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# HRNet
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> [Deep High-Resolution Representation Learning for Human Pose Estimation](https://arxiv.org/abs/1908.07919)
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## Introduction
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<!-- [BACKBONE] -->
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<a href="https://github.com/HRNet/HRNet-Semantic-Segmentation">Official Repo</a>
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<a href="https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/hrnet.py#L218">Code Snippet</a>
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## Abstract
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<!-- [ABSTRACT] -->
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High-resolution representations are essential for position-sensitive vision problems, such as human pose estimation, semantic segmentation, and object detection. Existing state-of-the-art frameworks first encode the input image as a low-resolution representation through a subnetwork that is formed by connecting high-to-low resolution convolutions \\emph{in series} (e.g., ResNet, VGGNet), and then recover the high-resolution representation from the encoded low-resolution representation. Instead, our proposed network, named as High-Resolution Network (HRNet), maintains high-resolution representations through the whole process. There are two key characteristics: (i) Connect the high-to-low resolution convolution streams \\emph{in parallel}; (ii) Repeatedly exchange the information across resolutions. The benefit is that the resulting representation is semantically richer and spatially more precise. We show the superiority of the proposed HRNet in a wide range of applications, including human pose estimation, semantic segmentation, and object detection, suggesting that the HRNet is a stronger backbone for computer vision problems. All the codes are available at [this https URL](https://github.com/HRNet).
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<!-- [IMAGE] -->
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<div align=center>
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<img src="https://user-images.githubusercontent.com/24582831/142901680-64c285bc-669f-4924-b054-46a2f07c5427.png" width="80%"/>
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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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| FCN | HRNetV2p-W18-Small | 512x1024 | 40000 | 1.7 | 23.74 | V100 | 73.86 | 75.91 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/hrnet/fcn_hr18s_4xb2-40k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x1024_40k_cityscapes/fcn_hr18s_512x1024_40k_cityscapes_20200601_014216-93db27d0.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x1024_40k_cityscapes/fcn_hr18s_512x1024_40k_cityscapes_20200601_014216.log.json) |
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| FCN | HRNetV2p-W18 | 512x1024 | 40000 | 2.9 | 12.97 | V100 | 77.19 | 78.92 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/hrnet/fcn_hr18_4xb2-40k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x1024_40k_cityscapes/fcn_hr18_512x1024_40k_cityscapes_20200601_014216-f196fb4e.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x1024_40k_cityscapes/fcn_hr18_512x1024_40k_cityscapes_20200601_014216.log.json) |
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| FCN | HRNetV2p-W48 | 512x1024 | 40000 | 6.2 | 6.42 | V100 | 78.48 | 79.69 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/hrnet/fcn_hr48_4xb2-40k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x1024_40k_cityscapes/fcn_hr48_512x1024_40k_cityscapes_20200601_014240-a989b146.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x1024_40k_cityscapes/fcn_hr48_512x1024_40k_cityscapes_20200601_014240.log.json) |
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| FCN | HRNetV2p-W18-Small | 512x1024 | 80000 | - | - | V100 | 75.31 | 77.48 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/hrnet/fcn_hr18s_4xb2-80k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x1024_80k_cityscapes/fcn_hr18s_512x1024_80k_cityscapes_20200601_202700-1462b75d.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x1024_80k_cityscapes/fcn_hr18s_512x1024_80k_cityscapes_20200601_202700.log.json) |
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| FCN | HRNetV2p-W18 | 512x1024 | 80000 | - | - | V100 | 78.65 | 80.35 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/hrnet/fcn_hr18_4xb2-80k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x1024_80k_cityscapes/fcn_hr18_512x1024_80k_cityscapes_20200601_223255-4e7b345e.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x1024_80k_cityscapes/fcn_hr18_512x1024_80k_cityscapes_20200601_223255.log.json) |
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| FCN | HRNetV2p-W48 | 512x1024 | 80000 | - | - | V100 | 79.93 | 80.72 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/hrnet/fcn_hr48_4xb2-80k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x1024_80k_cityscapes/fcn_hr48_512x1024_80k_cityscapes_20200601_202606-58ea95d6.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x1024_80k_cityscapes/fcn_hr48_512x1024_80k_cityscapes_20200601_202606.log.json) |
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| FCN | HRNetV2p-W18-Small | 512x1024 | 160000 | - | - | V100 | 76.31 | 78.31 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/hrnet/fcn_hr18s_4xb2-160k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x1024_160k_cityscapes/fcn_hr18s_512x1024_160k_cityscapes_20200602_190901-4a0797ea.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x1024_160k_cityscapes/fcn_hr18s_512x1024_160k_cityscapes_20200602_190901.log.json) |
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| FCN | HRNetV2p-W18 | 512x1024 | 160000 | - | - | V100 | 78.80 | 80.74 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/hrnet/fcn_hr18_4xb2-160k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x1024_160k_cityscapes/fcn_hr18_512x1024_160k_cityscapes_20200602_190822-221e4a4f.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x1024_160k_cityscapes/fcn_hr18_512x1024_160k_cityscapes_20200602_190822.log.json) |
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| FCN | HRNetV2p-W48 | 512x1024 | 160000 | - | - | V100 | 80.65 | 81.92 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/hrnet/fcn_hr48_4xb2-160k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x1024_160k_cityscapes/fcn_hr48_512x1024_160k_cityscapes_20200602_190946-59b7973e.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x1024_160k_cityscapes/fcn_hr48_512x1024_160k_cityscapes_20200602_190946.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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| FCN | HRNetV2p-W18-Small | 512x512 | 80000 | 3.8 | 38.66 | V100 | 31.38 | 32.45 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/hrnet/fcn_hr18s_4xb4-80k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_80k_ade20k/fcn_hr18s_512x512_80k_ade20k_20200614_144345-77fc814a.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_80k_ade20k/fcn_hr18s_512x512_80k_ade20k_20200614_144345.log.json) |
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| FCN | HRNetV2p-W18 | 512x512 | 80000 | 4.9 | 22.57 | V100 | 36.27 | 37.28 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/hrnet/fcn_hr18_4xb4-80k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_80k_ade20k/fcn_hr18_512x512_80k_ade20k_20210827_114910-6c9382c0.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_80k_ade20k/fcn_hr18_512x512_80k_ade20k_20210827_114910.log.json) |
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| FCN | HRNetV2p-W48 | 512x512 | 80000 | 8.2 | 21.23 | V100 | 41.90 | 43.27 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/hrnet/fcn_hr48_4xb4-80k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_80k_ade20k/fcn_hr48_512x512_80k_ade20k_20200614_193946-7ba5258d.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_80k_ade20k/fcn_hr48_512x512_80k_ade20k_20200614_193946.log.json) |
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| FCN | HRNetV2p-W18-Small | 512x512 | 160000 | - | - | V100 | 33.07 | 34.56 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/hrnet/fcn_hr18s_4xb4-160k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_160k_ade20k/fcn_hr18s_512x512_160k_ade20k_20210829_174739-f1e7c2e7.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_160k_ade20k/fcn_hr18s_512x512_160k_ade20k_20210829_174739.log.json) |
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| FCN | HRNetV2p-W18 | 512x512 | 160000 | - | - | V100 | 36.79 | 38.58 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/hrnet/fcn_hr18_4xb4-160k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_160k_ade20k/fcn_hr18_512x512_160k_ade20k_20200614_214426-ca961836.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_160k_ade20k/fcn_hr18_512x512_160k_ade20k_20200614_214426.log.json) |
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| FCN | HRNetV2p-W48 | 512x512 | 160000 | - | - | V100 | 42.02 | 43.86 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/hrnet/fcn_hr48_4xb4-160k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_160k_ade20k/fcn_hr48_512x512_160k_ade20k_20200614_214407-a52fc02c.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_160k_ade20k/fcn_hr48_512x512_160k_ade20k_20200614_214407.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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| FCN | HRNetV2p-W18-Small | 512x512 | 20000 | 1.8 | 43.36 | V100 | 65.5 | 68.89 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/hrnet/fcn_hr18s_4xb4-20k_voc12aug-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_20k_voc12aug/fcn_hr18s_512x512_20k_voc12aug_20210829_174910-0aceadb4.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_20k_voc12aug/fcn_hr18s_512x512_20k_voc12aug_20210829_174910.log.json) |
