100 GPUS
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x1024_80k_cityscapes/upernet_r101_512x1024_80k_cityscapes_20200607_002403-f05f2345.pth
|
||||
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x1024_80k_cityscapes/upernet_r101_512x1024_80k_cityscapes_20200607_002403.log.json
|
||||
Paper:
|
||||
Title: Unified Perceptual Parsing for Scene Understanding
|
||||
URL: https://arxiv.org/pdf/1807.10221.pdf
|
||||
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/uper_head.py#L13
|
||||
Framework: PyTorch
|
||||
- Name: upernet_r50_4xb2-80k_cityscapes-769x769
|
||||
In Collection: UPerNet
|
||||
Results:
|
||||
Task: Semantic Segmentation
|
||||
Dataset: Cityscapes
|
||||
Metrics:
|
||||
mIoU: 79.39
|
||||
mIoU(ms+flip): 80.92
|
||||
Config: configs/upernet/upernet_r50_4xb2-80k_cityscapes-769x769.py
|
||||
Metadata:
|
||||
Training Data: Cityscapes
|
||||
Batch Size: 8
|
||||
Architecture:
|
||||
- R-50
|
||||
- UPerNet
|
||||
Training Resources: 4x V100 GPUS
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_769x769_80k_cityscapes/upernet_r50_769x769_80k_cityscapes_20200607_005107-82ae7d15.pth
|
||||
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_769x769_80k_cityscapes/upernet_r50_769x769_80k_cityscapes_20200607_005107.log.json
|
||||
Paper:
|
||||
Title: Unified Perceptual Parsing for Scene Understanding
|
||||
URL: https://arxiv.org/pdf/1807.10221.pdf
|
||||
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/uper_head.py#L13
|
||||
Framework: PyTorch
|
||||
- Name: upernet_r101_4xb2-80k_cityscapes-769x769
|
||||
In Collection: UPerNet
|
||||
Results:
|
||||
Task: Semantic Segmentation
|
||||
Dataset: Cityscapes
|
||||
Metrics:
|
||||
mIoU: 80.1
|
||||
mIoU(ms+flip): 81.49
|
||||
Config: configs/upernet/upernet_r101_4xb2-80k_cityscapes-769x769.py
|
||||
Metadata:
|
||||
Training Data: Cityscapes
|
||||
Batch Size: 8
|
||||
Architecture:
|
||||
- R-101
|
||||
- UPerNet
|
||||
Training Resources: 4x V100 GPUS
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_769x769_80k_cityscapes/upernet_r101_769x769_80k_cityscapes_20200607_001014-082fc334.pth
|
||||
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_769x769_80k_cityscapes/upernet_r101_769x769_80k_cityscapes_20200607_001014.log.json
|
||||
Paper:
|
||||
Title: Unified Perceptual Parsing for Scene Understanding
|
||||
URL: https://arxiv.org/pdf/1807.10221.pdf
|
||||
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/uper_head.py#L13
|
||||
Framework: PyTorch
|
||||
- Name: upernet_r50_4xb4-80k_ade20k-512x512
|
||||
In Collection: UPerNet
|
||||
Results:
|
||||
Task: Semantic Segmentation
|
||||
Dataset: ADE20K
|
||||
Metrics:
|
||||
mIoU: 40.7
|
||||
mIoU(ms+flip): 41.81
|
||||
Config: configs/upernet/upernet_r50_4xb4-80k_ade20k-512x512.py
|
||||
Metadata:
|
||||
Training Data: ADE20K
|
||||
Batch Size: 16
|
||||
Architecture:
|
||||
- R-50
|
||||
- UPerNet
|
||||
Training Resources: 4x V100 GPUS
|
||||
Memory (GB): 8.1
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_80k_ade20k/upernet_r50_512x512_80k_ade20k_20200614_144127-ecc8377b.pth
|
||||
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_80k_ade20k/upernet_r50_512x512_80k_ade20k_20200614_144127.log.json
|
||||
Paper:
|
||||
Title: Unified Perceptual Parsing for Scene Understanding
|
||||
URL: https://arxiv.org/pdf/1807.10221.pdf
|
||||
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/uper_head.py#L13
|
||||
Framework: PyTorch
|
||||
