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# UNet
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> [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://arxiv.org/abs/1505.04597)
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## Introduction
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<!-- [ALGORITHM] -->
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<a href="http://lmb.informatik.uni-freiburg.de/people/ronneber/u-net">Official Repo</a>
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<a href="https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/unet.py#L225">Code Snippet</a>
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## Abstract
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<!-- [ABSTRACT] -->
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There is large consent that successful training of deep networks requires many thousand annotated training samples. In this paper, we present a network and training strategy that relies on the strong use of data augmentation to use the available annotated samples more efficiently. The architecture consists of a contracting path to capture context and a symmetric expanding path that enables precise localization. We show that such a network can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks. Using the same network trained on transmitted light microscopy images (phase contrast and DIC) we won the ISBI cell tracking challenge 2015 in these categories by a large margin. Moreover, the network is fast. Segmentation of a 512x512 image takes less than a second on a recent GPU. The full implementation (based on Caffe) and the trained networks are available at [this http URL](https://lmb.informatik.uni-freiburg.de/people/ronneber/u-net/).
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<!-- [IMAGE] -->
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<div align=center>
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<img src="https://user-images.githubusercontent.com/24582831/142902977-20fe689d-a147-4d92-9690-dbfde8b68dbe.png" width="70%"/>
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</div>
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## Results and models
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### Cityscapes
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| Method | Backbone | Loss | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device | mIoU | mIoU(ms+flip) | config | download |
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| ---------- | ----------- | ------------- | --------- | ------: | -------- | -------------- | ------ | ----: | ------------: | ------------------------------------------------------------------------------------------------------------------------------ | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| UNet + FCN | UNet-S5-D16 | Cross Entropy | 512x1024 | 160000 | 17.91 | 3.05 | V100 | 69.10 | 71.05 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/unet/unet-s5-d16_fcn_4xb4-160k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_4x4_512x1024_160k_cityscapes/fcn_unet_s5-d16_4x4_512x1024_160k_cityscapes_20211210_145204-6860854e.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_4x4_512x1024_160k_cityscapes/fcn_unet_s5-d16_4x4_512x1024_160k_cityscapes_20211210_145204.log.json) |
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### DRIVE
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| Method | Backbone | Loss | Image Size | Crop Size | Stride | Lr schd | Mem (GB) | Inf time (fps) | Device | mDice | Dice | config | download |
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| ---------------- | ----------- | -------------------- | ---------- | --------- | -----: | ------- | -------- | -------------: | ------ | ----: | ----: | ------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| UNet + FCN | UNet-S5-D16 | Cross Entropy | 584x565 | 64x64 | 42x42 | 40000 | 0.680 | - | V100 | 88.38 | 78.67 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/unet/unet-s5-d16_fcn_4xb4-40k_drive-64x64.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_64x64_40k_drive/fcn_unet_s5-d16_64x64_40k_drive_20201223_191051-5daf6d3b.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/unet_s5-d16_64x64_40k_drive/unet_s5-d16_64x64_40k_drive-20201223_191051.log.json) |
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| UNet + FCN | UNet-S5-D16 | Cross Entropy + Dice | 584x565 | 64x64 | 42x42 | 40000 | 0.582 | - | V100 | 88.71 | 79.32 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/unet/unet-s5-d16_fcn_4xb4-ce-1.0-dice-3.0-40k_drive-64x64.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive/fcn_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive_20211210_201820-785de5c2.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive/fcn_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive_20211210_201820.log.json) |
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| UNet + PSPNet | UNet-S5-D16 | Cross Entropy | 584x565 | 64x64 | 42x42 | 40000 | 0.599 | - | V100 | 88.35 | 78.62 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/unet/unet-s5-d16_pspnet_4xb4-40k_drive-64x64.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_64x64_40k_drive/pspnet_unet_s5-d16_64x64_40k_drive_20201227_181818-aac73387.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_64x64_40k_drive/pspnet_unet_s5-d16_64x64_40k_drive-20201227_181818.log.json) |