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| FCN | HRNetV2p-W18 | 512x512 | 20000 | 2.9 | 23.48 | V100 | 72.30 | 74.71 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/hrnet/fcn_hr18_4xb4-20k_voc12aug-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_20k_voc12aug/fcn_hr18_512x512_20k_voc12aug_20200617_224503-488d45f7.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_20k_voc12aug/fcn_hr18_512x512_20k_voc12aug_20200617_224503.log.json) |
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| FCN | HRNetV2p-W48 | 512x512 | 20000 | 6.2 | 22.05 | V100 | 75.87 | 78.58 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/hrnet/fcn_hr48_4xb4-20k_voc12aug-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_20k_voc12aug/fcn_hr48_512x512_20k_voc12aug_20200617_224419-89de05cd.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_20k_voc12aug/fcn_hr48_512x512_20k_voc12aug_20200617_224419.log.json) |
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| FCN | HRNetV2p-W18-Small | 512x512 | 40000 | - | - | V100 | 66.61 | 70.00 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/hrnet/fcn_hr18s_4xb4-40k_voc12aug-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_40k_voc12aug/fcn_hr18s_512x512_40k_voc12aug_20200614_000648-4f8d6e7f.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_40k_voc12aug/fcn_hr18s_512x512_40k_voc12aug_20200614_000648.log.json) |
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| FCN | HRNetV2p-W18 | 512x512 | 40000 | - | - | V100 | 72.90 | 75.59 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/hrnet/fcn_hr18_4xb4-40k_voc12aug-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_40k_voc12aug/fcn_hr18_512x512_40k_voc12aug_20200613_224401-1b4b76cd.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_40k_voc12aug/fcn_hr18_512x512_40k_voc12aug_20200613_224401.log.json) |
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| FCN | HRNetV2p-W48 | 512x512 | 40000 | - | - | V100 | 76.24 | 78.49 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/hrnet/fcn_hr48_4xb4-40k_voc12aug-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_40k_voc12aug/fcn_hr48_512x512_40k_voc12aug_20200613_222111-1b0f18bc.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_40k_voc12aug/fcn_hr48_512x512_40k_voc12aug_20200613_222111.log.json) |
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### Pascal Context
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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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| FCN | HRNetV2p-W48 | 480x480 | 40000 | 6.1 | 8.86 | V100 | 45.14 | 47.42 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/hrnet/fcn_hr48_4xb4-40k_pascal-context-480x480.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_480x480_40k_pascal_context/fcn_hr48_480x480_40k_pascal_context_20200911_164852-667d00b0.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_480x480_40k_pascal_context/fcn_hr48_480x480_40k_pascal_context-20200911_164852.log.json) |
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| FCN | HRNetV2p-W48 | 480x480 | 80000 | - | - | V100 | 45.84 | 47.84 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/hrnet/fcn_hr48_4xb4-80k_pascal-context-480x480.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_480x480_80k_pascal_context/fcn_hr48_480x480_80k_pascal_context_20200911_155322-847a6711.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_480x480_80k_pascal_context/fcn_hr48_480x480_80k_pascal_context-20200911_155322.log.json) |
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### Pascal Context 59
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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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| FCN | HRNetV2p-W48 | 480x480 | 40000 | - | - | V100 | 50.33 | 52.83 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/hrnet/fcn_hr48_4xb4-40k_pascal-context-59-480x480.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_480x480_40k_pascal_context_59/fcn_hr48_480x480_40k_pascal_context_59_20210410_122738-b808b8b2.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_480x480_40k_pascal_context_59/fcn_hr48_480x480_40k_pascal_context_59-20210410_122738.log.json) |
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| FCN | HRNetV2p-W48 | 480x480 | 80000 | - | - | V100 | 51.12 | 53.56 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/hrnet/fcn_hr48_4xb4-80k_pascal-context-59-480x480.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_480x480_80k_pascal_context_59/fcn_hr48_480x480_80k_pascal_context_59_20210411_003240-3ae7081e.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_480x480_80k_pascal_context_59/fcn_hr48_480x480_80k_pascal_context_59-20210411_003240.log.json) |
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### LoveDA
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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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| FCN | HRNetV2p-W18-Small | 512x512 | 80000 | 1.59 | 24.87 | V100 | 49.28 | 49.42 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/hrnet/fcn_hr18s_4xb4-80k_loveda-512x512.pyy) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_80k_loveda/fcn_hr18s_512x512_80k_loveda_20211210_203228-60a86a7a.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_80k_loveda/fcn_hr18s_512x512_80k_loveda_20211210_203228.log.json) |
|
||||
| FCN | HRNetV2p-W18 | 512x512 | 80000 | 2.76 | 12.92 | V100 | 50.81 | 50.95 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/hrnet/fcn_hr18_4xb4-80k_loveda-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_80k_loveda/fcn_hr18_512x512_80k_loveda_20211210_203952-93d9c3b3.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_80k_loveda/fcn_hr18_512x512_80k_loveda_20211210_203952.log.json) |
|
||||
| FCN | HRNetV2p-W48 | 512x512 | 80000 | 6.20 | 9.61 | V100 | 51.42 | 51.64 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/hrnet/fcn_hr48_4xb4-80k_loveda-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_80k_loveda/fcn_hr48_512x512_80k_loveda_20211211_044756-67072f55.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_80k_loveda/fcn_hr48_512x512_80k_loveda_20211211_044756.log.json) |
|
||||
|
||||
### Potsdam
|
||||
|
||||
| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device | mIoU | mIoU(ms+flip) | config | download |
|
||||
| ------ | ------------------ | --------- | ------: | -------- | -------------- | ------ | ----: | ------------: | -------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
|
||||
| FCN | HRNetV2p-W18-Small | 512x512 | 80000 | 1.58 | 36.00 | V100 | 77.64 | 78.8 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/hrnet/fcn_hr18s_4xb4-80k_potsdam-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_80k_potsdam/fcn_hr18s_512x512_80k_potsdam_20211218_205517-ba32af63.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_80k_potsdam/fcn_hr18s_512x512_80k_potsdam_20211218_205517.log.json) |
|
||||
| FCN | HRNetV2p-W18 | 512x512 | 80000 | 2.76 | 19.25 | V100 | 78.26 | 79.24 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/hrnet/fcn_hr18_4xb4-80k_potsdam-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_80k_potsdam/fcn_hr18_512x512_80k_potsdam_20211218_205517-5d0387ad.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_80k_potsdam/fcn_hr18_512x512_80k_potsdam_20211218_205517.log.json) |
|
||||
| FCN | HRNetV2p-W48 | 512x512 | 80000 | 6.20 | 16.42 | V100 | 78.39 | 79.34 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/hrnet/fcn_hr48_4xb4-80k_potsdam-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_80k_potsdam/fcn_hr48_512x512_80k_potsdam_20211219_020601-97434c78.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_80k_potsdam/fcn_hr48_512x512_80k_potsdam_20211219_020601.log.json) |
|
||||
|
||||
### Vaihingen
|
||||
|
||||
| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device | mIoU | mIoU(ms+flip) | config | download |
|
||||
| ------ | ------------------ | --------- | ------: | -------- | -------------- | ------ | ----: | ------------: | ---------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
|
||||
| FCN | HRNetV2p-W18-Small | 512x512 | 80000 | 1.58 | 38.11 | V100 | 71.81 | 73.1 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/hrnet/fcn_hr18s_4xb4-80k_vaihingen-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_4x4_512x512_80k_vaihingen/fcn_hr18s_4x4_512x512_80k_vaihingen_20211231_230909-b23aae02.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_4x4_512x512_80k_vaihingen/fcn_hr18s_4x4_512x512_80k_vaihingen_20211231_230909.log.json) |
|
||||
| FCN | HRNetV2p-W18 | 512x512 | 80000 | 2.76 | 19.55 | V100 | 72.57 | 74.09 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/hrnet/fcn_hr18_4xb4-80k_vaihingen-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_4x4_512x512_80k_vaihingen/fcn_hr18_4x4_512x512_80k_vaihingen_20211231_231216-2ec3ae8a.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_4x4_512x512_80k_vaihingen/fcn_hr18_4x4_512x512_80k_vaihingen_20211231_231216.log.json) |
|
||||
| FCN | HRNetV2p-W48 | 512x512 | 80000 | 6.20 | 17.25 | V100 | 72.50 | 73.52 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/hrnet/fcn_hr48_4xb4-80k_vaihingen-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_4x4_512x512_80k_vaihingen/fcn_hr48_4x4_512x512_80k_vaihingen_20211231_231244-7133cb22.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_4x4_512x512_80k_vaihingen/fcn_hr48_4x4_512x512_80k_vaihingen_20211231_231244.log.json) |
|
||||