- Name: upernet_r101_4xb4-80k_ade20k-512x512
|
||||
In Collection: UPerNet
|
||||
Results:
|
||||
Task: Semantic Segmentation
|
||||
Dataset: ADE20K
|
||||
Metrics:
|
||||
mIoU: 42.91
|
||||
mIoU(ms+flip): 43.96
|
||||
Config: configs/upernet/upernet_r101_4xb4-80k_ade20k-512x512.py
|
||||
Metadata:
|
||||
Training Data: ADE20K
|
||||
Batch Size: 16
|
||||
Architecture:
|
||||
- R-101
|
||||
- UPerNet
|
||||
Training Resources: 4x V100 GPUS
|
||||
Memory (GB): 9.1
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_80k_ade20k/upernet_r101_512x512_80k_ade20k_20200614_185117-32e4db94.pth
|
||||
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_80k_ade20k/upernet_r101_512x512_80k_ade20k_20200614_185117.log.json
|
||||
Paper:
|
||||
Title: Unified Perceptual Parsing for Scene Understanding
|
||||
URL: https://arxiv.org/pdf/1807.10221.pdf
|
||||
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/uper_head.py#L13
|
||||
Framework: PyTorch
|
||||
- Name: upernet_r50_4xb4-160k_ade20k-512x512
|
||||
In Collection: UPerNet
|
||||
Results:
|
||||
Task: Semantic Segmentation
|
||||
Dataset: ADE20K
|
||||
Metrics:
|
||||
mIoU: 42.05
|
||||
mIoU(ms+flip): 42.78
|
||||
Config: configs/upernet/upernet_r50_4xb4-160k_ade20k-512x512.py
|
||||
Metadata:
|
||||
Training Data: ADE20K
|
||||
Batch Size: 16
|
||||
Architecture:
|
||||
- R-50
|
||||
- UPerNet
|
||||
Training Resources: 4x V100 GPUS
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_160k_ade20k/upernet_r50_512x512_160k_ade20k_20200615_184328-8534de8d.pth
|
||||
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_160k_ade20k/upernet_r50_512x512_160k_ade20k_20200615_184328.log.json
|
||||
Paper:
|
||||
Title: Unified Perceptual Parsing for Scene Understanding
|
||||
URL: https://arxiv.org/pdf/1807.10221.pdf
|
||||
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/uper_head.py#L13
|
||||
Framework: PyTorch
|
||||
- Name: upernet_r101_4xb4-160k_ade20k-512x512
|
||||
In Collection: UPerNet
|
||||
Results:
|
||||
Task: Semantic Segmentation
|
||||
Dataset: ADE20K
|
||||
Metrics:
|
||||
mIoU: 43.82
|
||||
mIoU(ms+flip): 44.85
|
||||
Config: configs/upernet/upernet_r101_4xb4-160k_ade20k-512x512.py
|
||||
Metadata:
|
||||
Training Data: ADE20K
|
||||
Batch Size: 16
|
||||
Architecture:
|
||||
- R-101
|
||||
- UPerNet
|
||||
Training Resources: 4x V100 GPUS
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_160k_ade20k/upernet_r101_512x512_160k_ade20k_20200615_161951-91b32684.pth
|
||||
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_160k_ade20k/upernet_r101_512x512_160k_ade20k_20200615_161951.log.json
|
||||
Paper:
|
||||
Title: Unified Perceptual Parsing for Scene Understanding
|
||||
URL: https://arxiv.org/pdf/1807.10221.pdf
|
||||
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/uper_head.py#L13
|
||||
Framework: PyTorch
|
||||
- Name: upernet_r50_4xb4-20k_voc12aug-512x512
|
||||
In Collection: UPerNet
|
||||
Results:
|
||||
Task: Semantic Segmentation
|
||||
Dataset: Pascal VOC 2012 + Aug
|
||||
Metrics:
|
||||
mIoU: 74.82
|
||||
mIoU(ms+flip): 76.35
|
||||
Config: configs/upernet/upernet_r50_4xb4-20k_voc12aug-512x512.py
|
||||
Metadata:
|
||||
Training Data: Pascal VOC 2012 + Aug
|
||||
Batch Size: 16
|
||||
Architecture:
|
||||
- R-50
|
||||
- UPerNet
|
||||
Training Resources: 4x V100 GPUS
|
||||
Memory (GB): 6.4
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_20k_voc12aug/upernet_r50_512x512_20k_voc12aug_20200617_165330-5b5890a7.pth