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| UNet + PSPNet | UNet-S5-D16 | Cross Entropy + Dice | 584x565 | 64x64 | 42x42 | 40000 | 0.585 | - | V100 | 88.76 | 79.42 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/unet/unet-s5-d16_pspnet_4xb4-ce-1.0-dice-3.0-40k_drive-64x64.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive/pspnet_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive_20211210_201821-22b3e3ba.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive/pspnet_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive_20211210_201821.log.json) |
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| UNet + DeepLabV3 | UNet-S5-D16 | Cross Entropy | 584x565 | 64x64 | 42x42 | 40000 | 0.596 | - | V100 | 88.38 | 78.69 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/unet/unet-s5-d16_deeplabv3_4xb4-40k_drive-64x64.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_64x64_40k_drive/deeplabv3_unet_s5-d16_64x64_40k_drive_20201226_094047-0671ff20.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_64x64_40k_drive/deeplabv3_unet_s5-d16_64x64_40k_drive-20201226_094047.log.json) |
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| UNet + DeepLabV3 | UNet-S5-D16 | Cross Entropy + Dice | 584x565 | 64x64 | 42x42 | 40000 | 0.582 | - | V100 | 88.84 | 79.56 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/unet/unet-s5-d16_deeplabv3_4xb4-ce-1.0-dice-3.0-40k_drive-64x64.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive_20211210_201825-6bf0efd7.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive_20211210_201825.log.json) |
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### STARE
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| Method | Backbone | Loss | Image Size | Crop Size | Stride | Lr schd | Mem (GB) | Inf time (fps) | Device | mDice | Dice | config | download |
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| ---------------- | ----------- | -------------------- | ---------- | --------- | -----: | ------- | -------- | -------------: | ------ | ----: | ----: | --------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
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| UNet + FCN | UNet-S5-D16 | Cross Entropy | 605x700 | 128x128 | 85x85 | 40000 | 0.968 | - | V100 | 89.78 | 81.02 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/unet/unet-s5-d16_fcn_4xb4-40k_stare-128x128.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_128x128_40k_stare/fcn_unet_s5-d16_128x128_40k_stare_20201223_191051-7d77e78b.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/unet_s5-d16_128x128_40k_stare/unet_s5-d16_128x128_40k_stare-20201223_191051.log.json) |
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| UNet + FCN | UNet-S5-D16 | Cross Entropy + Dice | 605x700 | 128x128 | 85x85 | 40000 | 0.986 | - | V100 | 90.65 | 82.70 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/unet/unet-s5-d16_fcn_4xb4-ce-1.0-dice-3.0-40k_stare-128x128.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare_20211210_201821-f75705a9.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare_20211210_201821.log.json) |
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| UNet + PSPNet | UNet-S5-D16 | Cross Entropy | 605x700 | 128x128 | 85x85 | 40000 | 0.982 | - | V100 | 89.89 | 81.22 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/unet/unet-s5-d16_pspnet_4xb4-40k_stare-128x128.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_128x128_40k_stare/pspnet_unet_s5-d16_128x128_40k_stare_20201227_181818-3c2923c4.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_128x128_40k_stare/pspnet_unet_s5-d16_128x128_40k_stare-20201227_181818.log.json) |
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| UNet + PSPNet | UNet-S5-D16 | Cross Entropy + Dice | 605x700 | 128x128 | 85x85 | 40000 | 1.028 | - | V100 | 90.72 | 82.84 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/unet/unet-s5-d16_pspnet_4xb4-ce-1.0-dice-3.0-40k_stare-128x128.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare_20211210_201823-f1063ef7.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare_20211210_201823.log.json) |
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| UNet + DeepLabV3 | UNet-S5-D16 | Cross Entropy | 605x700 | 128x128 | 85x85 | 40000 | 0.999 | - | V100 | 89.73 | 80.93 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/unet/unet-s5-d16_deeplabv3_4xb4-40k_stare-128x128.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_128x128_40k_stare/deeplabv3_unet_s5-d16_128x128_40k_stare_20201226_094047-93dcb93c.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_128x128_40k_stare/deeplabv3_unet_s5-d16_128x128_40k_stare-20201226_094047.log.json) |
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| UNet + DeepLabV3 | UNet-S5-D16 | Cross Entropy + Dice | 605x700 | 128x128 | 85x85 | 40000 | 1.010 | - | V100 | 90.65 | 82.71 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/unet/unet-s5-d16_deeplabv3_4xb4-ce-1.0-dice-3.0-40k_stare-128x128.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare_20211210_201825-21db614c.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare_20211210_201825.log.json) |
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### CHASE_DB1
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| Method | Backbone | Loss | Image Size | Crop Size | Stride | Lr schd | Mem (GB) | Inf time (fps) | Device | mDice | Dice | config | download |
