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Seg_All_In_One_MMSeg/configs/deeplabv3plus/README.md
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# DeepLabV3+
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> [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://arxiv.org/abs/1802.02611)
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## Introduction
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<!-- [ALGORITHM] -->
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<a href="https://github.com/tensorflow/models/tree/master/research/deeplab">Official Repo</a>
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<a href="https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/sep_aspp_head.py#L30">Code Snippet</a>
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## Abstract
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<!-- [ABSTRACT] -->
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Spatial pyramid pooling module or encode-decoder structure are used in deep neural networks for semantic segmentation task. The former networks are able to encode multi-scale contextual information by probing the incoming features with filters or pooling operations at multiple rates and multiple effective fields-of-view, while the latter networks can capture sharper object boundaries by gradually recovering the spatial information. In this work, we propose to combine the advantages from both methods. Specifically, our proposed model, DeepLabv3+, extends DeepLabv3 by adding a simple yet effective decoder module to refine the segmentation results especially along object boundaries. We further explore the Xception model and apply the depthwise separable convolution to both Atrous Spatial Pyramid Pooling and decoder modules, resulting in a faster and stronger encoder-decoder network. We demonstrate the effectiveness of the proposed model on PASCAL VOC 2012 and Cityscapes datasets, achieving the test set performance of 89.0% and 82.1% without any post-processing. Our paper is accompanied with a publicly available reference implementation of the proposed models in Tensorflow at [this https URL](https://github.com/tensorflow/models/tree/master/research/deeplab).
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<!-- [IMAGE] -->
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<div align=center>
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<img src="https://user-images.githubusercontent.com/24582831/142900680-3e2c3098-8341-4760-bbfd-b1d7d29968ea.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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| DeepLabV3+ | R-50-D8 | 512x1024 | 40000 | 7.5 | 3.94 | V100 | 79.61 | 81.01 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/deeplabv3plus/deeplabv3plus_r50-d8_4xb2-40k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x1024_40k_cityscapes/deeplabv3plus_r50-d8_512x1024_40k_cityscapes_20200605_094610-d222ffcd.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x1024_40k_cityscapes/deeplabv3plus_r50-d8_512x1024_40k_cityscapes_20200605_094610.log.json) |
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| DeepLabV3+ | R-101-D8 | 512x1024 | 40000 | 11 | 2.60 | V100 | 80.21 | 81.82 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb2-40k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x1024_40k_cityscapes/deeplabv3plus_r101-d8_512x1024_40k_cityscapes_20200605_094614-3769eecf.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x1024_40k_cityscapes/deeplabv3plus_r101-d8_512x1024_40k_cityscapes_20200605_094614.log.json) |
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| DeepLabV3+ | R-50-D8 | 769x769 | 40000 | 8.5 | 1.72 | V100 | 78.97 | 80.46 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/deeplabv3plus/deeplabv3plus_r50-d8_4xb2-40k_cityscapes-769x769.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_769x769_40k_cityscapes/deeplabv3plus_r50-d8_769x769_40k_cityscapes_20200606_114143-1dcb0e3c.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_769x769_40k_cityscapes/deeplabv3plus_r50-d8_769x769_40k_cityscapes_20200606_114143.log.json) |
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| DeepLabV3+ | R-101-D8 | 769x769 | 40000 | 12.5 | 1.15 | V100 | 79.46 | 80.50 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb2-40k_cityscapes-769x769.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_769x769_40k_cityscapes/deeplabv3plus_r101-d8_769x769_40k_cityscapes_20200606_114304-ff414b9e.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_769x769_40k_cityscapes/deeplabv3plus_r101-d8_769x769_40k_cityscapes_20200606_114304.log.json) |
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| DeepLabV3+ | R-18-D8 | 512x1024 | 80000 | 2.2 | 14.27 | V100 | 76.89 | 78.76 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/deeplabv3plus/deeplabv3plus_r18-d8_4xb2-80k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_512x1024_80k_cityscapes/deeplabv3plus_r18-d8_512x1024_80k_cityscapes_20201226_080942-cff257fe.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_512x1024_80k_cityscapes/deeplabv3plus_r18-d8_512x1024_80k_cityscapes-20201226_080942.log.json) |
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| DeepLabV3+ | R-50-D8 | 512x1024 | 80000 | - | - | V100 | 80.09 | 81.13 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/deeplabv3plus/deeplabv3plus_r50-d8_4xb2-80k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x1024_80k_cityscapes/deeplabv3plus_r50-d8_512x1024_80k_cityscapes_20200606_114049-f9fb496d.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x1024_80k_cityscapes/deeplabv3plus_r50-d8_512x1024_80k_cityscapes_20200606_114049.log.json) |
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| DeepLabV3+ | R-101-D8 | 512x1024 | 80000 | - | - | V100 | 80.97 | 82.03 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb2-80k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x1024_80k_cityscapes/deeplabv3plus_r101-d8_512x1024_80k_cityscapes_20200606_114143-068fcfe9.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x1024_80k_cityscapes/deeplabv3plus_r101-d8_512x1024_80k_cityscapes_20200606_114143.log.json) |
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| DeepLabV3+ (FP16) | R-101-D8 | 512x1024 | 80000 | 6.35 | 7.87 | V100 | 80.46 | - | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb2-amp-80k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_fp16_512x1024_80k_cityscapes/deeplabv3plus_r101-d8_fp16_512x1024_80k_cityscapes_20200717_230920-f1104f4b.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_fp16_512x1024_80k_cityscapes/deeplabv3plus_r101-d8_fp16_512x1024_80k_cityscapes_20200717_230920.log.json) |
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| DeepLabV3+ | R-18-D8 | 769x769 | 80000 | 2.5 | 5.74 | V100 | 76.26 | 77.91 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/deeplabv3plus/deeplabv3plus_r18-d8_4xb2-80k_cityscapes-769x769.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_769x769_80k_cityscapes/deeplabv3plus_r18-d8_769x769_80k_cityscapes_20201226_083346-f326e06a.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_769x769_80k_cityscapes/deeplabv3plus_r18-d8_769x769_80k_cityscapes-20201226_083346.log.json) |
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| DeepLabV3+ | R-50-D8 | 769x769 | 80000 | - | - | V100 | 79.83 | 81.48 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/deeplabv3plus/deeplabv3plus_r50-d8_4xb2-80k_cityscapes-769x769.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_769x769_80k_cityscapes/deeplabv3plus_r50-d8_769x769_80k_cityscapes_20200606_210233-0e9dfdc4.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_769x769_80k_cityscapes/deeplabv3plus_r50-d8_769x769_80k_cityscapes_20200606_210233.log.json) |
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| DeepLabV3+ | R-101-D8 | 769x769 | 80000 | - | - | V100 | 80.65 | 81.47 | [config<sup>\[1\]</sup>](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb2-80k_cityscapes-769x769.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_769x769_80k_cityscapes/deeplabv3plus_r101-d8_769x769_80k_cityscapes_20220406_154720-dfcc0b68.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_769x769_80k_cityscapes/deeplabv3plus_r101-d8_769x769_80k_cityscapes_20220406_154720.log.json) |
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| DeepLabV3+ | R-101-D16-MG124 | 512x1024 | 40000 | 5.8 | 7.48 | V100 | 79.09 | 80.36 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/deeplabv3plus/ddeeplabv3plus_r101-d16-mg124_4xb2-40k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d16-mg124_512x1024_40k_cityscapes/deeplabv3plus_r101-d16-mg124_512x1024_40k_cityscapes_20200908_005644-cf9ce186.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d16-mg124_512x1024_40k_cityscapes/deeplabv3plus_r101-d16-mg124_512x1024_40k_cityscapes-20200908_005644.log.json) |
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| DeepLabV3+ | R-101-D16-MG124 | 512x1024 | 80000 | 9.9 | - | V100 | 79.90 | 81.33 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/deeplabv3plus/deeplabv3plus_r101-d16-mg124_4xb2-80k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d16-mg124_512x1024_80k_cityscapes/deeplabv3plus_r101-d16-mg124_512x1024_80k_cityscapes_20200908_005644-ee6158e0.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d16-mg124_512x1024_80k_cityscapes/deeplabv3plus_r101-d16-mg124_512x1024_80k_cityscapes-20200908_005644.log.json) |
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| DeepLabV3+ | R-18b-D8 | 512x1024 | 80000 | 2.1 | 14.95 | V100 | 75.87 | 77.52 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/deeplabv3plus/deeplabv3plus_r18b-d8_4xb2-80k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18b-d8_512x1024_80k_cityscapes/deeplabv3plus_r18b-d8_512x1024_80k_cityscapes_20201226_090828-e451abd9.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18b-d8_512x1024_80k_cityscapes/deeplabv3plus_r18b-d8_512x1024_80k_cityscapes-20201226_090828.log.json) |
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| DeepLabV3+ | R-50b-D8 | 512x1024 | 80000 | 7.4 | 3.94 | V100 | 80.28 | 81.44 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/deeplabv3plus/deeplabv3plus_r50b-d8_4xb2-80k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50b-d8_512x1024_80k_cityscapes/deeplabv3plus_r50b-d8_512x1024_80k_cityscapes_20201225_213645-a97e4e43.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50b-d8_512x1024_80k_cityscapes/deeplabv3plus_r50b-d8_512x1024_80k_cityscapes-20201225_213645.log.json) |
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| DeepLabV3+ | R-101b-D8 | 512x1024 | 80000 | 10.9 | 2.60 | V100 | 80.16 | 81.41 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/deeplabv3plus/deeplabv3plus_r101b-d8_4xb2-80k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101b-d8_512x1024_80k_cityscapes/deeplabv3plus_r101b-d8_512x1024_80k_cityscapes_20201226_190843-9c3c93a4.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101b-d8_512x1024_80k_cityscapes/deeplabv3plus_r101b-d8_512x1024_80k_cityscapes-20201226_190843.log.json) |
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| DeepLabV3+ | R-18b-D8 | 769x769 | 80000 | 2.4 | 5.96 | V100 | 76.36 | 78.24 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/deeplabv3plus/deeplabv3plus_r18b-d8_4xb2-80k_cityscapes-769x769.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18b-d8_769x769_80k_cityscapes/deeplabv3plus_r18b-d8_769x769_80k_cityscapes_20201226_151312-2c868aff.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18b-d8_769x769_80k_cityscapes/deeplabv3plus_r18b-d8_769x769_80k_cityscapes-20201226_151312.log.json) |
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| DeepLabV3+ | R-50b-D8 | 769x769 | 80000 | 8.4 | 1.72 | V100 | 79.41 | 80.56 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/deeplabv3plus/deeplabv3plus_r50b-d8_4xb2-80k_cityscapes-769x769.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50b-d8_769x769_80k_cityscapes/deeplabv3plus_r50b-d8_769x769_80k_cityscapes_20201225_224655-8b596d1c.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50b-d8_769x769_80k_cityscapes/deeplabv3plus_r50b-d8_769x769_80k_cityscapes-20201225_224655.log.json) |
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| DeepLabV3+ | R-101b-D8 | 769x769 | 80000 | 12.3 | 1.10 | V100 | 79.88 | 81.46 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/deeplabv3plus/deeplabv3plus_r101b-d8_4xb2-80k_cityscapes-769x769.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101b-d8_769x769_80k_cityscapes/deeplabv3plus_r101b-d8_769x769_80k_cityscapes_20201226_205041-227cdf7c.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101b-d8_769x769_80k_cityscapes/deeplabv3plus_r101b-d8_769x769_80k_cityscapes-20201226_205041.log.json) |
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\[1\] The training of the model is sensitive to random seed, and the seed to train it is 1111.