|
||||
### iSAID
|
||||
|
||||
| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device | mIoU | mIoU(ms+flip) | config | download |
|
||||
| ------ | ------------------ | --------- | ------: | -------- | -------------- | ------ | ----: | ------------: | ------------------------------------------------------------------------------------------------------------------ | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
||||
| FCN | HRNetV2p-W18-Small | 896x896 | 80000 | 4.95 | 13.84 | V100 | 62.30 | 62.97 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/hrnet/fcn_hr18s_4xb4-80k_isaid-896x896.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_4x4_896x896_80k_isaid/fcn_hr18s_4x4_896x896_80k_isaid_20220118_001603-3cc0769b.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_4x4_896x896_80k_isaid/fcn_hr18s_4x4_896x896_80k_isaid_20220118_001603.log.json) |
|
||||
| FCN | HRNetV2p-W18 | 896x896 | 80000 | 8.30 | 7.71 | V100 | 65.06 | 65.60 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/hrnet/fcn_hr18_4xb4-80k_isaid-896x896.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_4x4_896x896_80k_isaid/fcn_hr18_4x4_896x896_80k_isaid_20220110_182230-49bf752e.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_4x4_896x896_80k_isaid/fcn_hr18_4x4_896x896_80k_isaid_20220110_182230.log.json) |
|
||||
| FCN | HRNetV2p-W48 | 896x896 | 80000 | 16.89 | 7.34 | V100 | 67.80 | 68.53 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/hrnet/fcn_hr48_4xb4-80k_isaid-896x896.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_4x4_896x896_80k_isaid/fcn_hr48_4x4_896x896_80k_isaid_20220114_174643-547fc420.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_4x4_896x896_80k_isaid/fcn_hr48_4x4_896x896_80k_isaid_20220114_174643.log.json) |
|
||||
|
||||
Note:
|
||||
|
||||
- `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
|
||||
|
||||
```bibtext
|
||||
@inproceedings{SunXLW19,
|
||||
title={Deep High-Resolution Representation Learning for Human Pose Estimation},
|
||||
author={Ke Sun and Bin Xiao and Dong Liu and Jingdong Wang},
|
||||
booktitle={CVPR},
|
||||
year={2019}
|
||||
}
|
||||
```
|
||||
@@ -0,0 +1,7 @@
|
||||
_base_ = [
|
||||
'../_base_/models/fcn_hr18.py', '../_base_/datasets/cityscapes.py',
|
||||
'../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py'
|
||||
]
|
||||
crop_size = (512, 1024)
|
||||
data_preprocessor = dict(size=crop_size)
|
||||
model = dict(data_preprocessor=data_preprocessor)
|
||||
@@ -0,0 +1,7 @@
|
||||
_base_ = [
|
||||
'../_base_/models/fcn_hr18.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 +1,7 @@
|
||||
_base_ = [
|
||||
'../_base_/models/fcn_hr18.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 +1,8 @@
|
||||
_base_ = [
|
||||
'../_base_/models/fcn_hr18.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))
|
||||
@@ -0,0 +1,8 @@
|
||||
_base_ = [
|
||||
'../_base_/models/fcn_hr18.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))
|
||||
@@ -0,0 +1,12 @@
|
||||
_base_ = [
|
||||
'../_base_/models/fcn_hr18.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),
|
||||
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 +1,12 @@
|
||||
_base_ = [
|
||||
'../_base_/models/fcn_hr18.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),
|
||||
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 +1,8 @@
|
||||
_base_ = [
|
||||
'../_base_/models/fcn_hr18.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))
|
||||
@@ -0,0 +1,8 @@
|
||||
_base_ = [
|
||||
'../_base_/models/fcn_hr18.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))
|
||||
@@ -0,0 +1,8 @@
|
||||
_base_ = [
|
||||
'../_base_/models/fcn_hr18.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))
|
||||
@@ -0,0 +1,8 @@
|
||||
_base_ = [
|
||||
'../_base_/models/fcn_hr18.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))
|
||||
@@ -0,0 +1,12 @@
|
||||
_base_ = [
|
||||
'../_base_/models/fcn_hr18.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),
|
||||
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 +1,12 @@
|
||||
_base_ = [
|
||||
'../_base_/models/fcn_hr18.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),
|
||||
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 +1,8 @@
|
||||
_base_ = [
|
||||
'../_base_/models/fcn_hr18.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))
|
||||
@@ -0,0 +1,8 @@
|
||||
_base_ = [
|
||||
'../_base_/models/fcn_hr18.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))
|
||||
@@ -0,0 +1,9 @@
|
||||
_base_ = './fcn_hr18_4xb2-160k_cityscapes-512x1024.py'
|
||||
model = dict(
|
||||
pretrained='open-mmlab://msra/hrnetv2_w18_small',
|
||||
backbone=dict(
|
||||
extra=dict(
|
||||
stage1=dict(num_blocks=(2, )),
|
||||
stage2=dict(num_blocks=(2, 2)),
|
||||
stage3=dict(num_modules=3, num_blocks=(2, 2, 2)),
|
||||
stage4=dict(num_modules=2, num_blocks=(2, 2, 2, 2)))))
|
||||
@@ -0,0 +1,9 @@
|
||||
_base_ = './fcn_hr18_4xb2-40k_cityscapes-512x1024.py'
|
||||
model = dict(
|
||||
pretrained='open-mmlab://msra/hrnetv2_w18_small',
|
||||
backbone=dict(
|
||||
extra=dict(
|
||||
stage1=dict(num_blocks=(2, )),
|
||||
stage2=dict(num_blocks=(2, 2)),
|
||||
stage3=dict(num_modules=3, num_blocks=(2, 2, 2)),
|
||||
stage4=dict(num_modules=2, num_blocks=(2, 2, 2, 2)))))
|
||||
@@ -0,0 +1,9 @@
|
||||
_base_ = './fcn_hr18_4xb2-80k_cityscapes-512x1024.py'
|
||||
model = dict(
|
||||
pretrained='open-mmlab://msra/hrnetv2_w18_small',
|
||||
backbone=dict(
|
||||
extra=dict(
|
||||
stage1=dict(num_blocks=(2, )),
|
||||
stage2=dict(num_blocks=(2, 2)),
|
||||
stage3=dict(num_modules=3, num_blocks=(2, 2, 2)),
|
||||
stage4=dict(num_modules=2, num_blocks=(2, 2, 2, 2)))))
|
||||
@@ -0,0 +1,9 @@
|
||||
_base_ = './fcn_hr18_4xb4-160k_ade20k-512x512.py'
|
||||
model = dict(
|
||||
pretrained='open-mmlab://msra/hrnetv2_w18_small',
|
||||
backbone=dict(
|
||||
extra=dict(
|
||||
stage1=dict(num_blocks=(2, )),
|
||||
stage2=dict(num_blocks=(2, 2)),
|
||||
stage3=dict(num_modules=3, num_blocks=(2, 2, 2)),
|
||||
stage4=dict(num_modules=2, num_blocks=(2, 2, 2, 2)))))
|
||||
@@ -0,0 +1,9 @@
|
||||
_base_ = './fcn_hr18_4xb4-20k_voc12aug-512x512.py'
|
||||
model = dict(
|
||||
pretrained='open-mmlab://msra/hrnetv2_w18_small',
|
||||
backbone=dict(
|
||||
extra=dict(
|
||||
stage1=dict(num_blocks=(2, )),
|
||||
stage2=dict(num_blocks=(2, 2)),
|
||||
stage3=dict(num_modules=3, num_blocks=(2, 2, 2)),
|
||||
stage4=dict(num_modules=2, num_blocks=(2, 2, 2, 2)))))
|
||||
@@ -0,0 +1,9 @@
|
||||
_base_ = './fcn_hr18_4xb4-40k_pascal-context-480x480.py'
|
||||
model = dict(
|
||||
pretrained='open-mmlab://msra/hrnetv2_w18_small',
|
||||
backbone=dict(
|
||||
extra=dict(
|
||||
stage1=dict(num_blocks=(2, )),
|
||||
stage2=dict(num_blocks=(2, 2)),
|
||||
stage3=dict(num_modules=3, num_blocks=(2, 2, 2)),
|
||||
stage4=dict(num_modules=2, num_blocks=(2, 2, 2, 2)))))
|
||||
@@ -0,0 +1,9 @@
|
||||
_base_ = './fcn_hr18_4xb4-40k_pascal-context-59-480x480.py'
|
||||
model = dict(
|
||||
pretrained='open-mmlab://msra/hrnetv2_w18_small',
|
||||
backbone=dict(
|
||||
extra=dict(
|
||||
stage1=dict(num_blocks=(2, )),
|
||||
stage2=dict(num_blocks=(2, 2)),
|
||||
stage3=dict(num_modules=3, num_blocks=(2, 2, 2)),
|
||||
stage4=dict(num_modules=2, num_blocks=(2, 2, 2, 2)))))
|
||||
@@ -0,0 +1,9 @@
|
||||
_base_ = './fcn_hr18_4xb4-40k_voc12aug-512x512.py'
|
||||
model = dict(
|
||||
pretrained='open-mmlab://msra/hrnetv2_w18_small',
|
||||
backbone=dict(
|
||||
extra=dict(
|
||||
stage1=dict(num_blocks=(2, )),
|
||||
stage2=dict(num_blocks=(2, 2)),
|
||||
stage3=dict(num_modules=3, num_blocks=(2, 2, 2)),
|
||||
stage4=dict(num_modules=2, num_blocks=(2, 2, 2, 2)))))
|
||||
@@ -0,0 +1,9 @@
|
||||
_base_ = './fcn_hr18_4xb4-80k_ade20k-512x512.py'
|
||||
model = dict(
|
||||
pretrained='open-mmlab://msra/hrnetv2_w18_small',
|
||||
backbone=dict(
|
||||
extra=dict(
|
||||
stage1=dict(num_blocks=(2, )),
|
||||
stage2=dict(num_blocks=(2, 2)),
|
||||
stage3=dict(num_modules=3, num_blocks=(2, 2, 2)),
|
||||
stage4=dict(num_modules=2, num_blocks=(2, 2, 2, 2)))))
|
||||
@@ -0,0 +1,9 @@
|
||||
_base_ = './fcn_hr18_4xb4-80k_isaid-896x896.py'
|
||||
model = dict(
|
||||
pretrained='open-mmlab://msra/hrnetv2_w18_small',
|
||||
backbone=dict(
|
||||
extra=dict(
|
||||
stage1=dict(num_blocks=(2, )),
|
||||
stage2=dict(num_blocks=(2, 2)),
|
||||
stage3=dict(num_modules=3, num_blocks=(2, 2, 2)),
|
||||
stage4=dict(num_modules=2, num_blocks=(2, 2, 2, 2)))))
|
||||
@@ -0,0 +1,9 @@
|
||||
_base_ = './fcn_hr18_4xb4-80k_loveda-512x512.py'
|
||||
model = dict(
|
||||
pretrained='open-mmlab://msra/hrnetv2_w18_small',
|
||||
backbone=dict(
|
||||
extra=dict(
|
||||
stage1=dict(num_blocks=(2, )),
|
||||
stage2=dict(num_blocks=(2, 2)),
|
||||
stage3=dict(num_modules=3, num_blocks=(2, 2, 2)),
|
||||
stage4=dict(num_modules=2, num_blocks=(2, 2, 2, 2)))))
|
||||
@@ -0,0 +1,9 @@
|
||||
_base_ = './fcn_hr18_4xb4-80k_pascal-context-480x480.py'
|
||||
model = dict(
|
||||
pretrained='open-mmlab://msra/hrnetv2_w18_small',
|
||||
backbone=dict(
|
||||
extra=dict(
|
||||
stage1=dict(num_blocks=(2, )),
|
||||
stage2=dict(num_blocks=(2, 2)),
|
||||
stage3=dict(num_modules=3, num_blocks=(2, 2, 2)),
|
||||
stage4=dict(num_modules=2, num_blocks=(2, 2, 2, 2)))))
|
||||
@@ -0,0 +1,9 @@
|
||||
_base_ = './fcn_hr18_4xb4-80k_pascal-context-59-480x480.py'
|
||||
model = dict(
|
||||
pretrained='open-mmlab://msra/hrnetv2_w18_small',
|
||||
backbone=dict(
|
||||
extra=dict(
|
||||
stage1=dict(num_blocks=(2, )),
|
||||
stage2=dict(num_blocks=(2, 2)),
|
||||
stage3=dict(num_modules=3, num_blocks=(2, 2, 2)),
|
||||
stage4=dict(num_modules=2, num_blocks=(2, 2, 2, 2)))))
|
||||
@@ -0,0 +1,9 @@
|
||||
_base_ = './fcn_hr18_4xb4-80k_potsdam-512x512.py'
|
||||
model = dict(
|
||||