|
||||
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_20k_voc12aug/upernet_r50_512x512_20k_voc12aug_20200617_165330.log.json
|
||||
Paper:
|
||||
Title: Unified Perceptual Parsing for Scene Understanding
|
||||
URL: https://arxiv.org/pdf/1807.10221.pdf
|
||||
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/uper_head.py#L13
|
||||
Framework: PyTorch
|
||||
- Name: upernet_r101_4xb4-20k_voc12aug-512x512
|
||||
In Collection: UPerNet
|
||||
Results:
|
||||
Task: Semantic Segmentation
|
||||
Dataset: Pascal VOC 2012 + Aug
|
||||
Metrics:
|
||||
mIoU: 77.1
|
||||
mIoU(ms+flip): 78.29
|
||||
Config: configs/upernet/upernet_r101_4xb4-20k_voc12aug-512x512.py
|
||||
Metadata:
|
||||
Training Data: Pascal VOC 2012 + Aug
|
||||
Batch Size: 16
|
||||
Architecture:
|
||||
- R-101
|
||||
- UPerNet
|
||||
Training Resources: 4x V100 GPUS
|
||||
Memory (GB): 7.5
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_20k_voc12aug/upernet_r101_512x512_20k_voc12aug_20200617_165629-f14e7f27.pth
|
||||
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_20k_voc12aug/upernet_r101_512x512_20k_voc12aug_20200617_165629.log.json
|
||||
Paper:
|
||||
Title: Unified Perceptual Parsing for Scene Understanding
|
||||
URL: https://arxiv.org/pdf/1807.10221.pdf
|
||||
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/uper_head.py#L13
|
||||
Framework: PyTorch
|
||||
- Name: upernet_r50_4xb4-40k_voc12aug-512x512
|
||||
In Collection: UPerNet
|
||||
Results:
|
||||
Task: Semantic Segmentation
|
||||
Dataset: Pascal VOC 2012 + Aug
|
||||
Metrics:
|
||||
mIoU: 75.92
|
||||
mIoU(ms+flip): 77.44
|
||||
Config: configs/upernet/upernet_r50_4xb4-40k_voc12aug-512x512.py
|
||||
Metadata:
|
||||
Training Data: Pascal VOC 2012 + Aug
|
||||
Batch Size: 16
|
||||
Architecture:
|
||||
- R-50
|
||||
- UPerNet
|
||||
Training Resources: 4x V100 GPUS
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_40k_voc12aug/upernet_r50_512x512_40k_voc12aug_20200613_162257-ca9bcc6b.pth
|
||||
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_40k_voc12aug/upernet_r50_512x512_40k_voc12aug_20200613_162257.log.json
|
||||
Paper:
|
||||
Title: Unified Perceptual Parsing for Scene Understanding
|
||||
URL: https://arxiv.org/pdf/1807.10221.pdf
|
||||
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/uper_head.py#L13
|
||||
Framework: PyTorch
|
||||
- Name: upernet_r101_4xb4-40k_voc12aug-512x512
|
||||
In Collection: UPerNet
|
||||
Results:
|
||||
Task: Semantic Segmentation
|
||||
Dataset: Pascal VOC 2012 + Aug
|
||||
Metrics:
|
||||
mIoU: 77.43
|
||||
mIoU(ms+flip): 78.56
|
||||
Config: configs/upernet/upernet_r101_4xb4-40k_voc12aug-512x512.py
|
||||
Metadata:
|
||||
Training Data: Pascal VOC 2012 + Aug
|
||||
Batch Size: 16
|
||||
Architecture:
|
||||
- R-101
|
||||
- UPerNet
|
||||
Training Resources: 4x V100 GPUS
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_40k_voc12aug/upernet_r101_512x512_40k_voc12aug_20200613_163549-e26476ac.pth
|
||||
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_40k_voc12aug/upernet_r101_512x512_40k_voc12aug_20200613_163549.log.json
|
||||
Paper:
|
||||
Title: Unified Perceptual Parsing for Scene Understanding
|
||||
URL: https://arxiv.org/pdf/1807.10221.pdf
|
||||
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/uper_head.py#L13
|
||||
Framework: PyTorch
|
||||
@@ -0,0 +1,2 @@
|
||||
_base_ = './upernet_r50_4xb2-40k_cityscapes-512x1024.py'
|
||||