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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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| DeepLabV3+ | R-50-D8 | 512x512 | 80000 | 10.6 | 21.01 | V100 | 42.72 | 43.75 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/deeplabv3plus/deeplabv3plus_r50-d8_4xb4-80k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_80k_ade20k/deeplabv3plus_r50-d8_512x512_80k_ade20k_20200614_185028-bf1400d8.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_80k_ade20k/deeplabv3plus_r50-d8_512x512_80k_ade20k_20200614_185028.log.json) |
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| DeepLabV3+ | R-101-D8 | 512x512 | 80000 | 14.1 | 14.16 | V100 | 44.60 | 46.06 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb4-160k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_80k_ade20k/deeplabv3plus_r101-d8_512x512_80k_ade20k_20200615_014139-d5730af7.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_80k_ade20k/deeplabv3plus_r101-d8_512x512_80k_ade20k_20200615_014139.log.json) |
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| DeepLabV3+ | R-50-D8 | 512x512 | 160000 | - | - | V100 | 43.95 | 44.93 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/deeplabv3plus/deeplabv3plus_r50-d8_4xb4-160k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_160k_ade20k/deeplabv3plus_r50-d8_512x512_160k_ade20k_20200615_124504-6135c7e0.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_160k_ade20k/deeplabv3plus_r50-d8_512x512_160k_ade20k_20200615_124504.log.json) |
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| DeepLabV3+ | R-101-D8 | 512x512 | 160000 | - | - | V100 | 45.47 | 46.35 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb4-160k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_160k_ade20k/deeplabv3plus_r101-d8_512x512_160k_ade20k_20200615_123232-38ed86bb.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_160k_ade20k/deeplabv3plus_r101-d8_512x512_160k_ade20k_20200615_123232.log.json) |
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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 |
|
||||
| ---------- | -------- | --------- | ------: | -------- | -------------- | ------ | ----: | ------------: | ----------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
||||
| DeepLabV3+ | R-50-D8 | 512x512 | 20000 | 7.6 | 21 | V100 | 75.93 | 77.50 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/deeplabv3plus/deeplabv3plus_r50-d8_4xb4-20k_voc12aug-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_20k_voc12aug/deeplabv3plus_r50-d8_512x512_20k_voc12aug_20200617_102323-aad58ef1.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_20k_voc12aug/deeplabv3plus_r50-d8_512x512_20k_voc12aug_20200617_102323.log.json) |
|
||||
| DeepLabV3+ | R-101-D8 | 512x512 | 20000 | 11 | 13.88 | V100 | 77.22 | 78.59 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb4-20k_voc12aug-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_20k_voc12aug/deeplabv3plus_r101-d8_512x512_20k_voc12aug_20200617_102345-c7ff3d56.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_20k_voc12aug/deeplabv3plus_r101-d8_512x512_20k_voc12aug_20200617_102345.log.json) |
|
||||
| DeepLabV3+ | R-50-D8 | 512x512 | 40000 | - | - | V100 | 76.81 | 77.57 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/deeplabv3plus/deeplabv3plus_r50-d8_4xb4-40k_voc12aug-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_40k_voc12aug/deeplabv3plus_r50-d8_512x512_40k_voc12aug_20200613_161759-e1b43aa9.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_40k_voc12aug/deeplabv3plus_r50-d8_512x512_40k_voc12aug_20200613_161759.log.json) |
|
||||
| DeepLabV3+ | R-101-D8 | 512x512 | 40000 | - | - | V100 | 78.62 | 79.53 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb4-40k_voc12aug-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_40k_voc12aug/deeplabv3plus_r101-d8_512x512_40k_voc12aug_20200613_205333-faf03387.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_40k_voc12aug/deeplabv3plus_r101-d8_512x512_40k_voc12aug_20200613_205333.log.json) |
|
||||
|
||||
### Pascal Context
|
||||
|
||||
| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device | mIoU | mIoU(ms+flip) | config | download |
|
||||
| ---------- | -------- | --------- | ------: | -------- | -------------- | ------ | ----: | ------------: | ---------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
||||
| DeepLabV3+ | R-101-D8 | 480x480 | 40000 | - | 9.09 | V100 | 47.30 | 48.47 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/deeplabv3plus/deeplabv3plus_r50-d8_4xb4-40k_pascal-context-480x480.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_480x480_40k_pascal_context/deeplabv3plus_r101-d8_480x480_40k_pascal_context_20200911_165459-d3c8a29e.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_480x480_40k_pascal_context/deeplabv3plus_r101-d8_480x480_40k_pascal_context-20200911_165459.log.json) |
|
||||
| DeepLabV3+ | R-101-D8 | 480x480 | 80000 | - | - | V100 | 47.23 | 48.26 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/deeplabv3plus/deeplabv3plus_r50-d8_4xb4-80k_pascal-context-480x480.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_480x480_80k_pascal_context/deeplabv3plus_r101-d8_480x480_80k_pascal_context_20200911_155322-145d3ee8.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_480x480_80k_pascal_context/deeplabv3plus_r101-d8_480x480_80k_pascal_context-20200911_155322.log.json) |
|
||||
|
||||
### Pascal Context 59
|
||||
|
||||
| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device | mIoU | mIoU(ms+flip) | config | download |
|
||||
| ---------- | -------- | --------- | ------: | -------- | -------------- | ------ | ----: | ------------: | -------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
||||
| DeepLabV3+ | R-101-D8 | 480x480 | 40000 | - | - | V100 | 52.86 | 54.54 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb4-40k_pascal-context-59-480x480.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_480x480_40k_pascal_context_59/deeplabv3plus_r101-d8_480x480_40k_pascal_context_59_20210416_111233-ed937f15.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_480x480_40k_pascal_context_59/deeplabv3plus_r101-d8_480x480_40k_pascal_context_59-20210416_111233.log.json) |
|
||||
| DeepLabV3+ | R-101-D8 | 480x480 | 80000 | - | - | V100 | 53.2 | 54.67 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb4-80k_pascal-context-59-480x480.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_480x480_80k_pascal_context_59/deeplabv3plus_r101-d8_480x480_80k_pascal_context_59_20210416_111127-7ca0331d.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_480x480_80k_pascal_context_59/deeplabv3plus_r101-d8_480x480_80k_pascal_context_59-20210416_111127.log.json) |
|
||||
|
||||
### LoveDA
|
||||
|
||||
| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device | mIoU | mIoU(ms+flip) | config | download |
|
||||
| ---------- | -------- | --------- | ------: | -------- | -------------- | ------ | ----: | ------------: | --------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
|
||||
| DeepLabV3+ | R-18-D8 | 512x512 | 80000 | 1.93 | 25.57 | V100 | 50.28 | 50.47 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/deeplabv3plus/deeplabv3plus_r18-d8_4xb4-80k_loveda-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_512x512_80k_loveda/deeplabv3plus_r18-d8_512x512_80k_loveda_20211104_132800-ce0fa0ca.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_512x512_80k_loveda/deeplabv3plus_r18-d8_512x512_80k_loveda_20211104_132800.log.json) |
|
||||
| DeepLabV3+ | R-50-D8 | 512x512 | 80000 | 7.37 | 6.00 | V100 | 50.99 | 50.65 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/deeplabv3plus/deeplabv3plus_r50-d8_4xb4-80k_loveda-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_80k_loveda/deeplabv3plus_r50-d8_512x512_80k_loveda_20211105_080442-f0720392.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_80k_loveda/deeplabv3plus_r50-d8_512x512_80k_loveda_20211105_080442.log.json) |
|
||||
| DeepLabV3+ | R-101-D8 | 512x512 | 80000 | 10.84 | 4.33 | V100 | 51.47 | 51.32 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb4-80k_loveda-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_80k_loveda/deeplabv3plus_r101-d8_512x512_80k_loveda_20211105_110759-4c1f297e.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_80k_loveda/deeplabv3plus_r101-d8_512x512_80k_loveda_20211105_110759.log.json) |
|
||||
|
||||
### Potsdam
|
||||
|
||||
| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device | mIoU | mIoU(ms+flip) | config | download |
|
||||
| ---------- | -------- | --------- | ------: | -------- | -------------- | ------ | ----: | ------------: | ---------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
||||
| DeepLabV3+ | R-18-D8 | 512x512 | 80000 | 1.91 | 81.68 | V100 | 77.09 | 78.44 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/deeplabv3plus/deeplabv3plus_r18-d8_4xb4-80k_potsdam-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_512x512_80k_potsdam/deeplabv3plus_r18-d8_512x512_80k_potsdam_20211219_020601-75fd5bc3.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_512x512_80k_potsdam/deeplabv3plus_r18-d8_512x512_80k_potsdam_20211219_020601.log.json) |
|
||||
| DeepLabV3+ | R-50-D8 | 512x512 | 80000 | 7.36 | 26.44 | V100 | 78.33 | 79.27 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/deeplabv3plus/deeplabv3plus_r50-d8_4xb4-80k_potsdam-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_80k_potsdam/deeplabv3plus_r50-d8_512x512_80k_potsdam_20211219_031508-7e7a2b24.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_80k_potsdam/deeplabv3plus_r50-d8_512x512_80k_potsdam_20211219_031508.log.json) |
|
||||
| DeepLabV3+ | R-101-D8 | 512x512 | 80000 | 10.83 | 17.56 | V100 | 78.7 | 79.47 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb4-80k_potsdam-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_80k_potsdam/deeplabv3plus_r101-d8_512x512_80k_potsdam_20211219_031508-8b112708.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_80k_potsdam/deeplabv3plus_r101-d8_512x512_80k_potsdam_20211219_031508.log.json) |
|
||||
|
||||
### Vaihingen
|
||||
|
||||
| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device | mIoU | mIoU(ms+flip) | config | download |
|
||||
| ---------- | -------- | --------- | ------: | -------- | -------------- | ------ | ----: | ------------: | ------------------------------------------------------------------------------------------------------------------------------------------ | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
||||
| DeepLabV3+ | R-18-D8 | 512x512 | 80000 | 1.91 | 72.79 | V100 | 72.50 | 74.13 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/deeplabv3plus/deeplabv3plus_r18-d8_4xb4-80k_vaihingen-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_4x4_512x512_80k_vaihingen/deeplabv3plus_r18-d8_4x4_512x512_80k_vaihingen_20211231_230805-7626a263.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_4x4_512x512_80k_vaihingen/deeplabv3plus_r18-d8_4x4_512x512_80k_vaihingen_20211231_230805.log.json) |
|
||||
| DeepLabV3+ | R-50-D8 | 512x512 | 80000 | 7.36 | 26.91 | V100 | 73.97 | 75.05 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/deeplabv3plus/deeplabv3plus_r50-d8_4xb4-80k_vaihingen-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_4x4_512x512_80k_vaihingen/deeplabv3plus_r50-d8_4x4_512x512_80k_vaihingen_20211231_230816-5040938d.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_4x4_512x512_80k_vaihingen/deeplabv3plus_r50-d8_4x4_512x512_80k_vaihingen_20211231_230816.log.json) |
|
||||
| DeepLabV3+ | R-101-D8 | 512x512 | 80000 | 10.83 | 18.59 | V100 | 73.06 | 74.14 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb4-80k_vaihingen-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_4x4_512x512_80k_vaihingen/deeplabv3plus_r101-d8_4x4_512x512_80k_vaihingen_20211231_230816-8a095afa.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_4x4_512x512_80k_vaihingen/deeplabv3plus_r101-d8_4x4_512x512_80k_vaihingen_20211231_230816.log.json) |
|
||||
|
||||
### iSAID
|
||||
|
||||
| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device | mIoU | mIoU(ms+flip) | config | download |
|
||||
| ---------- | -------- | --------- | ------: | -------- | -------------- | ------ | ----: | ------------: | ------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
||||
| DeepLabV3+ | R-18-D8 | 896x896 | 80000 | 6.19 | 24.81 | V100 | 61.35 | 62.61 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/deeplabv3plus/deeplabv3plus_r18-d8_4xb4-80k_isaid-896x896.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_4x4_896x896_80k_isaid/deeplabv3plus_r18-d8_4x4_896x896_80k_isaid_20220110_180526-7059991d.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_4x4_896x896_80k_isaid/deeplabv3plus_r18-d8_4x4_896x896_80k_isaid_20220110_180526.log.json) |
|
||||
| DeepLabV3+ | R-50-D8 | 896x896 | 80000 | 21.45 | 8.42 | V100 | 67.06 | 68.02 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/deeplabv3plus/deeplabv3plus_r50-d8_4xb4-80k_isaid-896x896.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_4x4_896x896_80k_isaid/deeplabv3plus_r50-d8_4x4_896x896_80k_isaid_20220110_180526-598be439.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_4x4_896x896_80k_isaid/deeplabv3plus_r50-d8_4x4_896x896_80k_isaid_20220110_180526.log.json) |
|
||||
|
||||
### Mapillary Vistas v1.2
|
||||
|
||||
| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device | mIoU | mIoU(ms+flip) | config | download |
|
||||
| ---------- | -------- | --------- | ------: | -------- | -------------- | ------ | ----: | ------------: | ------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
||||
| DeepLabV3+ | R-50-D8 | 1280x1280 | 300000 | 24.04 | 17.92 | A100 | 47.35 | - | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/deeplabv3plus/deeplabv3plus_r50-d8_4xb2-300k_mapillay_v1_65-1280x1280.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_4xb2-300k_mapillay_v1_65-1280x1280/deeplabv3plus_r50-d8_4xb2-300k_mapillay_v1_65-1280x1280_20230301_110504-655f8e43.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_4xb2-300k_mapillay_v1_65-1280x1280/deeplabv3plus_r50-d8_4xb2-300k_mapillay_v1_65-1280x1280_20230301_110504.json) |
|
||||
|
||||
Note:
|
||||
|
||||
- `D-8`/`D-16` here corresponding to the output stride 8/16 setting for DeepLab series.
|
||||
- `MG-124` stands for multi-grid dilation in the last stage of ResNet.
|
||||
- `FP16` means Mixed Precision (FP16) is adopted in training.
|
||||
- `896x896` is the Crop Size of iSAID dataset, which is followed by the implementation of [PointFlow: Flowing Semantics Through Points for Aerial Image Segmentation](https://arxiv.org/pdf/2103.06564.pdf)
|
||||
|
||||
## Citation
|
||||
|
||||
```bibtex
|
||||
@inproceedings{deeplabv3plus2018,
|
||||
title={Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation},
|
||||
author={Liang-Chieh Chen and Yukun Zhu and George Papandreou and Florian Schroff and Hartwig Adam},
|
||||
booktitle={ECCV},
|
||||
year={2018}
|
||||
}
|
||||
```
|
||||
@@ -0,0 +1,11 @@
|
||||
_base_ = './deeplabv3plus_r50-d8_4xb2-40k_cityscapes-512x1024.py'
|
||||
model = dict(
|
||||
pretrained='open-mmlab://resnet101_v1c',
|
||||
backbone=dict(
|
||||
depth=101,
|
||||
dilations=(1, 1, 1, 2),
|
||||
strides=(1, 2, 2, 1),
|
||||
multi_grid=(1, 2, 4)),
|
||||
decode_head=dict(
|
||||
dilations=(1, 6, 12, 18),
|
||||
sampler=dict(type='OHEMPixelSampler', min_kept=100000)))
|
||||
@@ -0,0 +1,11 @@
|
||||
_base_ = './deeplabv3plus_r50-d8_4xb2-80k_cityscapes-512x1024.py'
|
||||
model = dict(
|
||||
pretrained='open-mmlab://resnet101_v1c',
|
||||
backbone=dict(
|
||||
depth=101,
|
||||
dilations=(1, 1, 1, 2),
|
||||
strides=(1, 2, 2, 1),
|
||||
multi_grid=(1, 2, 4)),
|
||||
decode_head=dict(
|
||||
dilations=(1, 6, 12, 18),
|
||||
sampler=dict(type='OHEMPixelSampler', min_kept=100000)))
|
||||
@@ -0,0 +1,2 @@
|
||||
_base_ = './deeplabv3plus_r50-d8_4xb2-40k_cityscapes-512x1024.py'
|
||||
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
|
||||
@@ -0,0 +1,2 @@
|
||||
_base_ = './deeplabv3plus_r50-d8_4xb2-40k_cityscapes-769x769.py'
|
||||
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
|
||||
@@ -0,0 +1,2 @@
|
||||
_base_ = './deeplabv3plus_r50-d8_4xb2-80k_cityscapes-512x1024.py'
|
||||
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
|
||||
@@ -0,0 +1,2 @@
|
||||
_base_ = './deeplabv3plus_r50-d8_4xb2-80k_cityscapes-769x769.py'
|
||||
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
|
||||
@@ -0,0 +1,7 @@
|
||||
_base_ = './deeplabv3plus_r101-d8_4xb2-80k_cityscapes-512x1024.py'
|
||||
optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0005)
|
||||
optim_wrapper = dict(
|
||||
_delete_=True,
|
||||
type='AmpOptimWrapper',
|
||||
optimizer=optimizer,
|
||||
loss_scale=512.)
|
||||
@@ -0,0 +1,2 @@
|
||||
_base_ = './deeplabv3plus_r50-d8_4xb4-160k_ade20k-512x512.py'
|
||||
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
|
||||