pretrained='open-mmlab://msra/hrnetv2_w18_small',
|
||||
backbone=dict(
|
||||
extra=dict(
|
||||
stage1=dict(num_blocks=(2, )),
|
||||
stage2=dict(num_blocks=(2, 2)),
|
||||
stage3=dict(num_modules=3, num_blocks=(2, 2, 2)),
|
||||
stage4=dict(num_modules=2, num_blocks=(2, 2, 2, 2)))))
|
||||
@@ -0,0 +1,9 @@
|
||||
_base_ = './fcn_hr18_4xb4-80k_vaihingen-512x512.py'
|
||||
model = dict(
|
||||
pretrained='open-mmlab://msra/hrnetv2_w18_small',
|
||||
backbone=dict(
|
||||
extra=dict(
|
||||
stage1=dict(num_blocks=(2, )),
|
||||
stage2=dict(num_blocks=(2, 2)),
|
||||
stage3=dict(num_modules=3, num_blocks=(2, 2, 2)),
|
||||
stage4=dict(num_modules=2, num_blocks=(2, 2, 2, 2)))))
|
||||
@@ -0,0 +1,10 @@
|
||||
_base_ = './fcn_hr18_4xb2-160k_cityscapes-512x1024.py'
|
||||
model = dict(
|
||||
pretrained='open-mmlab://msra/hrnetv2_w48',
|
||||
backbone=dict(
|
||||
extra=dict(
|
||||
stage2=dict(num_channels=(48, 96)),
|
||||
stage3=dict(num_channels=(48, 96, 192)),
|
||||
stage4=dict(num_channels=(48, 96, 192, 384)))),
|
||||
decode_head=dict(
|
||||
in_channels=[48, 96, 192, 384], channels=sum([48, 96, 192, 384])))
|
||||
@@ -0,0 +1,10 @@
|
||||
_base_ = './fcn_hr18_4xb2-40k_cityscapes-512x1024.py'
|
||||
model = dict(
|
||||
pretrained='open-mmlab://msra/hrnetv2_w48',
|
||||
backbone=dict(
|
||||
extra=dict(
|
||||
stage2=dict(num_channels=(48, 96)),
|
||||
stage3=dict(num_channels=(48, 96, 192)),
|
||||
stage4=dict(num_channels=(48, 96, 192, 384)))),
|
||||
decode_head=dict(
|
||||
in_channels=[48, 96, 192, 384], channels=sum([48, 96, 192, 384])))
|
||||
@@ -0,0 +1,10 @@
|
||||
_base_ = './fcn_hr18_4xb2-80k_cityscapes-512x1024.py'
|
||||
model = dict(
|
||||
pretrained='open-mmlab://msra/hrnetv2_w48',
|
||||
backbone=dict(
|
||||
extra=dict(
|
||||
stage2=dict(num_channels=(48, 96)),
|
||||
stage3=dict(num_channels=(48, 96, 192)),
|
||||
stage4=dict(num_channels=(48, 96, 192, 384)))),
|
||||
decode_head=dict(
|
||||
in_channels=[48, 96, 192, 384], channels=sum([48, 96, 192, 384])))
|
||||
@@ -0,0 +1,10 @@
|
||||
_base_ = './fcn_hr18_4xb4-160k_ade20k-512x512.py'
|
||||
model = dict(
|
||||
pretrained='open-mmlab://msra/hrnetv2_w48',
|
||||
backbone=dict(
|
||||
extra=dict(
|
||||
stage2=dict(num_channels=(48, 96)),
|
||||
stage3=dict(num_channels=(48, 96, 192)),
|
||||
stage4=dict(num_channels=(48, 96, 192, 384)))),
|
||||
decode_head=dict(
|
||||
in_channels=[48, 96, 192, 384], channels=sum([48, 96, 192, 384])))
|
||||
@@ -0,0 +1,10 @@
|
||||
_base_ = './fcn_hr18_4xb4-20k_voc12aug-512x512.py'
|
||||
model = dict(
|
||||
pretrained='open-mmlab://msra/hrnetv2_w48',
|
||||
backbone=dict(
|
||||
extra=dict(
|
||||
stage2=dict(num_channels=(48, 96)),
|
||||
stage3=dict(num_channels=(48, 96, 192)),
|
||||
stage4=dict(num_channels=(48, 96, 192, 384)))),
|
||||
decode_head=dict(
|
||||
in_channels=[48, 96, 192, 384], channels=sum([48, 96, 192, 384])))
|
||||
@@ -0,0 +1,10 @@
|
||||
_base_ = './fcn_hr18_4xb4-40k_pascal-context-480x480.py'
|
||||
model = dict(
|
||||
pretrained='open-mmlab://msra/hrnetv2_w48',
|
||||
backbone=dict(
|
||||
extra=dict(
|
||||
stage2=dict(num_channels=(48, 96)),
|
||||
stage3=dict(num_channels=(48, 96, 192)),
|
||||
stage4=dict(num_channels=(48, 96, 192, 384)))),
|
||||
decode_head=dict(
|
||||
in_channels=[48, 96, 192, 384], channels=sum([48, 96, 192, 384])))
|
||||
@@ -0,0 +1,10 @@
|
||||
_base_ = './fcn_hr18_4xb4-40k_pascal-context-59-480x480.py'
|
||||
model = dict(
|
||||
pretrained='open-mmlab://msra/hrnetv2_w48',
|
||||
backbone=dict(
|
||||
extra=dict(
|
||||
stage2=dict(num_channels=(48, 96)),
|
||||
stage3=dict(num_channels=(48, 96, 192)),
|
||||
stage4=dict(num_channels=(48, 96, 192, 384)))),
|
||||
decode_head=dict(
|
||||
in_channels=[48, 96, 192, 384], channels=sum([48, 96, 192, 384])))
|
||||
@@ -0,0 +1,10 @@
|
||||
_base_ = './fcn_hr18_4xb4-40k_voc12aug-512x512.py'
|
||||
model = dict(
|
||||
pretrained='open-mmlab://msra/hrnetv2_w48',
|
||||
backbone=dict(
|
||||
extra=dict(
|
||||
stage2=dict(num_channels=(48, 96)),
|
||||
stage3=dict(num_channels=(48, 96, 192)),
|
||||
stage4=dict(num_channels=(48, 96, 192, 384)))),
|
||||
decode_head=dict(
|
||||
in_channels=[48, 96, 192, 384], channels=sum([48, 96, 192, 384])))
|
||||
@@ -0,0 +1,10 @@
|
||||
_base_ = './fcn_hr18_4xb4-80k_ade20k-512x512.py'
|
||||
model = dict(
|
||||
pretrained='open-mmlab://msra/hrnetv2_w48',
|
||||
backbone=dict(
|
||||
extra=dict(
|
||||
stage2=dict(num_channels=(48, 96)),
|
||||
stage3=dict(num_channels=(48, 96, 192)),
|
||||
stage4=dict(num_channels=(48, 96, 192, 384)))),
|
||||
decode_head=dict(
|
||||
in_channels=[48, 96, 192, 384], channels=sum([48, 96, 192, 384])))
|
||||
@@ -0,0 +1,10 @@
|
||||
_base_ = './fcn_hr18_4xb4-80k_isaid-896x896.py'
|
||||
model = dict(
|
||||
pretrained='open-mmlab://msra/hrnetv2_w48',
|
||||
backbone=dict(
|
||||
extra=dict(
|
||||
stage2=dict(num_channels=(48, 96)),
|
||||
stage3=dict(num_channels=(48, 96, 192)),
|
||||
stage4=dict(num_channels=(48, 96, 192, 384)))),
|
||||
decode_head=dict(
|
||||
in_channels=[48, 96, 192, 384], channels=sum([48, 96, 192, 384])))
|
||||
@@ -0,0 +1,10 @@
|
||||
_base_ = './fcn_hr18_4xb4-80k_loveda-512x512.py'
|
||||
model = dict(
|
||||
pretrained='open-mmlab://msra/hrnetv2_w48',
|
||||
backbone=dict(
|
||||
extra=dict(
|
||||
stage2=dict(num_channels=(48, 96)),
|
||||
stage3=dict(num_channels=(48, 96, 192)),
|
||||
stage4=dict(num_channels=(48, 96, 192, 384)))),
|
||||
decode_head=dict(
|
||||
in_channels=[48, 96, 192, 384], channels=sum([48, 96, 192, 384])))
|
||||
@@ -0,0 +1,10 @@
|
||||
_base_ = './fcn_hr18_4xb4-80k_pascal-context-480x480.py'
|
||||
model = dict(
|
||||
pretrained='open-mmlab://msra/hrnetv2_w48',
|
||||
backbone=dict(
|
||||
extra=dict(
|
||||
stage2=dict(num_channels=(48, 96)),
|
||||
stage3=dict(num_channels=(48, 96, 192)),
|
||||
stage4=dict(num_channels=(48, 96, 192, 384)))),
|
||||
decode_head=dict(
|
||||
in_channels=[48, 96, 192, 384], channels=sum([48, 96, 192, 384])))
|
||||
@@ -0,0 +1,10 @@
|
||||
_base_ = './fcn_hr18_4xb4-80k_pascal-context-59-480x480.py'
|
||||
model = dict(
|
||||
pretrained='open-mmlab://msra/hrnetv2_w48',
|
||||
backbone=dict(
|
||||
extra=dict(
|
||||
stage2=dict(num_channels=(48, 96)),
|
||||
stage3=dict(num_channels=(48, 96, 192)),
|
||||
stage4=dict(num_channels=(48, 96, 192, 384)))),
|
||||
decode_head=dict(
|
||||
in_channels=[48, 96, 192, 384], channels=sum([48, 96, 192, 384])))
|
||||
@@ -0,0 +1,10 @@
|
||||
_base_ = './fcn_hr18_4xb4-80k_potsdam-512x512.py'
|
||||
model = dict(
|
||||
pretrained='open-mmlab://msra/hrnetv2_w48',
|
||||
backbone=dict(
|
||||
extra=dict(
|
||||
stage2=dict(num_channels=(48, 96)),
|
||||
stage3=dict(num_channels=(48, 96, 192)),
|
||||
stage4=dict(num_channels=(48, 96, 192, 384)))),
|
||||
decode_head=dict(
|
||||
in_channels=[48, 96, 192, 384], channels=sum([48, 96, 192, 384])))
|
||||
@@ -0,0 +1,10 @@
|
||||
_base_ = './fcn_hr18_4xb4-80k_vaihingen-512x512.py'
|
||||
model = dict(
|
||||
pretrained='open-mmlab://msra/hrnetv2_w48',
|
||||
backbone=dict(
|
||||
extra=dict(
|
||||
stage2=dict(num_channels=(48, 96)),
|
||||
stage3=dict(num_channels=(48, 96, 192)),
|
||||
stage4=dict(num_channels=(48, 96, 192, 384)))),
|
||||
decode_head=dict(
|
||||
in_channels=[48, 96, 192, 384], channels=sum([48, 96, 192, 384])))
|
||||
874
Seg_All_In_One_MMSeg/configs/hrnet/metafile.yaml
Normal file
874
Seg_All_In_One_MMSeg/configs/hrnet/metafile.yaml
Normal file
@@ -0,0 +1,874 @@
|
||||
Models:
|
||||
- Name: fcn_hr18s_4xb2-40k_cityscapes-512x1024
|
||||
In Collection: FCN
|
||||
Results:
|
||||
Task: Semantic Segmentation
|
||||
Dataset: Cityscapes
|
||||
Metrics:
|
||||
mIoU: 73.86
|
||||
mIoU(ms+flip): 75.91
|
||||
Config: configs/hrnet/fcn_hr18s_4xb2-40k_cityscapes-512x1024.py
|
||||
Metadata:
|
||||
Training Data: Cityscapes
|
||||
Batch Size: 8
|
||||
Architecture:
|
||||
- HRNetV2p-W18-Small
|
||||
- FCN
|
||||
Training Resources: 4x V100 GPUS
|
||||
Memory (GB): 1.7
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x1024_40k_cityscapes/fcn_hr18s_512x1024_40k_cityscapes_20200601_014216-93db27d0.pth
|
||||
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x1024_40k_cityscapes/fcn_hr18s_512x1024_40k_cityscapes_20200601_014216.log.json
|
||||
Paper:
|
||||
Title: Deep High-Resolution Representation Learning for Human Pose Estimation
|
||||
URL: https://arxiv.org/abs/1908.07919
|
||||
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/hrnet.py#L218
|
||||
Framework: PyTorch
|
||||
- Name: fcn_hr18_4xb2-40k_cityscapes-512x1024
|
||||
In Collection: FCN
|
||||
Results:
|
||||
Task: Semantic Segmentation
|
||||
Dataset: Cityscapes
|
||||
Metrics:
|
||||
mIoU: 77.19
|
||||
mIoU(ms+flip): 78.92
|
||||
Config: configs/hrnet/fcn_hr18_4xb2-40k_cityscapes-512x1024.py
|
||||
Metadata:
|
||||
Training Data: Cityscapes
|
||||
Batch Size: 8
|
||||
Architecture:
|
||||
- HRNetV2p-W18
|
||||
- FCN
|
||||
Training Resources: 4x V100 GPUS
|
||||
Memory (GB): 2.9
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x1024_40k_cityscapes/fcn_hr18_512x1024_40k_cityscapes_20200601_014216-f196fb4e.pth
|
||||
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x1024_40k_cityscapes/fcn_hr18_512x1024_40k_cityscapes_20200601_014216.log.json
|
||||
Paper:
|
||||
Title: Deep High-Resolution Representation Learning for Human Pose Estimation
|
||||
URL: https://arxiv.org/abs/1908.07919
|
||||
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/hrnet.py#L218
|
||||
Framework: PyTorch
|
||||
- Name: fcn_hr48_4xb2-40k_cityscapes-512x1024
|
||||
In Collection: FCN
|
||||
Results:
|
||||
Task: Semantic Segmentation
|
||||
Dataset: Cityscapes
|
||||
Metrics:
|
||||
mIoU: 78.48
|
||||
mIoU(ms+flip): 79.69
|
||||
Config: configs/hrnet/fcn_hr48_4xb2-40k_cityscapes-512x1024.py