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| ---------------- | ----------- | -------------------- | ---------- | --------- | -----: | ------- | -------- | -------------: | ------ | ----: | ----: | ------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| UNet + FCN | UNet-S5-D16 | Cross Entropy | 960x999 | 128x128 | 85x85 | 40000 | 0.968 | - | V100 | 89.46 | 80.24 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/unet/unet-s5-d16_fcn_4xb4-40k_chase-db1-128x128.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_128x128_40k_chase_db1/fcn_unet_s5-d16_128x128_40k_chase_db1_20201223_191051-11543527.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/unet_s5-d16_128x128_40k_chase_db1/unet_s5-d16_128x128_40k_chase_db1-20201223_191051.log.json) |
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| UNet + FCN | UNet-S5-D16 | Cross Entropy + Dice | 960x999 | 128x128 | 85x85 | 40000 | 0.986 | - | V100 | 89.52 | 80.40 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/unet/unet-s5-d16_fcn_4xb4-ce-1.0-dice-3.0-40k_chase-db1-128x128.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1_20211210_201821-1c4eb7cf.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1_20211210_201821.log.json) |
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| UNet + PSPNet | UNet-S5-D16 | Cross Entropy | 960x999 | 128x128 | 85x85 | 40000 | 0.982 | - | V100 | 89.52 | 80.36 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/unet/unet-s5-d16_pspnet_4xb4-40k_chase-db1-128x128.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_128x128_40k_chase_db1/pspnet_unet_s5-d16_128x128_40k_chase_db1_20201227_181818-68d4e609.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_128x128_40k_chase_db1/pspnet_unet_s5-d16_128x128_40k_chase_db1-20201227_181818.log.json) |
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| UNet + PSPNet | UNet-S5-D16 | Cross Entropy + Dice | 960x999 | 128x128 | 85x85 | 40000 | 1.028 | - | V100 | 89.45 | 80.28 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/unet/unet-s5-d16_pspnet_4xb4-ce-1.0-dice-3.0-40k_chase-db1-128x128.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1_20211210_201823-c0802c4d.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1_20211210_201823.log.json) |
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| UNet + DeepLabV3 | UNet-S5-D16 | Cross Entropy | 960x999 | 128x128 | 85x85 | 40000 | 0.999 | - | V100 | 89.57 | 80.47 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/unet/unet_s5-d16_deeplabv3_4xb4-40k_chase-db1-128x128.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_128x128_40k_chase_db1/deeplabv3_unet_s5-d16_128x128_40k_chase_db1_20201226_094047-4c5aefa3.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_128x128_40k_chase_db1/deeplabv3_unet_s5-d16_128x128_40k_chase_db1-20201226_094047.log.json) |
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| UNet + DeepLabV3 | UNet-S5-D16 | Cross Entropy + Dice | 960x999 | 128x128 | 85x85 | 40000 | 1.010 | - | V100 | 89.49 | 80.37 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/unet/unet-s5-d16_deeplabv3_4xb4-ce-1.0-dice-3.0-40k_chase-db1-128x128.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1_20211210_201825-4ef29df5.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1_20211210_201825.log.json) |
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### HRF
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| Method | Backbone | Loss | Image Size | Crop Size | Stride | Lr schd | Mem (GB) | Inf time (fps) | Device | mDice | Dice | config | download |
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| ---------------- | ----------- | -------------------- | ---------- | --------- | ------: | ------- | -------- | -------------: | ------ | ----: | ----: | ------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| UNet + FCN | UNet-S5-D16 | Cross Entropy | 2336x3504 | 256x256 | 170x170 | 40000 | 2.525 | - | V100 | 88.92 | 79.45 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/unet/unet-s5-d16_fcn_4xb4-40k_hrf-256x256.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_256x256_40k_hrf/fcn_unet_s5-d16_256x256_40k_hrf_20201223_173724-d89cf1ed.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/unet_s5-d16_256x256_40k_hrf/unet_s5-d16_256x256_40k_hrf-20201223_173724.log.json) |
|
||||
| UNet + FCN | UNet-S5-D16 | Cross Entropy + Dice | 2336x3504 | 256x256 | 170x170 | 40000 | 2.623 | - | V100 | 89.64 | 80.87 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/unet/unet-s5-d16_fcn_4xb4-ce-1.0-dice-3.0-40k_hrf-256x256.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf/fcn_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf_20211210_201821-c314da8a.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf/fcn_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf_20211210_201821.log.json) |
|
||||
| UNet + PSPNet | UNet-S5-D16 | Cross Entropy | 2336x3504 | 256x256 | 170x170 | 40000 | 2.588 | - | V100 | 89.24 | 80.07 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/unet/unet-s5-d16_pspnet_4xb4-40k_hrf-256x256.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_256x256_40k_hrf/pspnet_unet_s5-d16_256x256_40k_hrf_20201227_181818-fdb7e29b.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_256x256_40k_hrf/pspnet_unet_s5-d16_256x256_40k_hrf-20201227_181818.log.json) |
|
||||
| UNet + PSPNet | UNet-S5-D16 | Cross Entropy + Dice | 2336x3504 | 256x256 | 170x170 | 40000 | 2.798 | - | V100 | 89.69 | 80.96 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/unet/unet-s5-d16_pspnet_4xb4-ce-1.0-dice-3.0-40k_hrf-256x256.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf/pspnet_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf_20211210_201823-53d492fa.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf/pspnet_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf_20211210_201823.log.json) |