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
|
||||
@@ -0,0 +1,2 @@
|
||||
_base_ = './upernet_r50_4xb2-40k_cityscapes-769x769.py'
|
||||
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
|
||||
@@ -0,0 +1,2 @@
|
||||
_base_ = './upernet_r50_4xb2-80k_cityscapes-512x1024.py'
|
||||
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
|
||||
@@ -0,0 +1,2 @@
|
||||
_base_ = './upernet_r50_4xb2-80k_cityscapes-769x769.py'
|
||||
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
|
||||
@@ -0,0 +1,2 @@
|
||||
_base_ = './upernet_r50_4xb4-160k_ade20k-512x512.py'
|
||||
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
|
||||
@@ -0,0 +1,2 @@
|
||||
_base_ = './upernet_r50_4xb4-20k_voc12aug-512x512.py'
|
||||
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
|
||||
@@ -0,0 +1,2 @@
|
||||
_base_ = './upernet_r50_4xb4-40k_voc12aug-512x512.py'
|
||||
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
|
||||
@@ -0,0 +1,2 @@
|
||||
_base_ = './upernet_r50_4xb4-80k_ade20k-512x512.py'
|
||||
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
|
||||
@@ -0,0 +1,6 @@
|
||||
_base_ = './upernet_r50_4xb2-40k_cityscapes-512x1024.py'
|
||||
model = dict(
|
||||
pretrained='open-mmlab://resnet18_v1c',
|
||||
backbone=dict(depth=18),
|
||||
decode_head=dict(in_channels=[64, 128, 256, 512]),
|
||||
auxiliary_head=dict(in_channels=256))
|
||||
@@ -0,0 +1,6 @@
|
||||
_base_ = './upernet_r50_4xb2-80k_cityscapes-512x1024.py'
|
||||
model = dict(
|
||||
pretrained='open-mmlab://resnet18_v1c',
|
||||
backbone=dict(depth=18),
|
||||
decode_head=dict(in_channels=[64, 128, 256, 512]),
|
||||
auxiliary_head=dict(in_channels=256))
|
||||
@@ -0,0 +1,9 @@
|
||||
_base_ = [
|
||||
'../_base_/models/upernet_r50.py', '../_base_/datasets/ade20k.py',
|
||||
'../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py'
|
||||
]
|
||||
model = dict(
|
||||
pretrained='open-mmlab://resnet18_v1c',
|
||||
backbone=dict(depth=18),
|
||||
decode_head=dict(in_channels=[64, 128, 256, 512], num_classes=150),
|
||||
auxiliary_head=dict(in_channels=256, num_classes=150))
|
||||
@@ -0,0 +1,10 @@
|
||||
_base_ = [
|
||||
'../_base_/models/upernet_r50.py',
|
||||
'../_base_/datasets/pascal_voc12_aug.py', '../_base_/default_runtime.py',
|
||||
'../_base_/schedules/schedule_20k.py'
|
||||
]
|
||||
model = dict(
|
||||
pretrained='open-mmlab://resnet18_v1c',
|
||||
backbone=dict(depth=18),
|
||||
decode_head=dict(in_channels=[64, 128, 256, 512], num_classes=21),
|
||||
auxiliary_head=dict(in_channels=256, num_classes=21))
|
||||
@@ -0,0 +1,10 @@
|
||||
_base_ = [
|
||||
'../_base_/models/upernet_r50.py',
|
||||
'../_base_/datasets/pascal_voc12_aug.py', '../_base_/default_runtime.py',
|
||||
'../_base_/schedules/schedule_40k.py'
|
||||
]
|
||||
model = dict(
|
||||
pretrained='open-mmlab://resnet18_v1c',
|
||||
backbone=dict(depth=18),
|
||||
decode_head=dict(in_channels=[64, 128, 256, 512], num_classes=21),
|
||||
auxiliary_head=dict(in_channels=256, num_classes=21))
|
||||
@@ -0,0 +1,9 @@
|
||||
_base_ = [
|
||||
'../_base_/models/upernet_r50.py', '../_base_/datasets/ade20k.py',
|
||||
'../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
|
||||
]
|
||||
model = dict(
|
||||
pretrained='open-mmlab://resnet18_v1c',
|
||||
backbone=dict(depth=18),
|
||||
decode_head=dict(in_channels=[64, 128, 256, 512], num_classes=150),
|
||||
auxiliary_head=dict(in_channels=256, num_classes=150))
|
||||
@@ -0,0 +1,7 @@
|
||||
_base_ = [
|
||||