|
||||
Metadata:
|
||||
Training Data: Cityscapes
|
||||
Batch Size: 8
|
||||
Architecture:
|
||||
- HRNetV2p-W48
|
||||
- FCN
|
||||
Training Resources: 4x V100 GPUS
|
||||
Memory (GB): 6.2
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x1024_40k_cityscapes/fcn_hr48_512x1024_40k_cityscapes_20200601_014240-a989b146.pth
|
||||
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x1024_40k_cityscapes/fcn_hr48_512x1024_40k_cityscapes_20200601_014240.log.json
|
||||
Paper:
|
||||
Title: Deep High-Resolution Representation Learning for Human Pose Estimation
|
||||
URL: https://arxiv.org/abs/1908.07919
|
||||
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/hrnet.py#L218
|
||||
Framework: PyTorch
|
||||
- Name: fcn_hr18s_4xb2-80k_cityscapes-512x1024
|
||||
In Collection: FCN
|
||||
Results:
|
||||
Task: Semantic Segmentation
|
||||
Dataset: Cityscapes
|
||||
Metrics:
|
||||
mIoU: 75.31
|
||||
mIoU(ms+flip): 77.48
|
||||
Config: configs/hrnet/fcn_hr18s_4xb2-80k_cityscapes-512x1024.py
|
||||
Metadata:
|
||||
Training Data: Cityscapes
|
||||
Batch Size: 8
|
||||
Architecture:
|
||||
- HRNetV2p-W18-Small
|
||||
- FCN
|
||||
Training Resources: 4x V100 GPUS
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x1024_80k_cityscapes/fcn_hr18s_512x1024_80k_cityscapes_20200601_202700-1462b75d.pth
|
||||
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x1024_80k_cityscapes/fcn_hr18s_512x1024_80k_cityscapes_20200601_202700.log.json
|
||||
Paper:
|
||||
Title: Deep High-Resolution Representation Learning for Human Pose Estimation
|
||||
URL: https://arxiv.org/abs/1908.07919
|
||||
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/hrnet.py#L218
|
||||
Framework: PyTorch
|
||||
- Name: fcn_hr18_4xb2-80k_cityscapes-512x1024
|
||||
In Collection: FCN
|
||||
Results:
|
||||
Task: Semantic Segmentation
|
||||
Dataset: Cityscapes
|
||||
Metrics:
|
||||
mIoU: 78.65
|
||||
mIoU(ms+flip): 80.35
|
||||
Config: configs/hrnet/fcn_hr18_4xb2-80k_cityscapes-512x1024.py
|
||||
Metadata:
|
||||
Training Data: Cityscapes
|
||||
Batch Size: 8
|
||||
Architecture:
|
||||
- HRNetV2p-W18
|
||||
- FCN
|
||||
Training Resources: 4x V100 GPUS
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x1024_80k_cityscapes/fcn_hr18_512x1024_80k_cityscapes_20200601_223255-4e7b345e.pth
|
||||
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x1024_80k_cityscapes/fcn_hr18_512x1024_80k_cityscapes_20200601_223255.log.json
|
||||
Paper:
|
||||
Title: Deep High-Resolution Representation Learning for Human Pose Estimation
|
||||
URL: https://arxiv.org/abs/1908.07919
|
||||
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/hrnet.py#L218
|
||||
Framework: PyTorch
|
||||
- Name: fcn_hr48_4xb2-80k_cityscapes-512x1024
|
||||
In Collection: FCN
|
||||
Results:
|
||||
Task: Semantic Segmentation
|
||||
Dataset: Cityscapes
|
||||
Metrics:
|
||||
mIoU: 79.93
|
||||
mIoU(ms+flip): 80.72
|
||||
Config: configs/hrnet/fcn_hr48_4xb2-80k_cityscapes-512x1024.py
|
||||
Metadata:
|
||||
Training Data: Cityscapes
|
||||
Batch Size: 8
|
||||
Architecture:
|
||||
- HRNetV2p-W48
|
||||
- FCN
|
||||
Training Resources: 4x V100 GPUS
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x1024_80k_cityscapes/fcn_hr48_512x1024_80k_cityscapes_20200601_202606-58ea95d6.pth
|
||||
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x1024_80k_cityscapes/fcn_hr48_512x1024_80k_cityscapes_20200601_202606.log.json
|
||||
Paper:
|
||||
Title: Deep High-Resolution Representation Learning for Human Pose Estimation
|
||||
URL: https://arxiv.org/abs/1908.07919
|
||||
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/hrnet.py#L218
|
||||
Framework: PyTorch
|
||||
- Name: fcn_hr18s_4xb2-160k_cityscapes-512x1024
|
||||
In Collection: FCN
|
||||
Results:
|
||||
Task: Semantic Segmentation
|
||||
Dataset: Cityscapes
|
||||
Metrics:
|
||||
mIoU: 76.31
|
||||
mIoU(ms+flip): 78.31
|
||||
Config: configs/hrnet/fcn_hr18s_4xb2-160k_cityscapes-512x1024.py
|
||||
Metadata:
|
||||
Training Data: Cityscapes
|
||||
Batch Size: 8
|
||||
Architecture:
|
||||
- HRNetV2p-W18-Small
|
||||
- FCN
|
||||
Training Resources: 4x V100 GPUS
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x1024_160k_cityscapes/fcn_hr18s_512x1024_160k_cityscapes_20200602_190901-4a0797ea.pth
|
||||
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x1024_160k_cityscapes/fcn_hr18s_512x1024_160k_cityscapes_20200602_190901.log.json
|
||||
Paper:
|
||||
Title: Deep High-Resolution Representation Learning for Human Pose Estimation
|
||||
URL: https://arxiv.org/abs/1908.07919
|
||||
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/hrnet.py#L218
|
||||
Framework: PyTorch
|
||||
- Name: fcn_hr18_4xb2-160k_cityscapes-512x1024
|
||||
In Collection: FCN
|
||||
Results:
|
||||
Task: Semantic Segmentation
|
||||
Dataset: Cityscapes
|
||||
Metrics:
|
||||
mIoU: 78.8
|
||||
mIoU(ms+flip): 80.74
|
||||
Config: configs/hrnet/fcn_hr18_4xb2-160k_cityscapes-512x1024.py
|
||||
Metadata:
|
||||
Training Data: Cityscapes
|
||||
Batch Size: 8
|
||||
Architecture:
|
||||
- HRNetV2p-W18
|
||||
- FCN
|
||||
Training Resources: 4x V100 GPUS
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x1024_160k_cityscapes/fcn_hr18_512x1024_160k_cityscapes_20200602_190822-221e4a4f.pth
|
||||
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x1024_160k_cityscapes/fcn_hr18_512x1024_160k_cityscapes_20200602_190822.log.json
|
||||
Paper:
|
||||
Title: Deep High-Resolution Representation Learning for Human Pose Estimation
|
||||
URL: https://arxiv.org/abs/1908.07919
|
||||
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/hrnet.py#L218
|
||||
Framework: PyTorch
|
||||
- Name: fcn_hr48_4xb2-160k_cityscapes-512x1024
|
||||
In Collection: FCN
|
||||
Results:
|
||||
Task: Semantic Segmentation
|
||||
Dataset: Cityscapes
|
||||
Metrics:
|
||||
mIoU: 80.65
|
||||
mIoU(ms+flip): 81.92
|
||||
Config: configs/hrnet/fcn_hr48_4xb2-160k_cityscapes-512x1024.py
|
||||
Metadata:
|
||||
Training Data: Cityscapes
|
||||
Batch Size: 8
|
||||
Architecture:
|
||||
- HRNetV2p-W48
|
||||
- FCN
|
||||
Training Resources: 4x V100 GPUS
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x1024_160k_cityscapes/fcn_hr48_512x1024_160k_cityscapes_20200602_190946-59b7973e.pth
|
||||
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x1024_160k_cityscapes/fcn_hr48_512x1024_160k_cityscapes_20200602_190946.log.json
|
||||
Paper:
|
||||
Title: Deep High-Resolution Representation Learning for Human Pose Estimation
|
||||
URL: https://arxiv.org/abs/1908.07919
|
||||
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/hrnet.py#L218
|
||||
Framework: PyTorch
|
||||
- Name: fcn_hr18s_4xb4-80k_ade20k-512x512
|
||||
In Collection: FCN
|
||||
Results:
|
||||
Task: Semantic Segmentation
|
||||
Dataset: ADE20K
|
||||
Metrics:
|
||||
mIoU: 31.38
|
||||
mIoU(ms+flip): 32.45
|
||||
Config: configs/hrnet/fcn_hr18s_4xb4-80k_ade20k-512x512.py
|
||||
Metadata:
|
||||
Training Data: ADE20K
|
||||
Batch Size: 16
|
||||
Architecture:
|
||||
- HRNetV2p-W18-Small
|
||||
- FCN
|
||||
Training Resources: 4x V100 GPUS
|
||||
Memory (GB): 3.8
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_80k_ade20k/fcn_hr18s_512x512_80k_ade20k_20200614_144345-77fc814a.pth
|
||||
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_80k_ade20k/fcn_hr18s_512x512_80k_ade20k_20200614_144345.log.json
|
||||
Paper:
|
||||
Title: Deep High-Resolution Representation Learning for Human Pose Estimation
|
||||
URL: https://arxiv.org/abs/1908.07919
|
||||
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/hrnet.py#L218
|
||||
Framework: PyTorch
|
||||
- Name: fcn_hr18_4xb4-80k_ade20k-512x512
|
||||
In Collection: FCN
|
||||
Results:
|
||||
Task: Semantic Segmentation
|
||||
Dataset: ADE20K
|
||||
Metrics:
|
||||
mIoU: 36.27
|
||||
mIoU(ms+flip): 37.28
|
||||
Config: configs/hrnet/fcn_hr18_4xb4-80k_ade20k-512x512.py
|
||||
Metadata:
|
||||
Training Data: ADE20K
|
||||
Batch Size: 16
|
||||
Architecture:
|
||||
- HRNetV2p-W18
|
||||
- FCN
|
||||
Training Resources: 4x V100 GPUS
|
||||
Memory (GB): 4.9
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_80k_ade20k/fcn_hr18_512x512_80k_ade20k_20210827_114910-6c9382c0.pth
|
||||
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_80k_ade20k/fcn_hr18_512x512_80k_ade20k_20210827_114910.log.json
|
||||
Paper:
|
||||
Title: Deep High-Resolution Representation Learning for Human Pose Estimation
|
||||
URL: https://arxiv.org/abs/1908.07919
|
||||
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/hrnet.py#L218
|
||||
Framework: PyTorch
|
||||
- Name: fcn_hr48_4xb4-80k_ade20k-512x512
|
||||
In Collection: FCN
|
||||
Results:
|
||||
Task: Semantic Segmentation
|
||||
Dataset: ADE20K
|
||||
Metrics:
|
||||
mIoU: 41.9
|
||||
mIoU(ms+flip): 43.27
|
||||
Config: configs/hrnet/fcn_hr48_4xb4-80k_ade20k-512x512.py
|
||||
Metadata:
|
||||
Training Data: ADE20K
|
||||
Batch Size: 16
|
||||
Architecture:
|
||||
- HRNetV2p-W48
|
||||
- FCN
|
||||
Training Resources: 4x V100 GPUS
|
||||
Memory (GB): 8.2
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_80k_ade20k/fcn_hr48_512x512_80k_ade20k_20200614_193946-7ba5258d.pth
|
||||
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_80k_ade20k/fcn_hr48_512x512_80k_ade20k_20200614_193946.log.json
|
||||
Paper:
|
||||
Title: Deep High-Resolution Representation Learning for Human Pose Estimation
|
||||
URL: https://arxiv.org/abs/1908.07919
|
||||
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/hrnet.py#L218
|
||||
Framework: PyTorch
|
||||
- Name: fcn_hr18s_4xb4-160k_ade20k-512x512
|
||||
In Collection: FCN
|
||||
Results:
|
||||
Task: Semantic Segmentation
|
||||
Dataset: ADE20K
|
||||
Metrics:
|
||||
mIoU: 33.07
|
||||
mIoU(ms+flip): 34.56
|
||||