|
||||
| UNet + DeepLabV3 | UNet-S5-D16 | Cross Entropy | 2336x3504 | 256x256 | 170x170 | 40000 | 2.604 | - | V100 | 89.32 | 80.21 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/unet/unet-s5-d16_deeplabv3_4xb4-40k_hrf-256x256.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_256x256_40k_hrf/deeplabv3_unet_s5-d16_256x256_40k_hrf_20201226_094047-3a1fdf85.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_256x256_40k_hrf/deeplabv3_unet_s5-d16_256x256_40k_hrf-20201226_094047.log.json) |
|
||||
| UNet + DeepLabV3 | UNet-S5-D16 | Cross Entropy + Dice | 2336x3504 | 256x256 | 170x170 | 40000 | 2.607 | - | V100 | 89.56 | 80.71 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/unet/unet-s5-d16_deeplabv3_4xb4-ce-1.0-dice-3.0-40k_hrf-256x256.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf_20211210_202032-59daf7a4.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf_20211210_202032.log.json) |
|
||||
|
||||
Note:
|
||||
|
||||
- In `DRIVE`, `STARE`, `CHASE_DB1`, and `HRF` dataset, `mDice` is mean dice of background and vessel, while `Dice` is dice metric of vessel(foreground) only.
|
||||
|
||||
## Citation
|
||||
|
||||
```bibtex
|
||||
@inproceedings{ronneberger2015u,
|
||||
title={U-net: Convolutional networks for biomedical image segmentation},
|
||||
author={Ronneberger, Olaf and Fischer, Philipp and Brox, Thomas},
|
||||
booktitle={International Conference on Medical image computing and computer-assisted intervention},
|
||||
pages={234--241},
|
||||
year={2015},
|
||||
organization={Springer}
|
||||
}
|
||||
```
|
||||
642
Seg_All_In_One_MMSeg/configs/unet/metafile.yaml
Normal file
642
Seg_All_In_One_MMSeg/configs/unet/metafile.yaml
Normal file
@@ -0,0 +1,642 @@
|
||||
Collections:
|
||||
- Name: UNet
|
||||
License: Apache License 2.0
|
||||
Metadata:
|
||||
Training Data:
|
||||
- Cityscapes
|
||||
- DRIVE
|
||||
- STARE
|
||||
- CHASE_DB1
|
||||
- HRF
|
||||
Paper:
|
||||
Title: 'U-Net: Convolutional Networks for Biomedical Image Segmentation'
|
||||
URL: https://arxiv.org/abs/1505.04597
|
||||
README: configs/unet/README.md
|
||||
Frameworks:
|
||||
- PyTorch
|
||||
Models:
|
||||
- Name: unet-s5-d16_fcn_4xb4-160k_cityscapes-512x1024
|
||||
In Collection: UNet
|
||||
Results:
|
||||
Task: Semantic Segmentation
|
||||
Dataset: Cityscapes
|
||||
Metrics:
|
||||
mIoU: 69.1
|
||||
mIoU(ms+flip): 71.05
|
||||
Config: configs/unet/unet-s5-d16_fcn_4xb4-160k_cityscapes-512x1024.py
|
||||
Metadata:
|
||||
Training Data: Cityscapes
|
||||
Batch Size: 16
|
||||
Architecture:
|
||||
- UNet-S5-D16
|
||||
- UNet
|
||||
- FCN
|
||||
Training Resources: 4x V100 GPUS
|
||||
Memory (GB): 17.91
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_4x4_512x1024_160k_cityscapes/fcn_unet_s5-d16_4x4_512x1024_160k_cityscapes_20211210_145204-6860854e.pth
|
||||
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_4x4_512x1024_160k_cityscapes/fcn_unet_s5-d16_4x4_512x1024_160k_cityscapes_20211210_145204.log.json
|
||||
Paper:
|
||||
Title: 'U-Net: Convolutional Networks for Biomedical Image Segmentation'
|
||||
URL: https://arxiv.org/abs/1505.04597
|
||||
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/unet.py#L225
|
||||
Framework: PyTorch
|
||||
- Name: unet-s5-d16_fcn_4xb4-40k_drive-64x64
|
||||
In Collection: UNet
|
||||
Results:
|
||||
Task: Semantic Segmentation
|
||||
Dataset: DRIVE
|
||||
Metrics:
|
||||
mDice: 88.38
|
||||
Dice: 78.67
|
||||
Config: configs/unet/unet-s5-d16_fcn_4xb4-40k_drive-64x64.py
|
||||
Metadata:
|
||||
Training Data: DRIVE
|
||||
Batch Size: 16
|
||||
Architecture:
|
||||
- UNet-S5-D16
|
||||
- UNet
|
||||
- FCN
|
||||
Training Resources: 4x V100 GPUS
|
||||
Memory (GB): 0.68
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_64x64_40k_drive/fcn_unet_s5-d16_64x64_40k_drive_20201223_191051-5daf6d3b.pth
|
||||
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/unet/unet_s5-d16_64x64_40k_drive/unet_s5-d16_64x64_40k_drive-20201223_191051.log.json
|
||||
Paper:
|
||||
Title: 'U-Net: Convolutional Networks for Biomedical Image Segmentation'
|
||||
URL: https://arxiv.org/abs/1505.04597
|
||||
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/unet.py#L225
|
||||
Framework: PyTorch
|
||||
- Name: unet-s5-d16_fcn_4xb4-ce-1.0-dice-3.0-40k_drive-64x64
|
||||
In Collection: UNet
|
||||
Results:
|
||||
Task: Semantic Segmentation
|
||||
Dataset: DRIVE
|
||||
Metrics:
|
||||
mDice: 88.71
|
||||
Dice: 79.32
|
||||
Config: configs/unet/unet-s5-d16_fcn_4xb4-ce-1.0-dice-3.0-40k_drive-64x64.py
|
||||
Metadata:
|
||||
Training Data: DRIVE
|
||||
Batch Size: 16
|
||||
Architecture:
|
||||
- UNet-S5-D16
|
||||
- UNet
|
||||
- FCN
|
||||
Training Resources: 4x V100 GPUS
|
||||
Memory (GB): 0.582
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive/fcn_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive_20211210_201820-785de5c2.pth
|
||||
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive/fcn_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive_20211210_201820.log.json
|
||||
Paper:
|
||||
Title: 'U-Net: Convolutional Networks for Biomedical Image Segmentation'
|
||||
URL: https://arxiv.org/abs/1505.04597
|
||||
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/unet.py#L225
|
||||
Framework: PyTorch
|
||||
- Name: unet-s5-d16_pspnet_4xb4-40k_drive-64x64
|
||||