@@ -0,0 +1,2 @@
|
||||
_base_ = './deeplabv3plus_r50-d8_4xb4-20k_voc12aug-512x512.py'
|
||||
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
|
||||
@@ -0,0 +1,2 @@
|
||||
_base_ = './deeplabv3plus_r50-d8_4xb4-40k_pascal-context-480x480.py'
|
||||
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
|
||||
@@ -0,0 +1,2 @@
|
||||
_base_ = './deeplabv3plus_r50-d8_4xb4-40k_pascal-context-59-480x480.py'
|
||||
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
|
||||
@@ -0,0 +1,2 @@
|
||||
_base_ = './deeplabv3plus_r50-d8_4xb4-40k_voc12aug-512x512.py'
|
||||
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
|
||||
@@ -0,0 +1,2 @@
|
||||
_base_ = './deeplabv3plus_r50-d8_4xb4-80k_ade20k-512x512.py'
|
||||
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
|
||||
@@ -0,0 +1,2 @@
|
||||
_base_ = './deeplabv3plus_r50-d8_4xb4-80k_loveda-512x512.py'
|
||||
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
|
||||
@@ -0,0 +1,2 @@
|
||||
_base_ = './deeplabv3plus_r50-d8_4xb4-80k_pascal-context-480x480.py'
|
||||
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
|
||||
@@ -0,0 +1,2 @@
|
||||
_base_ = './deeplabv3plus_r50-d8_4xb4-80k_pascal-context-59-480x480.py'
|
||||
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
|
||||
@@ -0,0 +1,2 @@
|
||||
_base_ = './deeplabv3plus_r50-d8_4xb4-80k_potsdam-512x512.py'
|
||||
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
|
||||
@@ -0,0 +1,2 @@
|
||||
_base_ = './deeplabv3plus_r50-d8_4xb4-80k_vaihingen-512x512.py'
|
||||
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
|
||||
@@ -0,0 +1,4 @@
|
||||
_base_ = './deeplabv3plus_r50-d8_4xb2-80k_cityscapes-512x1024.py'
|
||||
model = dict(
|
||||
pretrained='torchvision://resnet101',
|
||||
backbone=dict(type='ResNet', depth=101))
|
||||
@@ -0,0 +1,4 @@
|
||||
_base_ = './deeplabv3plus_r50-d8_4xb2-80k_cityscapes-769x769.py'
|
||||
model = dict(
|
||||
pretrained='torchvision://resnet101',
|
||||
backbone=dict(type='ResNet', depth=101))
|
||||
@@ -0,0 +1,11 @@
|
||||
_base_ = './deeplabv3plus_r50-d8_4xb2-80k_cityscapes-512x1024.py'
|
||||
model = dict(
|
||||
pretrained='open-mmlab://resnet18_v1c',
|
||||
backbone=dict(depth=18),
|
||||
decode_head=dict(
|
||||
c1_in_channels=64,
|
||||
c1_channels=12,
|
||||
in_channels=512,
|
||||
channels=128,
|
||||
),
|
||||
auxiliary_head=dict(in_channels=256, channels=64))
|
||||
@@ -0,0 +1,11 @@
|
||||
_base_ = './deeplabv3plus_r50-d8_4xb2-80k_cityscapes-769x769.py'
|
||||
model = dict(
|
||||
pretrained='open-mmlab://resnet18_v1c',
|
||||
backbone=dict(depth=18),
|
||||
decode_head=dict(
|
||||
c1_in_channels=64,
|
||||
c1_channels=12,
|
||||
in_channels=512,
|
||||
channels=128,
|
||||
),
|
||||
auxiliary_head=dict(in_channels=256, channels=64))
|
||||
@@ -0,0 +1,11 @@
|
||||
_base_ = './deeplabv3plus_r50-d8_4xb4-80k_isaid-896x896.py'
|
||||
model = dict(
|
||||
pretrained='open-mmlab://resnet18_v1c',
|
||||
backbone=dict(depth=18),
|
||||
decode_head=dict(
|
||||
c1_in_channels=64,
|
||||
c1_channels=12,
|
||||
in_channels=512,
|
||||
channels=128,
|
||||
),
|
||||
auxiliary_head=dict(in_channels=256, channels=64))
|
||||
@@ -0,0 +1,11 @@
|
||||
_base_ = './deeplabv3plus_r50-d8_4xb4-80k_loveda-512x512.py'
|
||||
model = dict(
|
||||
pretrained='open-mmlab://resnet18_v1c',
|
||||
backbone=dict(depth=18),
|
||||
decode_head=dict(
|
||||
c1_in_channels=64,
|
||||
c1_channels=12,
|
||||
in_channels=512,
|
||||
channels=128,
|
||||
),
|
||||
auxiliary_head=dict(in_channels=256, channels=64))
|
||||
@@ -0,0 +1,11 @@
|
||||
_base_ = './deeplabv3plus_r50-d8_4xb4-80k_potsdam-512x512.py'
|
||||
model = dict(
|
||||
pretrained='open-mmlab://resnet18_v1c',
|
||||
backbone=dict(depth=18),
|
||||
decode_head=dict(
|
||||
c1_in_channels=64,
|
||||
c1_channels=12,
|
||||
in_channels=512,
|
||||
channels=128,
|
||||
),
|
||||
auxiliary_head=dict(in_channels=256, channels=64))
|
||||
@@ -0,0 +1,11 @@
|
||||
_base_ = './deeplabv3plus_r50-d8_4xb4-80k_vaihingen-512x512.py'
|
||||
model = dict(
|
||||
pretrained='open-mmlab://resnet18_v1c',
|
||||
backbone=dict(depth=18),
|
||||
decode_head=dict(
|
||||
c1_in_channels=64,
|
||||
c1_channels=12,
|
||||
in_channels=512,
|
||||
channels=128,
|
||||
),
|
||||
auxiliary_head=dict(in_channels=256, channels=64))
|
||||
@@ -0,0 +1,11 @@
|
||||
_base_ = './deeplabv3plus_r50-d8_4xb2-80k_cityscapes-512x1024.py'
|
||||
model = dict(
|
||||
pretrained='torchvision://resnet18',
|
||||
backbone=dict(type='ResNet', depth=18),
|
||||
decode_head=dict(
|
||||
c1_in_channels=64,
|
||||
c1_channels=12,
|
||||
in_channels=512,
|
||||
channels=128,
|
||||
),
|
||||
auxiliary_head=dict(in_channels=256, channels=64))
|
||||
@@ -0,0 +1,11 @@
|
||||
_base_ = './deeplabv3plus_r50-d8_4xb2-40k_cityscapes-769x769.py'
|
||||
model = dict(
|
||||
pretrained='torchvision://resnet18',
|
||||
backbone=dict(type='ResNet', depth=18),
|
||||
decode_head=dict(
|
||||
c1_in_channels=64,
|
||||
c1_channels=12,
|
||||
in_channels=512,
|
||||
channels=128,
|
||||
),
|
||||
auxiliary_head=dict(in_channels=256, channels=64))
|
||||
@@ -0,0 +1,58 @@
|
||||
_base_ = [
|
||||
'../_base_/models/deeplabv3plus_r50-d8.py',
|
||||
'../_base_/datasets/mapillary_v1_65.py',
|
||||
'../_base_/default_runtime.py',
|
||||
]
|
||||
|
||||
crop_size = (1280, 1280)
|
||||
data_preprocessor = dict(size=crop_size)
|
||||
model = dict(
|
||||
data_preprocessor=data_preprocessor,
|
||||
pretrained='open-mmlab://resnet50_v1c',
|
||||
backbone=dict(depth=50),
|
||||
decode_head=dict(num_classes=65),
|
||||
auxiliary_head=dict(num_classes=65))
|
||||
|
||||
iters = 300000
|
||||
# optimizer
|
||||
optimizer = dict(
|
||||
type='AdamW', lr=0.0001, betas=(0.9, 0.999), weight_decay=0.0001)
|
||||
# optimizer
|
||||
optim_wrapper = dict(
|
||||
type='OptimWrapper',
|
||||
optimizer=optimizer,
|
||||
clip_grad=dict(max_norm=0.01, norm_type=2),
|
||||
paramwise_cfg=dict(
|
||||
custom_keys={'backbone': dict(lr_mult=0.1, decay_mult=1.0)}))
|
||||
param_scheduler = [
|
||||
dict(
|
||||
type='PolyLR',
|
||||
eta_min=0,
|
||||
power=0.9,
|
||||
begin=0,
|
||||
end=iters,
|
||||
by_epoch=False)
|
||||
]
|
||||
|
||||
# training schedule for 300k
|
||||
train_cfg = dict(
|
||||
type='IterBasedTrainLoop', max_iters=iters, val_interval=iters // 10)
|
||||
val_cfg = dict(type='ValLoop')
|
||||
test_cfg = dict(type='TestLoop')
|
||||
|
||||
default_hooks = dict(
|
||||
timer=dict(type='IterTimerHook'),
|
||||
logger=dict(type='LoggerHook', interval=50, log_metric_by_epoch=False),
|
||||
param_scheduler=dict(type='ParamSchedulerHook'),
|
||||
checkpoint=dict(
|
||||
type='CheckpointHook', by_epoch=False, interval=iters // 10),
|
||||
sampler_seed=dict(type='DistSamplerSeedHook'),
|
||||
visualization=dict(type='SegVisualizationHook'))
|
||||
|
||||
train_dataloader = dict(batch_size=2)
|
||||
|
||||
# Default setting for scaling LR automatically
|
||||
# - `enable` means enable scaling LR automatically
|
||||
# or not by default.
|
||||
# - `base_batch_size` = (4 GPUs) x (2 samples per GPU).
|
||||
auto_scale_lr = dict(enable=False, base_batch_size=8)
|
||||
@@ -0,0 +1,8 @@
|
||||
_base_ = [
|
||||
'../_base_/models/deeplabv3plus_r50-d8.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/deeplabv3plus_r50-d8.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 +1,8 @@
|
||||
_base_ = [
|
||||
'../_base_/models/deeplabv3plus_r50-d8.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/deeplabv3plus_r50-d8.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/deeplabv3plus_r50-d8.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/deeplabv3plus_r50-d8.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 +1,14 @@
|
||||
_base_ = [
|
||||
'../_base_/models/deeplabv3plus_r50-d8.py',
|
||||
'../_base_/datasets/pascal_context.py', '../_base_/default_runtime.py',
|
||||
'../_base_/schedules/schedule_40k.py'
|
||||
]
|
||||
crop_size = (480, 480)
|
||||
data_preprocessor = dict(size=crop_size)
|
||||
model = dict(
|
||||
data_preprocessor=data_preprocessor,
|
||||
decode_head=dict(num_classes=60),
|
||||
auxiliary_head=dict(num_classes=60),
|
||||
test_cfg=dict(mode='slide', crop_size=(480, 480), stride=(320, 320)))
|
||||
optimizer = dict(type='SGD', lr=0.004, momentum=0.9, weight_decay=0.0001)
|
||||
optim_wrapper = dict(type='OptimWrapper', optimizer=optimizer)
|
||||
@@ -0,0 +1,14 @@
|
||||
_base_ = [
|
||||
'../_base_/models/deeplabv3plus_r50-d8.py',
|
||||
'../_base_/datasets/pascal_context_59.py', '../_base_/default_runtime.py',