Config: configs/hrnet/fcn_hr18s_4xb4-160k_ade20k-512x512.py
|
||||
Metadata:
|
||||
Training Data: ADE20K
|
||||
Batch Size: 16
|
||||
Architecture:
|
||||
- HRNetV2p-W18-Small
|
||||
- FCN
|
||||
Training Resources: 4x V100 GPUS
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_160k_ade20k/fcn_hr18s_512x512_160k_ade20k_20210829_174739-f1e7c2e7.pth
|
||||
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_160k_ade20k/fcn_hr18s_512x512_160k_ade20k_20210829_174739.log.json
|
||||
Paper:
|
||||
Title: Deep High-Resolution Representation Learning for Human Pose Estimation
|
||||
URL: https://arxiv.org/abs/1908.07919
|
||||
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/hrnet.py#L218
|
||||
Framework: PyTorch
|
||||
- Name: fcn_hr18_4xb4-160k_ade20k-512x512
|
||||
In Collection: FCN
|
||||
Results:
|
||||
Task: Semantic Segmentation
|
||||
Dataset: ADE20K
|
||||
Metrics:
|
||||
mIoU: 36.79
|
||||
mIoU(ms+flip): 38.58
|
||||
Config: configs/hrnet/fcn_hr18_4xb4-160k_ade20k-512x512.py
|
||||
Metadata:
|
||||
Training Data: ADE20K
|
||||
Batch Size: 16
|
||||
Architecture:
|
||||
- HRNetV2p-W18
|
||||
- FCN
|
||||
Training Resources: 4x V100 GPUS
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_160k_ade20k/fcn_hr18_512x512_160k_ade20k_20200614_214426-ca961836.pth
|
||||
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_160k_ade20k/fcn_hr18_512x512_160k_ade20k_20200614_214426.log.json
|
||||
Paper:
|
||||
Title: Deep High-Resolution Representation Learning for Human Pose Estimation
|
||||
URL: https://arxiv.org/abs/1908.07919
|
||||
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/hrnet.py#L218
|
||||
Framework: PyTorch
|
||||
- Name: fcn_hr48_4xb4-160k_ade20k-512x512
|
||||
In Collection: FCN
|
||||
Results:
|
||||
Task: Semantic Segmentation
|
||||
Dataset: ADE20K
|
||||
Metrics:
|
||||
mIoU: 42.02
|
||||
mIoU(ms+flip): 43.86
|
||||
Config: configs/hrnet/fcn_hr48_4xb4-160k_ade20k-512x512.py
|
||||
Metadata:
|
||||
Training Data: ADE20K
|
||||
Batch Size: 16
|
||||
Architecture:
|
||||
- HRNetV2p-W48
|
||||
- FCN
|
||||
Training Resources: 4x V100 GPUS
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_160k_ade20k/fcn_hr48_512x512_160k_ade20k_20200614_214407-a52fc02c.pth
|
||||
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_160k_ade20k/fcn_hr48_512x512_160k_ade20k_20200614_214407.log.json
|
||||
Paper:
|
||||
Title: Deep High-Resolution Representation Learning for Human Pose Estimation
|
||||
URL: https://arxiv.org/abs/1908.07919
|
||||
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/hrnet.py#L218
|
||||
Framework: PyTorch
|
||||
- Name: fcn_hr18s_4xb4-20k_voc12aug-512x512
|
||||
In Collection: FCN
|
||||
Results:
|
||||
Task: Semantic Segmentation
|
||||
Dataset: Pascal VOC 2012 + Aug
|
||||
Metrics:
|
||||
mIoU: 65.5
|
||||
mIoU(ms+flip): 68.89
|
||||
Config: configs/hrnet/fcn_hr18s_4xb4-20k_voc12aug-512x512.py
|
||||
Metadata:
|
||||
Training Data: Pascal VOC 2012 + Aug
|
||||
Batch Size: 16
|
||||
Architecture:
|
||||
- HRNetV2p-W18-Small
|
||||
- FCN
|
||||
Training Resources: 4x V100 GPUS
|
||||
Memory (GB): 1.8
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_20k_voc12aug/fcn_hr18s_512x512_20k_voc12aug_20210829_174910-0aceadb4.pth
|
||||
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_20k_voc12aug/fcn_hr18s_512x512_20k_voc12aug_20210829_174910.log.json
|
||||
Paper:
|
||||
Title: Deep High-Resolution Representation Learning for Human Pose Estimation
|
||||
URL: https://arxiv.org/abs/1908.07919
|
||||
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/hrnet.py#L218
|
||||
Framework: PyTorch
|
||||
- Name: fcn_hr18_4xb4-20k_voc12aug-512x512
|
||||
In Collection: FCN
|
||||
Results:
|
||||
Task: Semantic Segmentation
|
||||
Dataset: Pascal VOC 2012 + Aug
|
||||
Metrics:
|
||||
mIoU: 72.3
|
||||
mIoU(ms+flip): 74.71
|
||||
Config: configs/hrnet/fcn_hr18_4xb4-20k_voc12aug-512x512.py
|
||||
Metadata:
|
||||
Training Data: Pascal VOC 2012 + Aug
|
||||
Batch Size: 16
|
||||
Architecture:
|
||||
- HRNetV2p-W18
|
||||
- FCN
|
||||
Training Resources: 4x V100 GPUS
|
||||
Memory (GB): 2.9
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_20k_voc12aug/fcn_hr18_512x512_20k_voc12aug_20200617_224503-488d45f7.pth
|
||||
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_20k_voc12aug/fcn_hr18_512x512_20k_voc12aug_20200617_224503.log.json
|
||||
Paper:
|
||||
Title: Deep High-Resolution Representation Learning for Human Pose Estimation
|
||||
URL: https://arxiv.org/abs/1908.07919
|
||||
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/hrnet.py#L218
|
||||
Framework: PyTorch
|
||||
- Name: fcn_hr48_4xb4-20k_voc12aug-512x512
|
||||
In Collection: FCN
|
||||
Results:
|
||||
Task: Semantic Segmentation
|
||||
Dataset: Pascal VOC 2012 + Aug
|
||||
Metrics:
|
||||
mIoU: 75.87
|
||||
mIoU(ms+flip): 78.58
|
||||
Config: configs/hrnet/fcn_hr48_4xb4-20k_voc12aug-512x512.py
|
||||
Metadata:
|
||||
Training Data: Pascal VOC 2012 + Aug
|
||||
Batch Size: 16
|
||||
Architecture:
|
||||
- HRNetV2p-W48
|
||||
- FCN
|
||||
Training Resources: 4x V100 GPUS
|
||||
Memory (GB): 6.2
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_20k_voc12aug/fcn_hr48_512x512_20k_voc12aug_20200617_224419-89de05cd.pth
|
||||
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_20k_voc12aug/fcn_hr48_512x512_20k_voc12aug_20200617_224419.log.json
|
||||
Paper:
|
||||
Title: Deep High-Resolution Representation Learning for Human Pose Estimation
|
||||
URL: https://arxiv.org/abs/1908.07919
|
||||
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/hrnet.py#L218
|
||||
Framework: PyTorch
|
||||
- Name: fcn_hr18s_4xb4-40k_voc12aug-512x512
|
||||
In Collection: FCN
|
||||
Results:
|
||||
Task: Semantic Segmentation
|
||||
Dataset: Pascal VOC 2012 + Aug
|
||||
Metrics:
|
||||
mIoU: 66.61
|
||||
mIoU(ms+flip): 70.0
|
||||
Config: configs/hrnet/fcn_hr18s_4xb4-40k_voc12aug-512x512.py
|
||||
Metadata:
|
||||
Training Data: Pascal VOC 2012 + Aug
|
||||
Batch Size: 16
|
||||
Architecture:
|
||||
- HRNetV2p-W18-Small
|
||||
- FCN
|
||||
Training Resources: 4x V100 GPUS
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_40k_voc12aug/fcn_hr18s_512x512_40k_voc12aug_20200614_000648-4f8d6e7f.pth
|
||||
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_40k_voc12aug/fcn_hr18s_512x512_40k_voc12aug_20200614_000648.log.json
|
||||
Paper:
|
||||
Title: Deep High-Resolution Representation Learning for Human Pose Estimation
|
||||
URL: https://arxiv.org/abs/1908.07919
|
||||
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/hrnet.py#L218
|
||||
Framework: PyTorch
|
||||
- Name: fcn_hr18_4xb4-40k_voc12aug-512x512
|
||||
In Collection: FCN
|
||||
Results:
|
||||
Task: Semantic Segmentation
|
||||
Dataset: Pascal VOC 2012 + Aug
|
||||
Metrics:
|
||||
mIoU: 72.9
|
||||
mIoU(ms+flip): 75.59
|
||||
Config: configs/hrnet/fcn_hr18_4xb4-40k_voc12aug-512x512.py
|
||||
Metadata:
|
||||
Training Data: Pascal VOC 2012 + Aug
|
||||
Batch Size: 16
|
||||
Architecture:
|
||||
- HRNetV2p-W18
|
||||
- FCN
|
||||
Training Resources: 4x V100 GPUS
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_40k_voc12aug/fcn_hr18_512x512_40k_voc12aug_20200613_224401-1b4b76cd.pth
|
||||
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_40k_voc12aug/fcn_hr18_512x512_40k_voc12aug_20200613_224401.log.json
|
||||
Paper:
|
||||
Title: Deep High-Resolution Representation Learning for Human Pose Estimation
|
||||
URL: https://arxiv.org/abs/1908.07919
|
||||
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/hrnet.py#L218
|
||||
Framework: PyTorch
|
||||
- Name: fcn_hr48_4xb4-40k_voc12aug-512x512
|
||||
In Collection: FCN
|
||||
Results:
|
||||
Task: Semantic Segmentation
|
||||
Dataset: Pascal VOC 2012 + Aug
|
||||
Metrics:
|
||||
mIoU: 76.24
|
||||
mIoU(ms+flip): 78.49
|
||||
Config: configs/hrnet/fcn_hr48_4xb4-40k_voc12aug-512x512.py
|
||||
Metadata:
|
||||
Training Data: Pascal VOC 2012 + Aug
|
||||
Batch Size: 16
|
||||
Architecture:
|
||||
- HRNetV2p-W48
|
||||
- FCN
|
||||
Training Resources: 4x V100 GPUS
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_40k_voc12aug/fcn_hr48_512x512_40k_voc12aug_20200613_222111-1b0f18bc.pth
|
||||
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_40k_voc12aug/fcn_hr48_512x512_40k_voc12aug_20200613_222111.log.json
|
||||
Paper:
|
||||
Title: Deep High-Resolution Representation Learning for Human Pose Estimation
|
||||
URL: https://arxiv.org/abs/1908.07919
|
||||
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/hrnet.py#L218
|
||||
Framework: PyTorch
|
||||
- Name: fcn_hr48_4xb4-40k_pascal-context-480x480
|
||||
In Collection: FCN
|
||||
Results:
|
||||
Task: Semantic Segmentation
|
||||
Dataset: Pascal Context
|
||||
Metrics:
|
||||
mIoU: 45.14
|
||||
mIoU(ms+flip): 47.42
|
||||
Config: configs/hrnet/fcn_hr48_4xb4-40k_pascal-context-480x480.py
|
||||
Metadata:
|
||||
Training Data: Pascal Context
|
||||
Batch Size: 16
|
||||
Architecture:
|
||||
- HRNetV2p-W48
|
||||
- FCN
|
||||
Training Resources: 4x V100 GPUS
|
||||
Memory (GB): 6.1
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_480x480_40k_pascal_context/fcn_hr48_480x480_40k_pascal_context_20200911_164852-667d00b0.pth
|
||||
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_480x480_40k_pascal_context/fcn_hr48_480x480_40k_pascal_context-20200911_164852.log.json
|
||||
Paper:
|
||||
Title: Deep High-Resolution Representation Learning for Human Pose Estimation
|
||||
URL: https://arxiv.org/abs/1908.07919
|
||||
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/hrnet.py#L218
|
||||
Framework: PyTorch
|
||||
- Name: fcn_hr48_4xb4-80k_pascal-context-480x480
|
||||
In Collection: FCN
|
||||
Results:
|
||||
Task: Semantic Segmentation
|
||||
Dataset: Pascal Context
|
||||
Metrics:
|
||||
mIoU: 45.84