'../_base_/models/upernet_r50.py', '../_base_/datasets/cityscapes.py',
|
||||
'../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py'
|
||||
]
|
||||
crop_size = (512, 1024)
|
||||
data_preprocessor = dict(size=crop_size)
|
||||
model = dict(data_preprocessor=data_preprocessor)
|
||||
@@ -0,0 +1,12 @@
|
||||
_base_ = [
|
||||
'../_base_/models/upernet_r50.py',
|
||||
'../_base_/datasets/cityscapes_769x769.py', '../_base_/default_runtime.py',
|
||||
'../_base_/schedules/schedule_40k.py'
|
||||
]
|
||||
crop_size = (769, 769)
|
||||
data_preprocessor = dict(size=crop_size)
|
||||
model = dict(
|
||||
data_preprocessor=data_preprocessor,
|
||||
decode_head=dict(align_corners=True),
|
||||
auxiliary_head=dict(align_corners=True),
|
||||
test_cfg=dict(mode='slide', crop_size=(769, 769), stride=(513, 513)))
|
||||
@@ -0,0 +1,7 @@
|
||||
_base_ = [
|
||||
'../_base_/models/upernet_r50.py', '../_base_/datasets/cityscapes.py',
|
||||
'../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
|
||||
]
|
||||
crop_size = (512, 1024)
|
||||
data_preprocessor = dict(size=crop_size)
|
||||
model = dict(data_preprocessor=data_preprocessor)
|
||||
@@ -0,0 +1,12 @@
|
||||
_base_ = [
|
||||
'../_base_/models/upernet_r50.py',
|
||||
'../_base_/datasets/cityscapes_769x769.py', '../_base_/default_runtime.py',
|
||||
'../_base_/schedules/schedule_80k.py'
|
||||
]
|
||||
crop_size = (769, 769)
|
||||
data_preprocessor = dict(size=crop_size)
|
||||
model = dict(
|
||||
data_preprocessor=data_preprocessor,
|
||||
decode_head=dict(align_corners=True),
|
||||
auxiliary_head=dict(align_corners=True),
|
||||
test_cfg=dict(mode='slide', crop_size=(769, 769), stride=(513, 513)))
|
||||
@@ -0,0 +1,10 @@
|
||||
_base_ = [
|
||||
'../_base_/models/upernet_r50.py', '../_base_/datasets/ade20k.py',
|
||||
'../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py'
|
||||
]
|
||||
crop_size = (512, 512)
|
||||
data_preprocessor = dict(size=crop_size)
|
||||
model = dict(
|
||||
data_preprocessor=data_preprocessor,
|
||||
decode_head=dict(num_classes=150),
|
||||
auxiliary_head=dict(num_classes=150))
|
||||
@@ -0,0 +1,11 @@
|
||||
_base_ = [
|
||||
'../_base_/models/upernet_r50.py',
|
||||
'../_base_/datasets/pascal_voc12_aug.py', '../_base_/default_runtime.py',
|
||||
'../_base_/schedules/schedule_20k.py'
|
||||
]
|
||||
crop_size = (512, 512)
|
||||
data_preprocessor = dict(size=crop_size)
|
||||
model = dict(
|
||||
data_preprocessor=data_preprocessor,
|
||||
decode_head=dict(num_classes=21),
|
||||
auxiliary_head=dict(num_classes=21))
|
||||
@@ -0,0 +1,11 @@
|
||||
_base_ = [
|
||||
'../_base_/models/upernet_r50.py',
|
||||
'../_base_/datasets/pascal_voc12_aug.py', '../_base_/default_runtime.py',
|
||||
'../_base_/schedules/schedule_40k.py'
|
||||
]
|
||||
crop_size = (512, 512)
|
||||
data_preprocessor = dict(size=crop_size)
|
||||
model = dict(
|
||||
data_preprocessor=data_preprocessor,
|
||||
decode_head=dict(num_classes=21),
|
||||
auxiliary_head=dict(num_classes=21))
|
||||
@@ -0,0 +1,10 @@
|
||||
_base_ = [
|
||||
'../_base_/models/upernet_r50.py', '../_base_/datasets/ade20k.py',
|
||||
'../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
|
||||
]
|
||||
crop_size = (512, 512)
|
||||
data_preprocessor = dict(size=crop_size)
|
||||
model = dict(
|
||||
data_preprocessor=data_preprocessor,
|
||||
decode_head=dict(num_classes=150),
|
||||
auxiliary_head=dict(num_classes=150))
|
||||
Reference in New Issue
Block a user