In Collection: UNet
|
||||
Results:
|
||||
Task: Semantic Segmentation
|
||||
Dataset: DRIVE
|
||||
Metrics:
|
||||
mDice: 88.35
|
||||
Dice: 78.62
|
||||
Config: configs/unet/unet-s5-d16_pspnet_4xb4-40k_drive-64x64.py
|
||||
Metadata:
|
||||
Training Data: DRIVE
|
||||
Batch Size: 16
|
||||
Architecture:
|
||||
- UNet-S5-D16
|
||||
- UNet
|
||||
- PSPNet
|
||||
Training Resources: 4x V100 GPUS
|
||||
Memory (GB): 0.599
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_64x64_40k_drive/pspnet_unet_s5-d16_64x64_40k_drive_20201227_181818-aac73387.pth
|
||||
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_64x64_40k_drive/pspnet_unet_s5-d16_64x64_40k_drive-20201227_181818.log.json
|
||||
Paper:
|
||||
Title: 'U-Net: Convolutional Networks for Biomedical Image Segmentation'
|
||||
URL: https://arxiv.org/abs/1505.04597
|
||||
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/unet.py#L225
|
||||
Framework: PyTorch
|
||||
- Name: unet-s5-d16_pspnet_4xb4-ce-1.0-dice-3.0-40k_drive-64x64
|
||||
In Collection: UNet
|
||||
Results:
|
||||
Task: Semantic Segmentation
|
||||
Dataset: DRIVE
|
||||
Metrics:
|
||||
mDice: 88.76
|
||||
Dice: 79.42
|
||||
Config: configs/unet/unet-s5-d16_pspnet_4xb4-ce-1.0-dice-3.0-40k_drive-64x64.py
|
||||
Metadata:
|
||||
Training Data: DRIVE
|
||||
Batch Size: 16
|
||||
Architecture:
|
||||
- UNet-S5-D16
|
||||
- UNet
|
||||
- PSPNet
|
||||
Training Resources: 4x V100 GPUS
|
||||
Memory (GB): 0.585
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive/pspnet_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive_20211210_201821-22b3e3ba.pth
|
||||
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive/pspnet_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive_20211210_201821.log.json
|
||||
Paper:
|
||||
Title: 'U-Net: Convolutional Networks for Biomedical Image Segmentation'
|
||||
URL: https://arxiv.org/abs/1505.04597
|
||||
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/unet.py#L225
|
||||
Framework: PyTorch
|
||||
- Name: unet-s5-d16_deeplabv3_4xb4-40k_drive-64x64
|
||||
In Collection: UNet
|
||||
Results:
|
||||
Task: Semantic Segmentation
|
||||
Dataset: DRIVE
|
||||
Metrics:
|
||||
mDice: 88.38
|
||||
Dice: 78.69
|
||||
Config: configs/unet/unet-s5-d16_deeplabv3_4xb4-40k_drive-64x64.py
|
||||
Metadata:
|
||||
Training Data: DRIVE
|
||||
Batch Size: 16
|
||||
Architecture:
|
||||
- UNet-S5-D16
|
||||
- UNet
|
||||
- DeepLabV3
|
||||
Training Resources: 4x V100 GPUS
|
||||
Memory (GB): 0.596
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_64x64_40k_drive/deeplabv3_unet_s5-d16_64x64_40k_drive_20201226_094047-0671ff20.pth
|
||||
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_64x64_40k_drive/deeplabv3_unet_s5-d16_64x64_40k_drive-20201226_094047.log.json
|
||||
Paper:
|
||||
Title: 'U-Net: Convolutional Networks for Biomedical Image Segmentation'
|
||||
URL: https://arxiv.org/abs/1505.04597
|
||||
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/unet.py#L225
|
||||
Framework: PyTorch
|
||||
- Name: unet-s5-d16_deeplabv3_4xb4-ce-1.0-dice-3.0-40k_drive-64x64
|
||||
In Collection: UNet
|
||||
Results:
|
||||
Task: Semantic Segmentation
|
||||
Dataset: DRIVE
|
||||
Metrics:
|
||||
mDice: 88.84
|
||||
Dice: 79.56
|
||||
Config: configs/unet/unet-s5-d16_deeplabv3_4xb4-ce-1.0-dice-3.0-40k_drive-64x64.py
|
||||
Metadata:
|
||||
Training Data: DRIVE
|
||||
Batch Size: 16
|
||||
Architecture:
|
||||
- UNet-S5-D16
|
||||
- UNet
|
||||
- DeepLabV3
|
||||
Training Resources: 4x V100 GPUS
|
||||
Memory (GB): 0.582
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive_20211210_201825-6bf0efd7.pth
|
||||
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive_20211210_201825.log.json
|
||||
Paper:
|
||||
Title: 'U-Net: Convolutional Networks for Biomedical Image Segmentation'
|
||||
URL: https://arxiv.org/abs/1505.04597
|
||||
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/unet.py#L225
|
||||
Framework: PyTorch
|
||||
- Name: unet-s5-d16_fcn_4xb4-40k_stare-128x128
|
||||
In Collection: UNet
|
||||
Results:
|
||||
Task: Semantic Segmentation
|
||||
Dataset: STARE
|
||||
Metrics:
|
||||
mDice: 89.78
|
||||
Dice: 81.02
|
||||
Config: configs/unet/unet-s5-d16_fcn_4xb4-40k_stare-128x128.py
|
||||
Metadata:
|
||||
Training Data: STARE
|
||||
Batch Size: 16
|
||||
Architecture:
|
||||
- UNet-S5-D16
|
||||
- UNet
|
||||
- FCN
|
||||
Training Resources: 4x V100 GPUS
|
||||
Memory (GB): 0.968
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_128x128_40k_stare/fcn_unet_s5-d16_128x128_40k_stare_20201223_191051-7d77e78b.pth
|
||||
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/unet/unet_s5-d16_128x128_40k_stare/unet_s5-d16_128x128_40k_stare-20201223_191051.log.json
|
||||
Paper:
|
||||
Title: 'U-Net: Convolutional Networks for Biomedical Image Segmentation'
|
||||
URL: https://arxiv.org/abs/1505.04597
|
||||
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/unet.py#L225
|
||||
Framework: PyTorch
|
||||
- Name: unet-s5-d16_fcn_4xb4-ce-1.0-dice-3.0-40k_stare-128x128
|
||||
In Collection: UNet
|
||||
Results:
|
||||
Task: Semantic Segmentation
|
||||
Dataset: STARE
|
||||
Metrics:
|
||||
mDice: 90.65
|
||||
Dice: 82.7
|
||||
Config: configs/unet/unet-s5-d16_fcn_4xb4-ce-1.0-dice-3.0-40k_stare-128x128.py
|
||||
Metadata:
|
||||
Training Data: STARE
|
||||
Batch Size: 16
|
||||
Architecture:
|
||||
- UNet-S5-D16
|
||||
- UNet
|
||||
- FCN
|
||||
Training Resources: 4x V100 GPUS
|
||||
Memory (GB): 0.986