|
||||
'../_base_/schedules/schedule_40k.py'
|
||||
]
|
||||
crop_size = (480, 480)
|
||||
data_preprocessor = dict(size=crop_size)
|
||||
model = dict(
|
||||
data_preprocessor=data_preprocessor,
|
||||
decode_head=dict(num_classes=59),
|
||||
auxiliary_head=dict(num_classes=59),
|
||||
test_cfg=dict(mode='slide', crop_size=(480, 480), stride=(320, 320)))
|
||||
optimizer = dict(type='SGD', lr=0.004, momentum=0.9, weight_decay=0.0001)
|
||||
optim_wrapper = dict(type='OptimWrapper', optimizer=optimizer)
|
||||
@@ -0,0 +1,11 @@
|
||||
_base_ = [
|
||||
'../_base_/models/deeplabv3plus_r50-d8.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/deeplabv3plus_r50-d8.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))
|
||||
@@ -0,0 +1,10 @@
|
||||
_base_ = [
|
||||
'../_base_/models/deeplabv3plus_r50-d8.py', '../_base_/datasets/isaid.py',
|
||||
'../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
|
||||
]
|
||||
crop_size = (896, 896)
|
||||
data_preprocessor = dict(size=crop_size)
|
||||
model = dict(
|
||||
data_preprocessor=data_preprocessor,
|
||||
decode_head=dict(num_classes=16),
|
||||
auxiliary_head=dict(num_classes=16))
|
||||
@@ -0,0 +1,10 @@
|
||||
_base_ = [
|
||||
'../_base_/models/deeplabv3plus_r50-d8.py', '../_base_/datasets/loveda.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=7),
|
||||
auxiliary_head=dict(num_classes=7))
|
||||
@@ -0,0 +1,14 @@
|
||||
_base_ = [
|
||||
'../_base_/models/deeplabv3plus_r50-d8.py',
|
||||
'../_base_/datasets/pascal_context.py', '../_base_/default_runtime.py',
|
||||
'../_base_/schedules/schedule_80k.py'
|
||||
]
|
||||
crop_size = (480, 480)
|
||||
data_preprocessor = dict(size=crop_size)
|
||||
model = dict(
|
||||
data_preprocessor=data_preprocessor,
|
||||
decode_head=dict(num_classes=60),
|
||||
auxiliary_head=dict(num_classes=60),
|
||||
test_cfg=dict(mode='slide', crop_size=(480, 480), stride=(320, 320)))
|
||||
optimizer = dict(type='SGD', lr=0.004, momentum=0.9, weight_decay=0.0001)
|
||||
optim_wrapper = dict(type='OptimWrapper', optimizer=optimizer)
|
||||
@@ -0,0 +1,14 @@
|
||||
_base_ = [
|
||||
'../_base_/models/deeplabv3plus_r50-d8.py',
|
||||
'../_base_/datasets/pascal_context_59.py', '../_base_/default_runtime.py',
|
||||
'../_base_/schedules/schedule_80k.py'
|
||||
]
|
||||
crop_size = (480, 480)
|
||||
data_preprocessor = dict(size=crop_size)
|
||||
model = dict(
|
||||
data_preprocessor=data_preprocessor,
|
||||
decode_head=dict(num_classes=59),
|
||||
auxiliary_head=dict(num_classes=59),
|
||||
test_cfg=dict(mode='slide', crop_size=(480, 480), stride=(320, 320)))
|
||||
optimizer = dict(type='SGD', lr=0.004, momentum=0.9, weight_decay=0.0001)
|
||||
optim_wrapper = dict(type='OptimWrapper', optimizer=optimizer)
|
||||
@@ -0,0 +1,11 @@
|
||||
_base_ = [
|
||||
'../_base_/models/deeplabv3plus_r50-d8.py',
|
||||
'../_base_/datasets/potsdam.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=6),
|
||||
auxiliary_head=dict(num_classes=6))
|
||||
@@ -0,0 +1,11 @@
|
||||
_base_ = [
|
||||
'../_base_/models/deeplabv3plus_r50-d8.py',
|
||||
'../_base_/datasets/vaihingen.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=6),
|
||||
auxiliary_head=dict(num_classes=6))
|
||||
@@ -0,0 +1,2 @@
|
||||
_base_ = './deeplabv3plus_r50-d8_4xb2-80k_cityscapes-512x1024.py'
|
||||
model = dict(pretrained='torchvision://resnet50', backbone=dict(type='ResNet'))
|
||||
@@ -0,0 +1,2 @@
|
||||
_base_ = './deeplabv3plus_r50-d8_4xb2-80k_cityscapes-769x769.py'
|
||||
model = dict(pretrained='torchvision://resnet50', backbone=dict(type='ResNet'))
|
||||
1041
Seg_All_In_One_MMSeg/configs/deeplabv3plus/metafile.yaml
Normal file
1041
Seg_All_In_One_MMSeg/configs/deeplabv3plus/metafile.yaml
Normal file
File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,116 @@
|
||||
_base_ = [
|
||||
'../_base_/models/deeplabv3plus_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/resnet101_v1c.pth',
|
||||
backbone=dict(
|
||||
depth=101,
|
||||
type='ResNetV1c',
|
||||
),
|
||||
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(
|
||||
c1_in_channels=256,
|
||||
c1_channels=48,
|
||||
in_channels=2048,
|
||||
channels=512,
|
||||
num_classes=36,
|
||||
loss_decode=dict(
|
||||
type='DiceLoss',
|
||||
use_sigmoid=False,
|
||||
loss_weight=1.0,
|
||||
),
|
||||
align_corners=False,
|
||||
norm_cfg=dict(
|
||||
type='BN',
|
||||
),
|
||||
),
|
||||
auxiliary_head=dict(
|
||||
in_channels=1024,
|
||||
channels=256,
|
||||
num_classes=36,
|
||||
loss_decode=dict(
|
||||
type='DiceLoss',
|
||||
use_sigmoid=False,
|
||||
loss_weight=1.0,
|
||||
),
|
||||
align_corners=False,
|
||||
norm_cfg=dict(
|
||||
type='BN',
|
||||
),
|
||||
),
|
||||
)
|
||||
|
||||
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,
|
||||
),
|
||||
]
|
||||
|
||||
@@ -0,0 +1,118 @@
|
||||
_base_ = [
|
||||
'../_base_/models/deeplabv3plus_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/torchvision_012/resnet101.pth',
|
||||
backbone=dict(
|
||||
depth=101,
|
||||
type='ResNet',
|
||||
),
|
||||
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(
|
||||
c1_in_channels=256,
|
||||
c1_channels=48,
|
||||
in_channels=2048,
|
||||
channels=512,
|
||||
num_classes=36,
|
||||
loss_decode=dict(
|
||||
type='DiceLoss',
|
||||
use_sigmoid=False,
|
||||
loss_weight=1.0,
|
||||
),
|
||||
align_corners=True,
|
||||
norm_cfg=dict(
|
||||
type='BN',
|
||||
),
|
||||
),
|
||||
auxiliary_head=dict(
|
||||
in_channels=1024,
|
||||
channels=256,
|
||||
num_classes=36,
|
||||
loss_decode=dict(
|
||||
type='DiceLoss',
|
||||
use_sigmoid=False,
|
||||
loss_weight=1.0,
|
||||
),
|
||||
align_corners=True,
|
||||
norm_cfg=dict(
|
||||
type='BN',
|
||||
),
|
||||
),
|
||||
)
|
||||
|
||||
test_cfg = dict(
|
||||
mode='slide',
|
||||
crop_size=(512, 1024),
|
||||
stride=(341, 682),
|
||||
)
|
||||
|
||||
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,
|
||||
),
|
||||
]
|
||||
|
||||
@@ -0,0 +1,125 @@
|
||||
_base_ = [
|
||||
'../_base_/models/deeplabv3plus_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, 512)
|
||||
|
||||
data_preprocessor = dict(
|
||||
size=(512, 512),
|
||||
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/resnet101_v1c.pth',
|
||||
backbone=dict(
|
||||
depth=101,
|
||||
type='ResNetV1c',
|
||||
dilations=(1, 1, 1, 2),
|
||||
strides=(1, 2, 2, 1),
|
||||
multi_grid=(1, 2, 4),
|
||||
),
|
||||
data_preprocessor=dict(
|
||||
size=(512, 512),
|
||||
mean=[
|
||||
94.94709810464303,
|
||||
61.72942233949928,
|
||||
75.93763705236906,
|
||||
],
|
||||
std=[
|
||||
44.005506081132594,
|
||||
42.69595666984776,
|
||||
44.99354156225523,
|
||||
],
|
||||
bgr_to_rgb=False,
|
||||
),
|
||||
decode_head=dict(
|
||||
sampler=dict(
|
||||
type='OHEMPixelSampler',
|
||||
thresh=0.7,
|
||||
min_kept=10000,
|
||||
),
|
||||
dilations=(1, 6, 12, 18),
|
||||
c1_in_channels=256,
|
||||
c1_channels=48,
|
||||
in_channels=2048,
|
||||
channels=512,
|
||||
num_classes=36,
|
||||
loss_decode=dict(
|
||||
type='DiceLoss',
|
||||
use_sigmoid=False,
|
||||
loss_weight=1.0,
|
||||
),
|
||||
align_corners=False,
|
||||
norm_cfg=dict(
|
||||
type='BN',
|
||||
),
|
||||
),
|
||||
auxiliary_head=dict(
|
||||
in_channels=1024,
|
||||
channels=256,
|
||||
num_classes=36,
|
||||
loss_decode=dict(
|
||||
type='DiceLoss',
|
||||
use_sigmoid=False,
|
||||
loss_weight=1.0,
|
||||
),
|
||||
align_corners=False,
|
||||
norm_cfg=dict(
|
||||
type='BN',
|
||||
),
|
||||
),
|
||||
)
|
||||
|
||||
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=False,
|
||||
begin=0,
|
||||
end=1500,
|
||||
),
|
||||
dict(
|
||||
type='PolyLR',
|
||||
power=0.9,
|
||||
begin=1500,
|
||||
end=40000,
|
||||
eta_min=1e-05,
|
||||
by_epoch=False,
|
||||
),
|
||||
]
|
||||
|
||||
@@ -0,0 +1,127 @@
|
||||
_base_ = [
|
||||
'../_base_/models/deeplabv3plus_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, 512)
|
||||
|
||||
data_preprocessor = dict(
|
||||
size=(512, 512),
|
||||
mean=[
|
||||
94.94709810464303,
|
||||
61.72942233949928,
|
||||
75.93763705236906,
|
||||
],
|
||||
std=[
|
||||
44.005506081132594,
|
||||
42.69595666984776,
|
||||
44.99354156225523,
|
||||
],
|
||||
bgr_to_rgb=False,
|
||||
)
|
||||
|
||||
model = dict(
|
||||
pretrained='./My_Local_Model/torchvision_012/resnet18.pth',
|
||||
backbone=dict(
|
||||
depth=18,
|
||||
type='ResNet',
|
||||
dilations=(1, 1, 1, 2),
|
||||
strides=(1, 2, 2, 1),
|
||||
multi_grid=(1, 2, 4),
|
||||
),
|
||||
data_preprocessor=dict(