|
||||
mIoU(ms+flip): 47.84
|
||||
Config: configs/hrnet/fcn_hr48_4xb4-80k_pascal-context-480x480.py
|
||||
Metadata:
|
||||
Training Data: Pascal Context
|
||||
Batch Size: 16
|
||||
Architecture:
|
||||
- HRNetV2p-W48
|
||||
- FCN
|
||||
Training Resources: 4x V100 GPUS
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_480x480_80k_pascal_context/fcn_hr48_480x480_80k_pascal_context_20200911_155322-847a6711.pth
|
||||
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_480x480_80k_pascal_context/fcn_hr48_480x480_80k_pascal_context-20200911_155322.log.json
|
||||
Paper:
|
||||
Title: Deep High-Resolution Representation Learning for Human Pose Estimation
|
||||
URL: https://arxiv.org/abs/1908.07919
|
||||
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/hrnet.py#L218
|
||||
Framework: PyTorch
|
||||
- Name: fcn_hr48_4xb4-40k_pascal-context-59-480x480
|
||||
In Collection: FCN
|
||||
Results:
|
||||
Task: Semantic Segmentation
|
||||
Dataset: Pascal Context 59
|
||||
Metrics:
|
||||
mIoU: 50.33
|
||||
mIoU(ms+flip): 52.83
|
||||
Config: configs/hrnet/fcn_hr48_4xb4-40k_pascal-context-59-480x480.py
|
||||
Metadata:
|
||||
Training Data: Pascal Context 59
|
||||
Batch Size: 16
|
||||
Architecture:
|
||||
- HRNetV2p-W48
|
||||
- FCN
|
||||
Training Resources: 4x V100 GPUS
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_480x480_40k_pascal_context_59/fcn_hr48_480x480_40k_pascal_context_59_20210410_122738-b808b8b2.pth
|
||||
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_480x480_40k_pascal_context_59/fcn_hr48_480x480_40k_pascal_context_59-20210410_122738.log.json
|
||||
Paper:
|
||||
Title: Deep High-Resolution Representation Learning for Human Pose Estimation
|
||||
URL: https://arxiv.org/abs/1908.07919
|
||||
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/hrnet.py#L218
|
||||
Framework: PyTorch
|
||||
- Name: fcn_hr48_4xb4-80k_pascal-context-59-480x480
|
||||
In Collection: FCN
|
||||
Results:
|
||||
Task: Semantic Segmentation
|
||||
Dataset: Pascal Context 59
|
||||
Metrics:
|
||||
mIoU: 51.12
|
||||
mIoU(ms+flip): 53.56
|
||||
Config: configs/hrnet/fcn_hr48_4xb4-80k_pascal-context-59-480x480.py
|
||||
Metadata:
|
||||
Training Data: Pascal Context 59
|
||||
Batch Size: 16
|
||||
Architecture:
|
||||
- HRNetV2p-W48
|
||||
- FCN
|
||||
Training Resources: 4x V100 GPUS
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_480x480_80k_pascal_context_59/fcn_hr48_480x480_80k_pascal_context_59_20210411_003240-3ae7081e.pth
|
||||
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_480x480_80k_pascal_context_59/fcn_hr48_480x480_80k_pascal_context_59-20210411_003240.log.json
|
||||
Paper:
|
||||
Title: Deep High-Resolution Representation Learning for Human Pose Estimation
|
||||
URL: https://arxiv.org/abs/1908.07919
|
||||
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/hrnet.py#L218
|
||||
Framework: PyTorch
|
||||
- Name: fcn_hr18s_4xb4-80k_loveda-512x512
|
||||
In Collection: FCN
|
||||
Results:
|
||||
Task: Semantic Segmentation
|
||||
Dataset: LoveDA
|
||||
Metrics:
|
||||
mIoU: 49.28
|
||||
mIoU(ms+flip): 49.42
|
||||
Config: configs/hrnet/fcn_hr18s_4xb4-80k_loveda-512x512.py
|
||||
Metadata:
|
||||
Training Data: LoveDA
|
||||
Batch Size: 16
|
||||
Architecture:
|
||||
- HRNetV2p-W18-Small
|
||||
- FCN
|
||||
Training Resources: 4x V100 GPUS
|
||||
Memory (GB): 1.59
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_80k_loveda/fcn_hr18s_512x512_80k_loveda_20211210_203228-60a86a7a.pth
|
||||
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_80k_loveda/fcn_hr18s_512x512_80k_loveda_20211210_203228.log.json
|
||||
Paper:
|
||||
Title: Deep High-Resolution Representation Learning for Human Pose Estimation
|
||||
URL: https://arxiv.org/abs/1908.07919
|
||||
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/hrnet.py#L218
|
||||
Framework: PyTorch
|
||||
- Name: fcn_hr18_4xb4-80k_loveda-512x512
|
||||
In Collection: FCN
|
||||
Results:
|
||||
Task: Semantic Segmentation
|
||||
Dataset: LoveDA
|
||||
Metrics:
|
||||
mIoU: 50.81
|
||||
mIoU(ms+flip): 50.95
|
||||
Config: configs/hrnet/fcn_hr18_4xb4-80k_loveda-512x512.py
|
||||
Metadata:
|
||||
Training Data: LoveDA
|
||||
Batch Size: 16
|
||||
Architecture:
|
||||
- HRNetV2p-W18
|
||||
- FCN
|
||||
Training Resources: 4x V100 GPUS
|
||||
Memory (GB): 2.76
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_80k_loveda/fcn_hr18_512x512_80k_loveda_20211210_203952-93d9c3b3.pth
|
||||
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_80k_loveda/fcn_hr18_512x512_80k_loveda_20211210_203952.log.json
|
||||
Paper:
|
||||
Title: Deep High-Resolution Representation Learning for Human Pose Estimation
|
||||
URL: https://arxiv.org/abs/1908.07919
|
||||
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/hrnet.py#L218
|
||||
Framework: PyTorch
|
||||
- Name: fcn_hr48_4xb4-80k_loveda-512x512
|
||||
In Collection: FCN
|
||||
Results:
|
||||
Task: Semantic Segmentation
|
||||
Dataset: LoveDA
|
||||
Metrics:
|
||||
mIoU: 51.42
|
||||
mIoU(ms+flip): 51.64
|
||||
Config: configs/hrnet/fcn_hr48_4xb4-80k_loveda-512x512.py
|
||||
Metadata:
|
||||
Training Data: LoveDA
|
||||
Batch Size: 16
|
||||
Architecture:
|
||||
- HRNetV2p-W48
|
||||
- FCN
|
||||
Training Resources: 4x V100 GPUS
|
||||
Memory (GB): 6.2
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_80k_loveda/fcn_hr48_512x512_80k_loveda_20211211_044756-67072f55.pth
|
||||
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_80k_loveda/fcn_hr48_512x512_80k_loveda_20211211_044756.log.json
|
||||
Paper:
|
||||
Title: Deep High-Resolution Representation Learning for Human Pose Estimation
|
||||
URL: https://arxiv.org/abs/1908.07919
|
||||
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/hrnet.py#L218
|
||||
Framework: PyTorch
|
||||
- Name: fcn_hr18s_4xb4-80k_potsdam-512x512
|
||||
In Collection: FCN
|
||||
Results:
|
||||
Task: Semantic Segmentation
|
||||
Dataset: Potsdam
|
||||
Metrics:
|
||||
mIoU: 77.64
|
||||
mIoU(ms+flip): 78.8
|
||||
Config: configs/hrnet/fcn_hr18s_4xb4-80k_potsdam-512x512.py
|
||||
Metadata:
|
||||
Training Data: Potsdam
|
||||
Batch Size: 16
|
||||
Architecture:
|
||||
- HRNetV2p-W18-Small
|
||||
- FCN
|
||||
Training Resources: 4x V100 GPUS
|
||||
Memory (GB): 1.58
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_80k_potsdam/fcn_hr18s_512x512_80k_potsdam_20211218_205517-ba32af63.pth
|
||||
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_80k_potsdam/fcn_hr18s_512x512_80k_potsdam_20211218_205517.log.json
|
||||
Paper:
|
||||
Title: Deep High-Resolution Representation Learning for Human Pose Estimation
|
||||
URL: https://arxiv.org/abs/1908.07919
|
||||
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/hrnet.py#L218
|
||||
Framework: PyTorch
|
||||
- Name: fcn_hr18_4xb4-80k_potsdam-512x512
|
||||
In Collection: FCN
|
||||
Results:
|
||||
Task: Semantic Segmentation
|
||||
Dataset: Potsdam
|
||||
Metrics:
|
||||
mIoU: 78.26
|
||||
mIoU(ms+flip): 79.24
|
||||
Config: configs/hrnet/fcn_hr18_4xb4-80k_potsdam-512x512.py
|
||||
Metadata:
|
||||
Training Data: Potsdam
|
||||
Batch Size: 16
|
||||
Architecture:
|
||||
- HRNetV2p-W18
|
||||
- FCN
|
||||
Training Resources: 4x V100 GPUS
|
||||
Memory (GB): 2.76
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_80k_potsdam/fcn_hr18_512x512_80k_potsdam_20211218_205517-5d0387ad.pth
|
||||
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_80k_potsdam/fcn_hr18_512x512_80k_potsdam_20211218_205517.log.json
|
||||
Paper:
|
||||
Title: Deep High-Resolution Representation Learning for Human Pose Estimation
|
||||
URL: https://arxiv.org/abs/1908.07919
|
||||
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/hrnet.py#L218
|
||||
Framework: PyTorch
|
||||
- Name: fcn_hr48_4xb4-80k_potsdam-512x512
|
||||
In Collection: FCN
|
||||
Results:
|
||||
Task: Semantic Segmentation
|
||||
Dataset: Potsdam
|
||||
Metrics:
|
||||
mIoU: 78.39
|
||||
mIoU(ms+flip): 79.34
|
||||
Config: configs/hrnet/fcn_hr48_4xb4-80k_potsdam-512x512.py
|
||||
Metadata:
|
||||
Training Data: Potsdam
|
||||
Batch Size: 16
|
||||
Architecture:
|
||||
- HRNetV2p-W48
|
||||
- FCN
|
||||
Training Resources: 4x V100 GPUS
|
||||
Memory (GB): 6.2
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_80k_potsdam/fcn_hr48_512x512_80k_potsdam_20211219_020601-97434c78.pth
|
||||
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_80k_potsdam/fcn_hr48_512x512_80k_potsdam_20211219_020601.log.json
|
||||
Paper:
|
||||
Title: Deep High-Resolution Representation Learning for Human Pose Estimation
|
||||
URL: https://arxiv.org/abs/1908.07919
|
||||
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/hrnet.py#L218
|
||||
Framework: PyTorch
|
||||
- Name: fcn_hr18s_4xb4-80k_vaihingen-512x512
|
||||
In Collection: FCN
|
||||
Results:
|
||||
Task: Semantic Segmentation
|
||||
Dataset: Vaihingen
|
||||
Metrics:
|
||||
mIoU: 71.81
|
||||
mIoU(ms+flip): 73.1
|
||||
Config: configs/hrnet/fcn_hr18s_4xb4-80k_vaihingen-512x512.py
|
||||
Metadata:
|
||||
Training Data: Vaihingen
|
||||
Batch Size: 16
|
||||
Architecture:
|
||||
- HRNetV2p-W18-Small
|
||||
- FCN
|
||||
Training Resources: 4x V100 GPUS
|
||||
Memory (GB): 1.58
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_4x4_512x512_80k_vaihingen/fcn_hr18s_4x4_512x512_80k_vaihingen_20211231_230909-b23aae02.pth
|
||||
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_4x4_512x512_80k_vaihingen/fcn_hr18s_4x4_512x512_80k_vaihingen_20211231_230909.log.json
|
||||
Paper:
|
||||
Title: Deep High-Resolution Representation Learning for Human Pose Estimation
|
||||
URL: https://arxiv.org/abs/1908.07919
|
||||
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/hrnet.py#L218
|
||||
Framework: PyTorch
|
||||
- Name: fcn_hr18_4xb4-80k_vaihingen-512x512
|