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare_20211210_201821-f75705a9.pth
|
||||
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare_20211210_201821.log.json
|
||||
Paper:
|
||||
Title: 'U-Net: Convolutional Networks for Biomedical Image Segmentation'
|
||||
URL: https://arxiv.org/abs/1505.04597
|
||||
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/unet.py#L225
|
||||
Framework: PyTorch
|
||||
- Name: unet-s5-d16_pspnet_4xb4-40k_stare-128x128
|
||||
In Collection: UNet
|
||||
Results:
|
||||
Task: Semantic Segmentation
|
||||
Dataset: STARE
|
||||
Metrics:
|
||||
mDice: 89.89
|
||||
Dice: 81.22
|
||||
Config: configs/unet/unet-s5-d16_pspnet_4xb4-40k_stare-128x128.py
|
||||
Metadata:
|
||||
Training Data: STARE
|
||||
Batch Size: 16
|
||||
Architecture:
|
||||
- UNet-S5-D16
|
||||
- UNet
|
||||
- PSPNet
|
||||
Training Resources: 4x V100 GPUS
|
||||
Memory (GB): 0.982
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_128x128_40k_stare/pspnet_unet_s5-d16_128x128_40k_stare_20201227_181818-3c2923c4.pth
|
||||
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_128x128_40k_stare/pspnet_unet_s5-d16_128x128_40k_stare-20201227_181818.log.json
|
||||
Paper:
|
||||
Title: 'U-Net: Convolutional Networks for Biomedical Image Segmentation'
|
||||
URL: https://arxiv.org/abs/1505.04597
|
||||
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/unet.py#L225
|
||||
Framework: PyTorch
|
||||
- Name: unet-s5-d16_pspnet_4xb4-ce-1.0-dice-3.0-40k_stare-128x128
|
||||
In Collection: UNet
|
||||
Results:
|
||||
Task: Semantic Segmentation
|
||||
Dataset: STARE
|
||||
Metrics:
|
||||
mDice: 90.72
|
||||
Dice: 82.84
|
||||
Config: configs/unet/unet-s5-d16_pspnet_4xb4-ce-1.0-dice-3.0-40k_stare-128x128.py
|
||||
Metadata:
|
||||
Training Data: STARE
|
||||
Batch Size: 16
|
||||
Architecture:
|
||||
- UNet-S5-D16
|
||||
- UNet
|
||||
- PSPNet
|
||||
Training Resources: 4x V100 GPUS
|
||||
Memory (GB): 1.028
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare_20211210_201823-f1063ef7.pth
|
||||
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare_20211210_201823.log.json
|
||||
Paper:
|
||||
Title: 'U-Net: Convolutional Networks for Biomedical Image Segmentation'
|
||||
URL: https://arxiv.org/abs/1505.04597
|
||||
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/unet.py#L225
|
||||
Framework: PyTorch
|
||||
- Name: unet-s5-d16_deeplabv3_4xb4-40k_stare-128x128
|
||||
In Collection: UNet
|
||||
Results:
|
||||
Task: Semantic Segmentation
|
||||
Dataset: STARE
|
||||
Metrics:
|
||||
mDice: 89.73
|
||||
Dice: 80.93
|
||||
Config: configs/unet/unet-s5-d16_deeplabv3_4xb4-40k_stare-128x128.py
|
||||
Metadata:
|
||||
Training Data: STARE
|
||||
Batch Size: 16
|
||||
Architecture:
|
||||
- UNet-S5-D16
|
||||
- UNet
|
||||
- DeepLabV3
|
||||
Training Resources: 4x V100 GPUS
|
||||
Memory (GB): 0.999
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_128x128_40k_stare/deeplabv3_unet_s5-d16_128x128_40k_stare_20201226_094047-93dcb93c.pth
|
||||
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_128x128_40k_stare/deeplabv3_unet_s5-d16_128x128_40k_stare-20201226_094047.log.json
|
||||
Paper:
|
||||
Title: 'U-Net: Convolutional Networks for Biomedical Image Segmentation'
|
||||
URL: https://arxiv.org/abs/1505.04597
|
||||
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/unet.py#L225
|
||||
Framework: PyTorch
|
||||
- Name: unet-s5-d16_deeplabv3_4xb4-ce-1.0-dice-3.0-40k_stare-128x128
|
||||
In Collection: UNet
|
||||
Results:
|
||||
Task: Semantic Segmentation
|
||||
Dataset: STARE
|
||||
Metrics:
|
||||
mDice: 90.65
|
||||
Dice: 82.71
|
||||
Config: configs/unet/unet-s5-d16_deeplabv3_4xb4-ce-1.0-dice-3.0-40k_stare-128x128.py
|
||||
Metadata:
|
||||
Training Data: STARE
|
||||
Batch Size: 16
|
||||
Architecture:
|
||||
- UNet-S5-D16
|
||||
- UNet
|
||||
- DeepLabV3
|
||||
Training Resources: 4x V100 GPUS
|
||||
Memory (GB): 1.01
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare_20211210_201825-21db614c.pth
|
||||
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare_20211210_201825.log.json
|
||||
Paper:
|
||||
Title: 'U-Net: Convolutional Networks for Biomedical Image Segmentation'
|
||||
URL: https://arxiv.org/abs/1505.04597
|
||||
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/unet.py#L225
|
||||
Framework: PyTorch
|
||||
- Name: unet-s5-d16_fcn_4xb4-40k_chase-db1-128x128
|
||||
In Collection: UNet
|
||||
Results:
|
||||
Task: Semantic Segmentation
|
||||
Dataset: CHASE_DB1
|
||||
Metrics:
|
||||
mDice: 89.46
|
||||
Dice: 80.24
|
||||
Config: configs/unet/unet-s5-d16_fcn_4xb4-40k_chase-db1-128x128.py
|
||||
Metadata:
|
||||
Training Data: CHASE_DB1
|
||||
Batch Size: 16
|
||||
Architecture:
|
||||
- UNet-S5-D16
|
||||
- UNet
|
||||
- FCN
|
||||
Training Resources: 4x V100 GPUS
|
||||
Memory (GB): 0.968
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_128x128_40k_chase_db1/fcn_unet_s5-d16_128x128_40k_chase_db1_20201223_191051-11543527.pth
|
||||
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/unet/unet_s5-d16_128x128_40k_chase_db1/unet_s5-d16_128x128_40k_chase_db1-20201223_191051.log.json
|
||||
Paper:
|
||||
Title: 'U-Net: Convolutional Networks for Biomedical Image Segmentation'
|
||||
URL: https://arxiv.org/abs/1505.04597
|
||||
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/unet.py#L225
|
||||
Framework: PyTorch
|
||||
- Name: unet-s5-d16_fcn_4xb4-ce-1.0-dice-3.0-40k_chase-db1-128x128
|
||||
In Collection: UNet
|
||||
Results:
|
||||
Task: Semantic Segmentation
|
||||
Dataset: CHASE_DB1
|