|
||||
size=(512, 512),
|
||||
mean=[
|
||||
94.94709810464303,
|
||||
61.72942233949928,
|
||||
75.93763705236906,
|
||||
],
|
||||
std=[
|
||||
44.005506081132594,
|
||||
42.69595666984776,
|
||||
44.99354156225523,
|
||||
],
|
||||
bgr_to_rgb=False,
|
||||
),
|
||||
decode_head=dict(
|
||||
sampler=dict(
|
||||
type='OHEMPixelSampler',
|
||||
thresh=0.7,
|
||||
min_kept=10000,
|
||||
),
|
||||
dilations=(1, 6, 12, 18),
|
||||
c1_in_channels=64,
|
||||
c1_channels=12,
|
||||
in_channels=512,
|
||||
channels=128,
|
||||
num_classes=36,
|
||||
loss_decode=dict(
|
||||
type='DiceLoss',
|
||||
use_sigmoid=False,
|
||||
loss_weight=1.0,
|
||||
),
|
||||
align_corners=True,
|
||||
norm_cfg=dict(
|
||||
type='BN',
|
||||
),
|
||||
),
|
||||
auxiliary_head=dict(
|
||||
in_channels=256,
|
||||
channels=64,
|
||||
num_classes=36,
|
||||
loss_decode=dict(
|
||||
type='DiceLoss',
|
||||
use_sigmoid=False,
|
||||
loss_weight=1.0,
|
||||
),
|
||||
align_corners=True,
|
||||
norm_cfg=dict(
|
||||
type='BN',
|
||||
),
|
||||
),
|
||||
)
|
||||
|
||||
test_cfg = dict(
|
||||
mode='slide',
|
||||
crop_size=(512, 512),
|
||||
stride=(341, 341),
|
||||
)
|
||||
|
||||
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,
|
||||
),
|
||||
]
|
||||
|
||||
@@ -0,0 +1,20 @@
|
||||
_base_ = ['../_base_/models/deeplabv3plus_r50-d8.py', '../_base_/datasets/publicdataset_autolaparo.py', '../_base_/default_runtime.py', '../_base_/schedules/schedule_300e_val1_check10.py']
|
||||
|
||||
norm_cfg = {'type': 'BN'}
|
||||
|
||||
crop_size = (512, 512)
|
||||
|
||||
data_preprocessor = {'size': (512, 512), 'mean': [123.62464353460942, 85.34836259209033, 82.31539425671558], 'std': [47.172211618459315, 47.08256715323592, 48.135121265163605], 'bgr_to_rgb': False}
|
||||
|
||||
model = {'pretrained': './My_Local_Model/open_mmlab/resnet18_v1c.pth', 'backbone': {'depth': 18, 'type': 'ResNetV1c'}, 'data_preprocessor': {'size': (512, 512), 'mean': [123.62464353460942, 85.34836259209033, 82.31539425671558], 'std': [47.172211618459315, 47.08256715323592, 48.135121265163605], 'bgr_to_rgb': False}, 'decode_head': {'c1_in_channels': 64, 'c1_channels': 12, 'in_channels': 512, 'channels': 128, 'num_classes': 10, 'loss_decode': {'type': 'DiceLoss', 'use_sigmoid': False, 'loss_weight': 1.0}, 'align_corners': False, 'norm_cfg': {'type': 'BN'}}, 'auxiliary_head': {'in_channels': 256, 'channels': 64, 'num_classes': 10, 'loss_decode': {'type': 'DiceLoss', 'use_sigmoid': False, 'loss_weight': 1.0}, 'align_corners': False, 'norm_cfg': {'type': 'BN'}}}
|
||||
|
||||
test_cfg = {'crop_size': (512, 512)}
|
||||
|
||||
optim_wrapper = {'type': 'OptimWrapper', '_delete_': True, 'optimizer': {'type': 'AdamW', 'lr': 0.0001, 'weight_decay': 0.0005}, 'clip_grad': {'max_norm': 1, 'norm_type': 2}}
|
||||
|
||||
param_scheduler = [{'type': 'LinearLR', 'start_factor': 1e-06, 'by_epoch': True, 'begin': 0, 'end': 10}, {'type': 'PolyLR', 'power': 0.9, 'begin': 10, 'end': 300, 'eta_min': 1e-05, 'by_epoch': True}]
|
||||
|
||||
vis_backends = [{'type': 'LocalVisBackend'}, {'type': 'TensorboardVisBackend'}, {'type': 'WandbVisBackend', 'init_kwargs': {'project': 'Seg_MMSeg_Test', 'name': 'my_deeplabv3plus_r50_r18_Pre_g1-300e_val1_check10_publicdataset_autolaparo-512x512-no_testslide'}}]
|
||||
|
||||
visualizer = {'name': 'visualizer', 'type': 'SegLocalVisualizer', 'vis_backends': [{'type': 'LocalVisBackend'}, {'type': 'TensorboardVisBackend'}, {'type': 'WandbVisBackend', 'init_kwargs': {'project': 'Seg_MMSeg_Test', 'name': 'my_deeplabv3plus_r50_r18_Pre_g1-300e_val1_check10_publicdataset_autolaparo-512x512-no_testslide'}}]}
|
||||
|
||||
@@ -0,0 +1,20 @@
|
||||
_base_ = ['../_base_/models/deeplabv3plus_r50-d8.py', '../_base_/datasets/publicdataset_cholecseg8k.py', '../_base_/default_runtime.py', '../_base_/schedules/schedule_300e_val1_check10.py']
|
||||
|
||||
norm_cfg = {'type': 'BN'}
|
||||
|
||||
crop_size = (512, 512)
|
||||
|
||||
data_preprocessor = {'size': (512, 512), 'mean': [85.65740418979115, 53.99282220050495, 46.074045888534535], 'std': [72.24589167201978, 56.76979155397199, 49.056637115061775], 'bgr_to_rgb': False}
|
||||
|
||||
model = {'pretrained': './My_Local_Model/open_mmlab/resnet18_v1c.pth', 'backbone': {'depth': 18, 'type': 'ResNetV1c'}, 'data_preprocessor': {'size': (512, 512), 'mean': [85.65740418979115, 53.99282220050495, 46.074045888534535], 'std': [72.24589167201978, 56.76979155397199, 49.056637115061775], 'bgr_to_rgb': False}, 'decode_head': {'c1_in_channels': 64, 'c1_channels': 12, 'in_channels': 512, 'channels': 128, 'num_classes': 13, 'loss_decode': {'type': 'DiceLoss', 'use_sigmoid': False, 'loss_weight': 1.0}, 'align_corners': False, 'norm_cfg': {'type': 'BN'}}, 'auxiliary_head': {'in_channels': 256, 'channels': 64, 'num_classes': 13, 'loss_decode': {'type': 'DiceLoss', 'use_sigmoid': False, 'loss_weight': 1.0}, 'align_corners': False, 'norm_cfg': {'type': 'BN'}}}
|
||||
|
||||
test_cfg = {'crop_size': (512, 512)}
|
||||
|
||||
optim_wrapper = {'type': 'OptimWrapper', '_delete_': True, 'optimizer': {'type': 'AdamW', 'lr': 0.0001, 'weight_decay': 0.0005}, 'clip_grad': {'max_norm': 1, 'norm_type': 2}}
|
||||
|
||||
param_scheduler = [{'type': 'LinearLR', 'start_factor': 1e-06, 'by_epoch': True, 'begin': 0, 'end': 10}, {'type': 'PolyLR', 'power': 0.9, 'begin': 10, 'end': 300, 'eta_min': 1e-05, 'by_epoch': True}]
|
||||
|
||||
vis_backends = [{'type': 'LocalVisBackend'}, {'type': 'TensorboardVisBackend'}, {'type': 'WandbVisBackend', 'init_kwargs': {'project': 'Seg_MMSeg_Test', 'name': 'my_deeplabv3plus_r50_r18_Pre_g1-300e_val1_check10_publicdataset_cholecseg8k-512x512-no_testslide'}}]
|
||||
|
||||
visualizer = {'name': 'visualizer', 'type': 'SegLocalVisualizer', 'vis_backends': [{'type': 'LocalVisBackend'}, {'type': 'TensorboardVisBackend'}, {'type': 'WandbVisBackend', 'init_kwargs': {'project': 'Seg_MMSeg_Test', 'name': 'my_deeplabv3plus_r50_r18_Pre_g1-300e_val1_check10_publicdataset_cholecseg8k-512x512-no_testslide'}}]}
|
||||
|
||||
@@ -0,0 +1,20 @@
|
||||
_base_ = ['../_base_/models/deeplabv3plus_r50-d8.py', '../_base_/datasets/publicdataset_dresden.py', '../_base_/default_runtime.py', '../_base_/schedules/schedule_300e_val1_check10.py']
|
||||
|
||||
norm_cfg = {'type': 'BN'}
|
||||
|
||||
crop_size = (512, 512)
|
||||
|
||||
data_preprocessor = {'size': (512, 512), 'mean': [103.172638338208, 61.44762740851152, 51.407770213021976], 'std': [75.77031253622098, 54.63616729031377, 49.45572239497569], 'bgr_to_rgb': False}
|
||||
|
||||
model = {'pretrained': './My_Local_Model/open_mmlab/resnet18_v1c.pth', 'backbone': {'depth': 18, 'type': 'ResNetV1c'}, 'data_preprocessor': {'size': (512, 512), 'mean': [103.172638338208, 61.44762740851152, 51.407770213021976], 'std': [75.77031253622098, 54.63616729031377, 49.45572239497569], 'bgr_to_rgb': False}, 'decode_head': {'c1_in_channels': 64, 'c1_channels': 12, 'in_channels': 512, 'channels': 128, 'num_classes': 11, 'loss_decode': {'type': 'DiceLoss', 'use_sigmoid': False, 'loss_weight': 1.0}, 'align_corners': False, 'norm_cfg': {'type': 'BN'}}, 'auxiliary_head': {'in_channels': 256, 'channels': 64, 'num_classes': 11, 'loss_decode': {'type': 'DiceLoss', 'use_sigmoid': False, 'loss_weight': 1.0}, 'align_corners': False, 'norm_cfg': {'type': 'BN'}}}
|
||||
|
||||
test_cfg = {'crop_size': (512, 512)}
|
||||
|
||||
optim_wrapper = {'type': 'OptimWrapper', '_delete_': True, 'optimizer': {'type': 'AdamW', 'lr': 0.0001, 'weight_decay': 0.0005}, 'clip_grad': {'max_norm': 1, 'norm_type': 2}}
|
||||
|
||||
param_scheduler = [{'type': 'LinearLR', 'start_factor': 1e-06, 'by_epoch': True, 'begin': 0, 'end': 10}, {'type': 'PolyLR', 'power': 0.9, 'begin': 10, 'end': 300, 'eta_min': 1e-05, 'by_epoch': True}]
|
||||
|
||||
vis_backends = [{'type': 'LocalVisBackend'}, {'type': 'TensorboardVisBackend'}, {'type': 'WandbVisBackend', 'init_kwargs': {'project': 'Seg_MMSeg_Test', 'name': 'my_deeplabv3plus_r50_r18_Pre_g1-300e_val1_check10_publicdataset_dresden-512x512-no_testslide'}}]
|
||||
|
||||
visualizer = {'name': 'visualizer', 'type': 'SegLocalVisualizer', 'vis_backends': [{'type': 'LocalVisBackend'}, {'type': 'TensorboardVisBackend'}, {'type': 'WandbVisBackend', 'init_kwargs': {'project': 'Seg_MMSeg_Test', 'name': 'my_deeplabv3plus_r50_r18_Pre_g1-300e_val1_check10_publicdataset_dresden-512x512-no_testslide'}}]}
|
||||
|
||||
@@ -0,0 +1,20 @@
|
||||