||||
In Collection: FCN
|
||||
Results:
|
||||
Task: Semantic Segmentation
|
||||
Dataset: Vaihingen
|
||||
Metrics:
|
||||
mIoU: 72.57
|
||||
mIoU(ms+flip): 74.09
|
||||
Config: configs/hrnet/fcn_hr18_4xb4-80k_vaihingen-512x512.py
|
||||
Metadata:
|
||||
Training Data: Vaihingen
|
||||
Batch Size: 16
|
||||
Architecture:
|
||||
- HRNetV2p-W18
|
||||
- FCN
|
||||
Training Resources: 4x V100 GPUS
|
||||
Memory (GB): 2.76
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_4x4_512x512_80k_vaihingen/fcn_hr18_4x4_512x512_80k_vaihingen_20211231_231216-2ec3ae8a.pth
|
||||
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_4x4_512x512_80k_vaihingen/fcn_hr18_4x4_512x512_80k_vaihingen_20211231_231216.log.json
|
||||
Paper:
|
||||
Title: Deep High-Resolution Representation Learning for Human Pose Estimation
|
||||
URL: https://arxiv.org/abs/1908.07919
|
||||
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/hrnet.py#L218
|
||||
Framework: PyTorch
|
||||
- Name: fcn_hr48_4xb4-80k_vaihingen-512x512
|
||||
In Collection: FCN
|
||||
Results:
|
||||
Task: Semantic Segmentation
|
||||
Dataset: Vaihingen
|
||||
Metrics:
|
||||
mIoU: 72.5
|
||||
mIoU(ms+flip): 73.52
|
||||
Config: configs/hrnet/fcn_hr48_4xb4-80k_vaihingen-512x512.py
|
||||
Metadata:
|
||||
Training Data: Vaihingen
|
||||
Batch Size: 16
|
||||
Architecture:
|
||||
- HRNetV2p-W48
|
||||
- FCN
|
||||
Training Resources: 4x V100 GPUS
|
||||
Memory (GB): 6.2
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_4x4_512x512_80k_vaihingen/fcn_hr48_4x4_512x512_80k_vaihingen_20211231_231244-7133cb22.pth
|
||||
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_4x4_512x512_80k_vaihingen/fcn_hr48_4x4_512x512_80k_vaihingen_20211231_231244.log.json
|
||||
Paper:
|
||||
Title: Deep High-Resolution Representation Learning for Human Pose Estimation
|
||||
URL: https://arxiv.org/abs/1908.07919
|
||||
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/hrnet.py#L218
|
||||
Framework: PyTorch
|
||||
- Name: fcn_hr18s_4xb4-80k_isaid-896x896
|
||||
In Collection: FCN
|
||||
Results:
|
||||
Task: Semantic Segmentation
|
||||
Dataset: iSAID
|
||||
Metrics:
|
||||
mIoU: 62.3
|
||||
mIoU(ms+flip): 62.97
|
||||
Config: configs/hrnet/fcn_hr18s_4xb4-80k_isaid-896x896.py
|
||||
Metadata:
|
||||
Training Data: iSAID
|
||||
Batch Size: 16
|
||||
Architecture:
|
||||
- HRNetV2p-W18-Small
|
||||
- FCN
|
||||
Training Resources: 4x V100 GPUS
|
||||
Memory (GB): 4.95
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_4x4_896x896_80k_isaid/fcn_hr18s_4x4_896x896_80k_isaid_20220118_001603-3cc0769b.pth
|
||||
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_4x4_896x896_80k_isaid/fcn_hr18s_4x4_896x896_80k_isaid_20220118_001603.log.json
|
||||
Paper:
|
||||
Title: Deep High-Resolution Representation Learning for Human Pose Estimation
|
||||
URL: https://arxiv.org/abs/1908.07919
|
||||
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/hrnet.py#L218
|
||||
Framework: PyTorch
|
||||
- Name: fcn_hr18_4xb4-80k_isaid-896x896
|
||||
In Collection: FCN
|
||||
Results:
|
||||
Task: Semantic Segmentation
|
||||
Dataset: iSAID
|
||||
Metrics:
|
||||
mIoU: 65.06
|
||||
mIoU(ms+flip): 65.6
|
||||
Config: configs/hrnet/fcn_hr18_4xb4-80k_isaid-896x896.py
|
||||
Metadata:
|
||||
Training Data: iSAID
|
||||
Batch Size: 16
|
||||
Architecture:
|
||||
- HRNetV2p-W18
|
||||
- FCN
|
||||
Training Resources: 4x V100 GPUS
|
||||
Memory (GB): 8.3
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_4x4_896x896_80k_isaid/fcn_hr18_4x4_896x896_80k_isaid_20220110_182230-49bf752e.pth
|
||||
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_4x4_896x896_80k_isaid/fcn_hr18_4x4_896x896_80k_isaid_20220110_182230.log.json
|
||||
Paper:
|
||||
Title: Deep High-Resolution Representation Learning for Human Pose Estimation
|
||||
URL: https://arxiv.org/abs/1908.07919
|
||||
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/hrnet.py#L218
|
||||
Framework: PyTorch
|
||||
- Name: fcn_hr48_4xb4-80k_isaid-896x896
|
||||
In Collection: FCN
|
||||
Results:
|
||||
Task: Semantic Segmentation
|
||||
Dataset: iSAID
|
||||
Metrics:
|
||||
mIoU: 67.8
|
||||
mIoU(ms+flip): 68.53
|
||||
Config: configs/hrnet/fcn_hr48_4xb4-80k_isaid-896x896.py
|
||||
Metadata:
|
||||
Training Data: iSAID
|
||||
Batch Size: 16
|
||||
Architecture:
|
||||
- HRNetV2p-W48
|
||||
- FCN
|
||||
Training Resources: 4x V100 GPUS
|
||||
Memory (GB): 16.89
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_4x4_896x896_80k_isaid/fcn_hr48_4x4_896x896_80k_isaid_20220114_174643-547fc420.pth
|
||||
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_4x4_896x896_80k_isaid/fcn_hr48_4x4_896x896_80k_isaid_20220114_174643.log.json
|
||||
Paper:
|
||||
Title: Deep High-Resolution Representation Learning for Human Pose Estimation
|
||||
URL: https://arxiv.org/abs/1908.07919
|
||||
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/hrnet.py#L218
|
||||
Framework: PyTorch
|
||||
@@ -0,0 +1,118 @@
|
||||
_base_ = [
|
||||
'../_base_/models/fcn_hr18.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/msra/hrnetv2_w18_small.pth',
|
||||
backbone=dict(
|
||||
extra=dict(
|
||||
stage1=dict(
|
||||
num_blocks=(2,),
|
||||
),
|
||||
stage2=dict(
|
||||
num_blocks=(2, 2),
|
||||
),
|
||||
stage3=dict(
|
||||
num_modules=3,
|
||||
num_blocks=(2, 2, 2),
|
||||
),
|
||||
stage4=dict(
|
||||
num_modules=2,
|
||||
num_blocks=(2, 2, 2, 2),
|
||||
),
|
||||
),
|
||||
),
|
||||
data_preprocessor=dict(
|
||||
size=(512, 1024),
|
||||
mean=[
|
||||
94.94709810464303,
|
||||
61.72942233949928,
|
||||
75.93763705236906,
|
||||
],
|
||||
std=[
|
||||
44.005506081132594,
|
||||
42.69595666984776,
|
||||
44.99354156225523,
|
||||
],
|
||||
bgr_to_rgb=False,
|
||||
),
|
||||
decode_head=dict(
|
||||
num_classes=36,
|
||||
loss_decode=dict(
|
||||
type='DiceLoss',
|
||||
use_sigmoid=False,
|
||||
loss_weight=1.0,
|
||||
),
|
||||
align_corners=True,
|
||||
in_channels=[
|
||||
18,
|
||||
36,
|
||||
72,
|
||||
144,
|
||||
],
|
||||
channels=270,
|
||||
),
|
||||
)
|
||||
|
||||
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 +1,122 @@
|
||||
_base_ = [
|
||||
'../_base_/models/fcn_hr18.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/msra/hrnetv2_w18.pth',
|
||||
backbone=dict(
|
||||
extra=dict(
|
||||
stage1=dict(
|
||||
num_blocks=(4,),
|
||||
num_channels=64,
|
||||
),
|
||||
stage2=dict(
|
||||
num_blocks=(4, 4),
|
||||
num_channels=(18, 36),
|
||||
),
|
||||
stage3=dict(
|
||||
num_modules=4,
|
||||
num_blocks=(4, 4, 4),
|
||||
num_channels=(18, 36, 72),
|
||||
),
|
||||
stage4=dict(
|
||||
num_modules=3,
|
||||
num_blocks=(4, 4, 4, 4),
|
||||
num_channels=(18, 36, 72, 144),
|
||||
),
|
||||
),
|
||||
),
|
||||
data_preprocessor=dict(
|
||||
size=(512, 1024),
|
||||
mean=[
|
||||
94.94709810464303,
|
||||
61.72942233949928,
|
||||
75.93763705236906,
|
||||
],
|
||||
std=[
|
||||
44.005506081132594,
|
||||
42.69595666984776,
|
||||
44.99354156225523,
|
||||
],
|
||||
bgr_to_rgb=False,
|
||||
),
|
||||
decode_head=dict(
|
||||
num_classes=36,
|
||||
loss_decode=dict(
|
||||
type='DiceLoss',
|
||||
use_sigmoid=False,
|
||||
loss_weight=1.0,
|
||||
),
|
||||
align_corners=True,
|
||||
in_channels=[
|
||||
18,
|
||||
36,
|
||||
72,
|
||||
144,
|
||||
],
|
||||
channels=270,
|
||||
),
|
||||
)
|
||||
|
||||
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 +1,113 @@
|
||||
_base_ = [
|
||||
'../_base_/models/fcn_hr18.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/msra/hrnetv2_w48.pth',
|
||||
backbone=dict(
|
||||
extra=dict(
|
||||
stage2=dict(
|
||||
num_channels=(48, 96),
|
||||
),
|
||||
stage3=dict(
|
||||
num_channels=(48, 96, 192),
|
||||
),
|
||||
stage4=dict(
|
||||
num_channels=(48, 96, 192, 384),
|
||||
),
|
||||
),
|
||||
),
|
||||
data_preprocessor=dict(
|
||||
size=(512, 1024),
|
||||
mean=[
|
||||
94.94709810464303,
|
||||
61.72942233949928,
|
||||
75.93763705236906,
|
||||
],
|
||||
std=[
|
||||
44.005506081132594,
|
||||
42.69595666984776,
|
||||
44.99354156225523,
|
||||
],
|
||||
bgr_to_rgb=False,
|
||||
),
|
||||
decode_head=dict(
|
||||
num_classes=36,
|
||||
loss_decode=dict(
|
||||
type='DiceLoss',
|
||||
use_sigmoid=False,
|
||||
loss_weight=1.0,
|
||||
),
|
||||
align_corners=True,
|
||||
in_channels=[
|
||||
48,
|
||||
96,
|
||||
192,
|
||||
384,
|
||||
],
|
||||
channels=720,
|
||||
),
|
||||
)
|
||||
|
||||
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 +1,113 @@
|
||||
_base_ = [
|
||||
'../_base_/models/fcn_hr18.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 = (769, 769)
|
||||
|
||||
data_preprocessor = dict(
|
||||
size=(769, 769),
|
||||
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/msra/hrnetv2_w48.pth',
|
||||
backbone=dict(
|
||||
extra=dict(
|
||||
stage2=dict(
|
||||
num_channels=(48, 96),
|
||||
),
|
||||
stage3=dict(
|
||||
num_channels=(48, 96, 192),
|
||||
),
|
||||
stage4=dict(
|
||||
num_channels=(48, 96, 192, 384),
|
||||
),
|
||||
),
|
||||
),
|
||||
data_preprocessor=dict(
|
||||
size=(769, 769),
|
||||
mean=[
|
||||
94.94709810464303,
|
||||
61.72942233949928,
|
||||
75.93763705236906,
|
||||
],
|
||||
std=[
|
||||
44.005506081132594,
|
||||
42.69595666984776,
|
||||
44.99354156225523,
|
||||
],
|
||||
bgr_to_rgb=False,
|
||||
),
|
||||
decode_head=dict(
|
||||
num_classes=36,
|
||||
loss_decode=dict(
|
||||
type='DiceLoss',
|
||||
use_sigmoid=False,
|
||||
loss_weight=1.0,
|
||||
),
|
||||
align_corners=True,
|
||||
in_channels=[
|
||||
48,
|
||||
96,
|
||||
192,
|
||||
384,
|
||||
],
|
||||
channels=720,
|
||||
),
|
||||
)
|
||||
|
||||
test_cfg = dict(
|
||||
mode='slide',
|
||||
crop_size=(769, 769),
|
||||
stride=(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,
|
||||
),
|
||||
]
|
||||
|
||||
Reference in New Issue
Block a user