||||
Metrics:
|
||||
mDice: 89.52
|
||||
Dice: 80.4
|
||||
Config: configs/unet/unet-s5-d16_fcn_4xb4-ce-1.0-dice-3.0-40k_chase-db1-128x128.py
|
||||
Metadata:
|
||||
Training Data: CHASE_DB1
|
||||
Batch Size: 16
|
||||
Architecture:
|
||||
- UNet-S5-D16
|
||||
- UNet
|
||||
- FCN
|
||||
Training Resources: 4x V100 GPUS
|
||||
Memory (GB): 0.986
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1_20211210_201821-1c4eb7cf.pth
|
||||
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1_20211210_201821.log.json
|
||||
Paper:
|
||||
Title: 'U-Net: Convolutional Networks for Biomedical Image Segmentation'
|
||||
URL: https://arxiv.org/abs/1505.04597
|
||||
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/unet.py#L225
|
||||
Framework: PyTorch
|
||||
- Name: unet-s5-d16_pspnet_4xb4-40k_chase-db1-128x128
|
||||
In Collection: UNet
|
||||
Results:
|
||||
Task: Semantic Segmentation
|
||||
Dataset: CHASE_DB1
|
||||
Metrics:
|
||||
mDice: 89.52
|
||||
Dice: 80.36
|
||||
Config: configs/unet/unet-s5-d16_pspnet_4xb4-40k_chase-db1-128x128.py
|
||||
Metadata:
|
||||
Training Data: CHASE_DB1
|
||||
Batch Size: 16
|
||||
Architecture:
|
||||
- UNet-S5-D16
|
||||
- UNet
|
||||
- PSPNet
|
||||
Training Resources: 4x V100 GPUS
|
||||
Memory (GB): 0.982
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_128x128_40k_chase_db1/pspnet_unet_s5-d16_128x128_40k_chase_db1_20201227_181818-68d4e609.pth
|
||||
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_128x128_40k_chase_db1/pspnet_unet_s5-d16_128x128_40k_chase_db1-20201227_181818.log.json
|
||||
Paper:
|
||||
Title: 'U-Net: Convolutional Networks for Biomedical Image Segmentation'
|
||||
URL: https://arxiv.org/abs/1505.04597
|
||||
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/unet.py#L225
|
||||
Framework: PyTorch
|
||||
- Name: unet-s5-d16_pspnet_4xb4-ce-1.0-dice-3.0-40k_chase-db1-128x128
|
||||
In Collection: UNet
|
||||
Results:
|
||||
Task: Semantic Segmentation
|
||||
Dataset: CHASE_DB1
|
||||
Metrics:
|
||||
mDice: 89.45
|
||||
Dice: 80.28
|
||||
Config: configs/unet/unet-s5-d16_pspnet_4xb4-ce-1.0-dice-3.0-40k_chase-db1-128x128.py
|
||||
Metadata:
|
||||
Training Data: CHASE_DB1
|
||||
Batch Size: 16
|
||||
Architecture:
|
||||
- UNet-S5-D16
|
||||
- UNet
|
||||
- PSPNet
|
||||
Training Resources: 4x V100 GPUS
|
||||
Memory (GB): 1.028
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1_20211210_201823-c0802c4d.pth
|
||||
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1_20211210_201823.log.json
|
||||
Paper:
|
||||
Title: 'U-Net: Convolutional Networks for Biomedical Image Segmentation'
|
||||
URL: https://arxiv.org/abs/1505.04597
|
||||
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/unet.py#L225
|
||||
Framework: PyTorch
|
||||
- Name: unet_s5-d16_deeplabv3_4xb4-40k_chase-db1-128x128
|
||||
In Collection: UNet
|
||||
Results:
|
||||
Task: Semantic Segmentation
|
||||
Dataset: CHASE_DB1
|
||||
Metrics:
|
||||
mDice: 89.57
|
||||
Dice: 80.47
|
||||
Config: configs/unet/unet_s5-d16_deeplabv3_4xb4-40k_chase-db1-128x128.py
|
||||
Metadata:
|
||||
Training Data: CHASE_DB1
|
||||
Batch Size: 16
|
||||
Architecture:
|
||||
- UNet-S5-D16
|
||||
- UNet
|
||||
- DeepLabV3
|
||||
Training Resources: 4x V100 GPUS
|
||||
Memory (GB): 0.999
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_128x128_40k_chase_db1/deeplabv3_unet_s5-d16_128x128_40k_chase_db1_20201226_094047-4c5aefa3.pth
|
||||
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_128x128_40k_chase_db1/deeplabv3_unet_s5-d16_128x128_40k_chase_db1-20201226_094047.log.json
|
||||
Paper:
|
||||
Title: 'U-Net: Convolutional Networks for Biomedical Image Segmentation'
|
||||
URL: https://arxiv.org/abs/1505.04597
|
||||
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/unet.py#L225
|
||||
Framework: PyTorch
|
||||
- Name: unet-s5-d16_deeplabv3_4xb4-ce-1.0-dice-3.0-40k_chase-db1-128x128
|
||||
In Collection: UNet
|
||||
Results:
|
||||
Task: Semantic Segmentation
|
||||
Dataset: CHASE_DB1
|
||||
Metrics:
|
||||
mDice: 89.49
|
||||
Dice: 80.37
|
||||
Config: configs/unet/unet-s5-d16_deeplabv3_4xb4-ce-1.0-dice-3.0-40k_chase-db1-128x128.py
|
||||
Metadata:
|
||||
Training Data: CHASE_DB1
|
||||
Batch Size: 16
|
||||
Architecture:
|
||||
- UNet-S5-D16
|
||||
- UNet
|
||||
- DeepLabV3
|
||||
Training Resources: 4x V100 GPUS
|
||||
Memory (GB): 1.01
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1_20211210_201825-4ef29df5.pth
|
||||
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1_20211210_201825.log.json
|
||||
Paper:
|
||||
Title: 'U-Net: Convolutional Networks for Biomedical Image Segmentation'
|
||||
URL: https://arxiv.org/abs/1505.04597
|
||||
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/unet.py#L225
|
||||
Framework: PyTorch
|
||||
- Name: unet-s5-d16_fcn_4xb4-40k_hrf-256x256
|
||||
In Collection: UNet
|
||||
Results:
|
||||
Task: Semantic Segmentation
|
||||
Dataset: HRF
|
||||
Metrics:
|
||||
mDice: 88.92
|
||||
Dice: 79.45
|
||||
Config: configs/unet/unet-s5-d16_fcn_4xb4-40k_hrf-256x256.py
|
||||
Metadata:
|
||||
Training Data: HRF
|
||||
Batch Size: 16
|
||||
Architecture:
|
||||
- UNet-S5-D16
|
||||
- UNet
|
||||
- FCN
|
||||
Training Resources: 4x V100 GPUS
|
||||
Memory (GB): 2.525
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_256x256_40k_hrf/fcn_unet_s5-d16_256x256_40k_hrf_20201223_173724-d89cf1ed.pth
|
||||
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/unet/unet_s5-d16_256x256_40k_hrf/unet_s5-d16_256x256_40k_hrf-20201223_173724.log.json