_base_ = ['../_base_/models/deeplabv3plus_r50-d8.py', '../_base_/datasets/publicdataset_endovis_2017.py', '../_base_/default_runtime.py', '../_base_/schedules/schedule_300e_val1_check10.py']
|
||||
|
||||
norm_cfg = {'type': 'BN'}
|
||||
|
||||
crop_size = (512, 512)
|
||||
|
||||
data_preprocessor = {'size': (512, 512), 'mean': [122.21429912990676, 77.0821859677977, 87.03836664626716], 'std': [50.53335800365262, 42.895340354037465, 47.739426483390446], 'bgr_to_rgb': False}
|
||||
|
||||
model = {'pretrained': './My_Local_Model/open_mmlab/resnet18_v1c.pth', 'backbone': {'depth': 18, 'type': 'ResNetV1c'}, 'data_preprocessor': {'size': (512, 512), 'mean': [122.21429912990676, 77.0821859677977, 87.03836664626716], 'std': [50.53335800365262, 42.895340354037465, 47.739426483390446], 'bgr_to_rgb': False}, 'decode_head': {'c1_in_channels': 64, 'c1_channels': 12, 'in_channels': 512, 'channels': 128, 'num_classes': 8, 'loss_decode': {'type': 'DiceLoss', 'use_sigmoid': False, 'loss_weight': 1.0}, 'align_corners': False, 'norm_cfg': {'type': 'BN'}}, 'auxiliary_head': {'in_channels': 256, 'channels': 64, 'num_classes': 8, 'loss_decode': {'type': 'DiceLoss', 'use_sigmoid': False, 'loss_weight': 1.0}, 'align_corners': False, 'norm_cfg': {'type': 'BN'}}}
|
||||
|
||||
test_cfg = {'crop_size': (512, 512)}
|
||||
|
||||
optim_wrapper = {'type': 'OptimWrapper', '_delete_': True, 'optimizer': {'type': 'AdamW', 'lr': 0.0001, 'weight_decay': 0.0005}, 'clip_grad': {'max_norm': 1, 'norm_type': 2}}
|
||||
|
||||
param_scheduler = [{'type': 'LinearLR', 'start_factor': 1e-06, 'by_epoch': True, 'begin': 0, 'end': 10}, {'type': 'PolyLR', 'power': 0.9, 'begin': 10, 'end': 300, 'eta_min': 1e-05, 'by_epoch': True}]
|
||||
|
||||
vis_backends = [{'type': 'LocalVisBackend'}, {'type': 'TensorboardVisBackend'}, {'type': 'WandbVisBackend', 'init_kwargs': {'project': 'Seg_MMSeg_Test', 'name': 'my_deeplabv3plus_r50_r18_Pre_g1-300e_val1_check10_publicdataset_endovis_2017-512x512-no_testslide'}}]
|
||||
|
||||
visualizer = {'name': 'visualizer', 'type': 'SegLocalVisualizer', 'vis_backends': [{'type': 'LocalVisBackend'}, {'type': 'TensorboardVisBackend'}, {'type': 'WandbVisBackend', 'init_kwargs': {'project': 'Seg_MMSeg_Test', 'name': 'my_deeplabv3plus_r50_r18_Pre_g1-300e_val1_check10_publicdataset_endovis_2017-512x512-no_testslide'}}]}
|
||||
|
||||
@@ -0,0 +1,20 @@
|
||||
_base_ = ['../_base_/models/deeplabv3plus_r50-d8.py', '../_base_/datasets/publicdataset_endovis_2018.py', '../_base_/default_runtime.py', '../_base_/schedules/schedule_300e_val1_check10.py']
|
||||
|
||||
norm_cfg = {'type': 'BN'}
|
||||
|
||||
crop_size = (512, 512)
|
||||
|
||||
data_preprocessor = {'size': (512, 512), 'mean': [122.21429912990676, 77.0821859677977, 87.03836664626716], 'std': [50.53335800365262, 42.895340354037465, 47.739426483390446], 'bgr_to_rgb': False}
|
||||
|
||||
model = {'pretrained': './My_Local_Model/open_mmlab/resnet18_v1c.pth', 'backbone': {'depth': 18, 'type': 'ResNetV1c'}, 'data_preprocessor': {'size': (512, 512), 'mean': [122.21429912990676, 77.0821859677977, 87.03836664626716], 'std': [50.53335800365262, 42.895340354037465, 47.739426483390446], 'bgr_to_rgb': False}, 'decode_head': {'c1_in_channels': 64, 'c1_channels': 12, 'in_channels': 512, 'channels': 128, 'num_classes': 8, 'loss_decode': {'type': 'DiceLoss', 'use_sigmoid': False, 'loss_weight': 1.0}, 'align_corners': False, 'norm_cfg': {'type': 'BN'}}, 'auxiliary_head': {'in_channels': 256, 'channels': 64, 'num_classes': 8, 'loss_decode': {'type': 'DiceLoss', 'use_sigmoid': False, 'loss_weight': 1.0}, 'align_corners': False, 'norm_cfg': {'type': 'BN'}}}
|
||||
|
||||
test_cfg = {'crop_size': (512, 512)}
|
||||
|
||||
optim_wrapper = {'type': 'OptimWrapper', '_delete_': True, 'optimizer': {'type': 'AdamW', 'lr': 0.0001, 'weight_decay': 0.0005}, 'clip_grad': {'max_norm': 1, 'norm_type': 2}}
|
||||
|
||||
param_scheduler = [{'type': 'LinearLR', 'start_factor': 1e-06, 'by_epoch': True, 'begin': 0, 'end': 10}, {'type': 'PolyLR', 'power': 0.9, 'begin': 10, 'end': 300, 'eta_min': 1e-05, 'by_epoch': True}]
|
||||
|
||||
vis_backends = [{'type': 'LocalVisBackend'}, {'type': 'TensorboardVisBackend'}, {'type': 'WandbVisBackend', 'init_kwargs': {'project': 'Seg_MMSeg_Test', 'name': 'my_deeplabv3plus_r50_r18_Pre_g1-300e_val1_check10_publicdataset_endovis_2018-512x512-no_testslide'}}]
|
||||
|
||||
visualizer = {'name': 'visualizer', 'type': 'SegLocalVisualizer', 'vis_backends': [{'type': 'LocalVisBackend'}, {'type': 'TensorboardVisBackend'}, {'type': 'WandbVisBackend', 'init_kwargs': {'project': 'Seg_MMSeg_Test', 'name': 'my_deeplabv3plus_r50_r18_Pre_g1-300e_val1_check10_publicdataset_endovis_2018-512x512-no_testslide'}}]}
|
||||
|
||||
@@ -0,0 +1,23 @@
|
||||
_base_ = ['../_base_/models/deeplabv3plus_r50-d8.py', '../_base_/datasets/publicdataset_autolaparo.py', '../_base_/default_runtime.py', '../_base_/schedules/schedule_300e_val1_check10.py']
|
||||
|
||||
norm_cfg = {'type': 'BN'}
|
||||
|
||||
crop_size = (512, 512)
|
||||
|
||||
# TODO ADD
|
||||
train_dataloader = dict(batch_size=8)
|
||||
|
||||
data_preprocessor = {'size': (512, 512), 'mean': [123.62464353460942, 85.34836259209033, 82.31539425671558], 'std': [47.172211618459315, 47.08256715323592, 48.135121265163605], 'bgr_to_rgb': False}
|
||||
|
||||
model = {'pretrained': './My_Local_Model/open_mmlab/resnet50_v1c.pth', 'backbone': {'depth': 50, 'type': 'ResNetV1c'}, 'data_preprocessor': {'size': (512, 512), 'mean': [123.62464353460942, 85.34836259209033, 82.31539425671558], 'std': [47.172211618459315, 47.08256715323592, 48.135121265163605], 'bgr_to_rgb': False}, 'decode_head': {'c1_in_channels': 256, 'c1_channels': 48, 'in_channels': 2048, 'channels': 512, 'num_classes': 10, 'loss_decode': {'type': 'DiceLoss', 'use_sigmoid': False, 'loss_weight': 1.0}, 'align_corners': False, 'norm_cfg': {'type': 'BN'}}, 'auxiliary_head': {'in_channels': 1024, 'channels': 256, 'num_classes': 10, 'loss_decode': {'type': 'DiceLoss', 'use_sigmoid': False, 'loss_weight': 1.0}, 'align_corners': False, 'norm_cfg': {'type': 'BN'}}}
|
||||
|
||||
test_cfg = {'crop_size': (512, 512)}
|
||||
|
||||
optim_wrapper = {'type': 'OptimWrapper', '_delete_': True, 'optimizer': {'type': 'AdamW', 'lr': 0.0001, 'weight_decay': 0.0005}, 'clip_grad': {'max_norm': 1, 'norm_type': 2}}
|
||||
|
||||
param_scheduler = [{'type': 'LinearLR', 'start_factor': 1e-06, 'by_epoch': True, 'begin': 0, 'end': 10}, {'type': 'PolyLR', 'power': 0.9, 'begin': 10, 'end': 300, 'eta_min': 1e-05, 'by_epoch': True}]
|
||||
|
||||
vis_backends = [{'type': 'LocalVisBackend'}, {'type': 'TensorboardVisBackend'}, {'type': 'WandbVisBackend', 'init_kwargs': {'project': 'Seg_MMSeg_Test', 'name': 'my_deeplabv3plus_r50_r50_Pre_g1-300e_val1_check10_publicdataset_autolaparo-512x512-no_testslide'}}]
|
||||
|
||||
visualizer = {'name': 'visualizer', 'type': 'SegLocalVisualizer', 'vis_backends': [{'type': 'LocalVisBackend'}, {'type': 'TensorboardVisBackend'}, {'type': 'WandbVisBackend', 'init_kwargs': {'project': 'Seg_MMSeg_Test', 'name': 'my_deeplabv3plus_r50_r50_Pre_g1-300e_val1_check10_publicdataset_autolaparo-512x512-no_testslide'}}]}
|
||||
|
||||
@@ -0,0 +1,22 @@
|
||||
_base_ = ['../_base_/models/deeplabv3plus_r50-d8.py', '../_base_/datasets/publicdataset_cholecseg8k.py', '../_base_/default_runtime.py', '../_base_/schedules/schedule_300e_val1_check10.py']
|
||||
|
||||
norm_cfg = {'type': 'BN'}
|
||||
|
||||
crop_size = (512, 512)
|
||||
|
||||
data_preprocessor = {'size': (512, 512), 'mean': [85.65740418979115, 53.99282220050495, 46.074045888534535], 'std': [72.24589167201978, 56.76979155397199, 49.056637115061775], 'bgr_to_rgb': False}
|
||||
# TODO ADD
|
||||
train_dataloader = dict(batch_size=8)
|
||||
|
||||
model = {'pretrained': './My_Local_Model/open_mmlab/resnet50_v1c.pth', 'backbone': {'depth': 50, 'type': 'ResNetV1c'}, 'data_preprocessor': {'size': (512, 512), 'mean': [85.65740418979115, 53.99282220050495, 46.074045888534535], 'std': [72.24589167201978, 56.76979155397199, 49.056637115061775], 'bgr_to_rgb': False}, 'decode_head': {'c1_in_channels': 256, 'c1_channels': 48, 'in_channels': 2048, 'channels': 512, 'num_classes': 13, 'loss_decode': {'type': 'DiceLoss', 'use_sigmoid': False, 'loss_weight': 1.0}, 'align_corners': False, 'norm_cfg': {'type': 'BN'}}, 'auxiliary_head': {'in_channels': 1024, 'channels': 256, 'num_classes': 13, 'loss_decode': {'type': 'DiceLoss', 'use_sigmoid': False, 'loss_weight': 1.0}, 'align_corners': False, 'norm_cfg': {'type': 'BN'}}}
|
||||
|
||||
test_cfg = {'crop_size': (512, 512)}
|
||||
|
||||
optim_wrapper = {'type': 'OptimWrapper', '_delete_': True, 'optimizer': {'type': 'AdamW', 'lr': 0.0001, 'weight_decay': 0.0005}, 'clip_grad': {'max_norm': 1, 'norm_type': 2}}
|
||||
|
||||
param_scheduler = [{'type': 'LinearLR', 'start_factor': 1e-06, 'by_epoch': True, 'begin': 0, 'end': 10}, {'type': 'PolyLR', 'power': 0.9, 'begin': 10, 'end': 300, 'eta_min': 1e-05, 'by_epoch': True}]
|
||||
|
||||
vis_backends = [{'type': 'LocalVisBackend'}, {'type': 'TensorboardVisBackend'}, {'type': 'WandbVisBackend', 'init_kwargs': {'project': 'Seg_MMSeg_Test', 'name': 'my_deeplabv3plus_r50_r50_Pre_g1-300e_val1_check10_publicdataset_cholecseg8k-512x512-no_testslide'}}]
|
||||
|
||||
visualizer = {'name': 'visualizer', 'type': 'SegLocalVisualizer', 'vis_backends': [{'type': 'LocalVisBackend'}, {'type': 'TensorboardVisBackend'}, {'type': 'WandbVisBackend', 'init_kwargs': {'project': 'Seg_MMSeg_Test', 'name': 'my_deeplabv3plus_r50_r50_Pre_g1-300e_val1_check10_publicdataset_cholecseg8k-512x512-no_testslide'}}]}
|
||||
|
||||
@@ -0,0 +1,23 @@
|
||||
_base_ = ['../_base_/models/deeplabv3plus_r50-d8.py', '../_base_/datasets/publicdataset_dresden.py', '../_base_/default_runtime.py', '../_base_/schedules/schedule_300e_val1_check10.py']
|
||||
|
||||
norm_cfg = {'type': 'BN'}
|
||||
|
||||
crop_size = (512, 512)
|
||||
|
||||
data_preprocessor = {'size': (512, 512), 'mean': [103.172638338208, 61.44762740851152, 51.407770213021976], 'std': [75.77031253622098, 54.63616729031377, 49.45572239497569], 'bgr_to_rgb': False}
|
||||
|
||||
# TODO ADD
|
||||
train_dataloader = dict(batch_size=8)
|
||||
|
||||
model = {'pretrained': './My_Local_Model/open_mmlab/resnet50_v1c.pth', 'backbone': {'depth': 50, 'type': 'ResNetV1c'}, 'data_preprocessor': {'size': (512, 512), 'mean': [103.172638338208, 61.44762740851152, 51.407770213021976], 'std': [75.77031253622098, 54.63616729031377, 49.45572239497569], 'bgr_to_rgb': False}, 'decode_head': {'c1_in_channels': 256, 'c1_channels': 48, 'in_channels': 2048, 'channels': 512, 'num_classes': 11, 'loss_decode': {'type': 'DiceLoss', 'use_sigmoid': False, 'loss_weight': 1.0}, 'align_corners': False, 'norm_cfg': {'type': 'BN'}}, 'auxiliary_head': {'in_channels': 1024, 'channels': 256, 'num_classes': 11, 'loss_decode': {'type': 'DiceLoss', 'use_sigmoid': False, 'loss_weight': 1.0}, 'align_corners': False, 'norm_cfg': {'type': 'BN'}}}
|
||||
|
||||
test_cfg = {'crop_size': (512, 512)}
|
||||
|
||||
optim_wrapper = {'type': 'OptimWrapper', '_delete_': True, 'optimizer': {'type': 'AdamW', 'lr': 0.0001, 'weight_decay': 0.0005}, 'clip_grad': {'max_norm': 1, 'norm_type': 2}}
|
||||
|
||||
param_scheduler = [{'type': 'LinearLR', 'start_factor': 1e-06, 'by_epoch': True, 'begin': 0, 'end': 10}, {'type': 'PolyLR', 'power': 0.9, 'begin': 10, 'end': 300, 'eta_min': 1e-05, 'by_epoch': True}]
|
||||
|
||||
vis_backends = [{'type': 'LocalVisBackend'}, {'type': 'TensorboardVisBackend'}, {'type': 'WandbVisBackend', 'init_kwargs': {'project': 'Seg_MMSeg_Test', 'name': 'my_deeplabv3plus_r50_r50_Pre_g1-300e_val1_check10_publicdataset_dresden-512x512-no_testslide'}}]
|
||||
|
||||
visualizer = {'name': 'visualizer', 'type': 'SegLocalVisualizer', 'vis_backends': [{'type': 'LocalVisBackend'}, {'type': 'TensorboardVisBackend'}, {'type': 'WandbVisBackend', 'init_kwargs': {'project': 'Seg_MMSeg_Test', 'name': 'my_deeplabv3plus_r50_r50_Pre_g1-300e_val1_check10_publicdataset_dresden-512x512-no_testslide'}}]}
|
||||
|
||||
@@ -0,0 +1,23 @@
|
||||
_base_ = ['../_base_/models/deeplabv3plus_r50-d8.py', '../_base_/datasets/publicdataset_endovis_2017.py', '../_base_/default_runtime.py', '../_base_/schedules/schedule_300e_val1_check10.py']
|
||||
|
||||
norm_cfg = {'type': 'BN'}
|
||||
|
||||
crop_size = (512, 512)
|
||||
|
||||
data_preprocessor = {'size': (512, 512), 'mean': [122.21429912990676, 77.0821859677977, 87.03836664626716], 'std': [50.53335800365262, 42.895340354037465, 47.739426483390446], 'bgr_to_rgb': False}
|
||||
|
||||
# TODO ADD
|
||||
train_dataloader = dict(batch_size=8)
|
||||
|
||||
model = {'pretrained': './My_Local_Model/open_mmlab/resnet50_v1c.pth', 'backbone': {'depth': 50, 'type': 'ResNetV1c'}, 'data_preprocessor': {'size': (512, 512), 'mean': [122.21429912990676, 77.0821859677977, 87.03836664626716], 'std': [50.53335800365262, 42.895340354037465, 47.739426483390446], 'bgr_to_rgb': False}, 'decode_head': {'c1_in_channels': 256, 'c1_channels': 48, 'in_channels': 2048, 'channels': 512, 'num_classes': 8, 'loss_decode': {'type': 'DiceLoss', 'use_sigmoid': False, 'loss_weight': 1.0}, 'align_corners': False, 'norm_cfg': {'type': 'BN'}}, 'auxiliary_head': {'in_channels': 1024, 'channels': 256, 'num_classes': 8, 'loss_decode': {'type': 'DiceLoss', 'use_sigmoid': False, 'loss_weight': 1.0}, 'align_corners': False, 'norm_cfg': {'type': 'BN'}}}
|
||||
|
||||
test_cfg = {'crop_size': (512, 512)}
|
||||
|
||||
optim_wrapper = {'type': 'OptimWrapper', '_delete_': True, 'optimizer': {'type': 'AdamW', 'lr': 0.0001, 'weight_decay': 0.0005}, 'clip_grad': {'max_norm': 1, 'norm_type': 2}}
|
||||
|
||||
param_scheduler = [{'type': 'LinearLR', 'start_factor': 1e-06, 'by_epoch': True, 'begin': 0, 'end': 10}, {'type': 'PolyLR', 'power': 0.9, 'begin': 10, 'end': 300, 'eta_min': 1e-05, 'by_epoch': True}]
|
||||
|
||||
vis_backends = [{'type': 'LocalVisBackend'}, {'type': 'TensorboardVisBackend'}, {'type': 'WandbVisBackend', 'init_kwargs': {'project': 'Seg_MMSeg_Test', 'name': 'my_deeplabv3plus_r50_r50_Pre_g1-300e_val1_check10_publicdataset_endovis_2017-512x512-no_testslide'}}]
|
||||
|
||||
visualizer = {'name': 'visualizer', 'type': 'SegLocalVisualizer', 'vis_backends': [{'type': 'LocalVisBackend'}, {'type': 'TensorboardVisBackend'}, {'type': 'WandbVisBackend', 'init_kwargs': {'project': 'Seg_MMSeg_Test', 'name': 'my_deeplabv3plus_r50_r50_Pre_g1-300e_val1_check10_publicdataset_endovis_2017-512x512-no_testslide'}}]}
|
||||
|
||||
@@ -0,0 +1,23 @@
|
||||
_base_ = ['../_base_/models/deeplabv3plus_r50-d8.py', '../_base_/datasets/publicdataset_endovis_2018.py', '../_base_/default_runtime.py', '../_base_/schedules/schedule_300e_val1_check10.py']
|
||||
|
||||
norm_cfg = {'type': 'BN'}
|
||||
|
||||
crop_size = (512, 512)
|
||||
|
||||
data_preprocessor = {'size': (512, 512), 'mean': [122.21429912990676, 77.0821859677977, 87.03836664626716], 'std': [50.53335800365262, 42.895340354037465, 47.739426483390446], 'bgr_to_rgb': False}
|
||||
|
||||
# TODO ADD
|
||||
train_dataloader = dict(batch_size=8)
|
||||
|
||||
model = {'pretrained': './My_Local_Model/open_mmlab/resnet50_v1c.pth', 'backbone': {'depth': 50, 'type': 'ResNetV1c'}, 'data_preprocessor': {'size': (512, 512), 'mean': [122.21429912990676, 77.0821859677977, 87.03836664626716], 'std': [50.53335800365262, 42.895340354037465, 47.739426483390446], 'bgr_to_rgb': False}, 'decode_head': {'c1_in_channels': 256, 'c1_channels': 48, 'in_channels': 2048, 'channels': 512, 'num_classes': 8, 'loss_decode': {'type': 'DiceLoss', 'use_sigmoid': False, 'loss_weight': 1.0}, 'align_corners': False, 'norm_cfg': {'type': 'BN'}}, 'auxiliary_head': {'in_channels': 1024, 'channels': 256, 'num_classes': 8, 'loss_decode': {'type': 'DiceLoss', 'use_sigmoid': False, 'loss_weight': 1.0}, 'align_corners': False, 'norm_cfg': {'type': 'BN'}}}
|
||||
|
||||
test_cfg = {'crop_size': (512, 512)}
|
||||
|
||||
optim_wrapper = {'type': 'OptimWrapper', '_delete_': True, 'optimizer': {'type': 'AdamW', 'lr': 0.0001, 'weight_decay': 0.0005}, 'clip_grad': {'max_norm': 1, 'norm_type': 2}}
|
||||
|
||||
param_scheduler = [{'type': 'LinearLR', 'start_factor': 1e-06, 'by_epoch': True, 'begin': 0, 'end': 10}, {'type': 'PolyLR', 'power': 0.9, 'begin': 10, 'end': 300, 'eta_min': 1e-05, 'by_epoch': True}]
|
||||
|
||||
vis_backends = [{'type': 'LocalVisBackend'}, {'type': 'TensorboardVisBackend'}, {'type': 'WandbVisBackend', 'init_kwargs': {'project': 'Seg_MMSeg_Test', 'name': 'my_deeplabv3plus_r50_r50_Pre_g1-300e_val1_check10_publicdataset_endovis_2018-512x512-no_testslide'}}]
|
||||
|
||||
visualizer = {'name': 'visualizer', 'type': 'SegLocalVisualizer', 'vis_backends': [{'type': 'LocalVisBackend'}, {'type': 'TensorboardVisBackend'}, {'type': 'WandbVisBackend', 'init_kwargs': {'project': 'Seg_MMSeg_Test', 'name': 'my_deeplabv3plus_r50_r50_Pre_g1-300e_val1_check10_publicdataset_endovis_2018-512x512-no_testslide'}}]}
|
||||
|
||||
Reference in New Issue
Block a user