|
||||
Paper:
|
||||
Title: 'U-Net: Convolutional Networks for Biomedical Image Segmentation'
|
||||
URL: https://arxiv.org/abs/1505.04597
|
||||
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/unet.py#L225
|
||||
Framework: PyTorch
|
||||
- Name: unet-s5-d16_fcn_4xb4-ce-1.0-dice-3.0-40k_hrf-256x256
|
||||
In Collection: UNet
|
||||
Results:
|
||||
Task: Semantic Segmentation
|
||||
Dataset: HRF
|
||||
Metrics:
|
||||
mDice: 89.64
|
||||
Dice: 80.87
|
||||
Config: configs/unet/unet-s5-d16_fcn_4xb4-ce-1.0-dice-3.0-40k_hrf-256x256.py
|
||||
Metadata:
|
||||
Training Data: HRF
|
||||
Batch Size: 16
|
||||
Architecture:
|
||||
- UNet-S5-D16
|
||||
- UNet
|
||||
- FCN
|
||||
Training Resources: 4x V100 GPUS
|
||||
Memory (GB): 2.623
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf/fcn_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf_20211210_201821-c314da8a.pth
|
||||
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf/fcn_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf_20211210_201821.log.json
|
||||
Paper:
|
||||
Title: 'U-Net: Convolutional Networks for Biomedical Image Segmentation'
|
||||
URL: https://arxiv.org/abs/1505.04597
|
||||
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/unet.py#L225
|
||||
Framework: PyTorch
|
||||
- Name: unet-s5-d16_pspnet_4xb4-40k_hrf-256x256
|
||||
In Collection: UNet
|
||||
Results:
|
||||
Task: Semantic Segmentation
|
||||
Dataset: HRF
|
||||
Metrics:
|
||||
mDice: 89.24
|
||||
Dice: 80.07
|
||||
Config: configs/unet/unet-s5-d16_pspnet_4xb4-40k_hrf-256x256.py
|
||||
Metadata:
|
||||
Training Data: HRF
|
||||
Batch Size: 16
|
||||
Architecture:
|
||||
- UNet-S5-D16
|
||||
- UNet
|
||||
- PSPNet
|
||||
Training Resources: 4x V100 GPUS
|
||||
Memory (GB): 2.588
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_256x256_40k_hrf/pspnet_unet_s5-d16_256x256_40k_hrf_20201227_181818-fdb7e29b.pth
|
||||
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_256x256_40k_hrf/pspnet_unet_s5-d16_256x256_40k_hrf-20201227_181818.log.json
|
||||
Paper:
|
||||
Title: 'U-Net: Convolutional Networks for Biomedical Image Segmentation'
|
||||
URL: https://arxiv.org/abs/1505.04597
|
||||
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/unet.py#L225
|
||||
Framework: PyTorch
|
||||
- Name: unet-s5-d16_pspnet_4xb4-ce-1.0-dice-3.0-40k_hrf-256x256
|
||||
In Collection: UNet
|
||||
Results:
|
||||
Task: Semantic Segmentation
|
||||
Dataset: HRF
|
||||
Metrics:
|
||||
mDice: 89.69
|
||||
Dice: 80.96
|
||||
Config: configs/unet/unet-s5-d16_pspnet_4xb4-ce-1.0-dice-3.0-40k_hrf-256x256.py
|
||||
Metadata:
|
||||
Training Data: HRF
|
||||
Batch Size: 16
|
||||
Architecture:
|
||||
- UNet-S5-D16
|
||||
- UNet
|
||||
- PSPNet
|
||||
Training Resources: 4x V100 GPUS
|
||||
Memory (GB): 2.798
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf/pspnet_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf_20211210_201823-53d492fa.pth
|
||||
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf/pspnet_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf_20211210_201823.log.json
|
||||
Paper:
|
||||
Title: 'U-Net: Convolutional Networks for Biomedical Image Segmentation'
|
||||
URL: https://arxiv.org/abs/1505.04597
|
||||
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/unet.py#L225
|
||||
Framework: PyTorch
|
||||
- Name: unet-s5-d16_deeplabv3_4xb4-40k_hrf-256x256
|
||||
In Collection: UNet
|
||||
Results:
|
||||
Task: Semantic Segmentation
|
||||
Dataset: HRF
|
||||
Metrics:
|
||||
mDice: 89.32
|
||||
Dice: 80.21
|
||||
Config: configs/unet/unet-s5-d16_deeplabv3_4xb4-40k_hrf-256x256.py
|
||||
Metadata:
|
||||
Training Data: HRF
|
||||
Batch Size: 16
|
||||
Architecture:
|
||||
- UNet-S5-D16
|
||||
- UNet
|
||||
- DeepLabV3
|
||||
Training Resources: 4x V100 GPUS
|
||||
Memory (GB): 2.604
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_256x256_40k_hrf/deeplabv3_unet_s5-d16_256x256_40k_hrf_20201226_094047-3a1fdf85.pth
|
||||
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_256x256_40k_hrf/deeplabv3_unet_s5-d16_256x256_40k_hrf-20201226_094047.log.json
|
||||
Paper:
|
||||
Title: 'U-Net: Convolutional Networks for Biomedical Image Segmentation'
|
||||
URL: https://arxiv.org/abs/1505.04597
|
||||
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/unet.py#L225
|
||||
Framework: PyTorch
|
||||
- Name: unet-s5-d16_deeplabv3_4xb4-ce-1.0-dice-3.0-40k_hrf-256x256
|
||||
In Collection: UNet
|
||||
Results:
|
||||
Task: Semantic Segmentation
|
||||
Dataset: HRF
|
||||
Metrics:
|
||||
mDice: 89.56
|
||||
Dice: 80.71
|
||||
Config: configs/unet/unet-s5-d16_deeplabv3_4xb4-ce-1.0-dice-3.0-40k_hrf-256x256.py
|
||||
Metadata:
|
||||
Training Data: HRF
|
||||
Batch Size: 16
|
||||
Architecture:
|
||||
- UNet-S5-D16
|
||||
- UNet
|
||||
- DeepLabV3
|
||||
Training Resources: 4x V100 GPUS
|
||||
Memory (GB): 2.607
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf_20211210_202032-59daf7a4.pth
|
||||
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf_20211210_202032.log.json
|
||||
Paper:
|
||||
Title: 'U-Net: Convolutional Networks for Biomedical Image Segmentation'
|
||||
URL: https://arxiv.org/abs/1505.04597
|
||||
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/unet.py#L225
|
||||
Framework: PyTorch
|
||||
@@ -0,0 +1,107 @@
|
||||
_base_ = [
|
||||
'../_base_/models/deeplabv3_unet_s5-d16.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 = (128, 128)
|
||||
|
||||
data_preprocessor = dict(
|
||||
size=(128, 128),
|
||||
mean=[
|
||||
94.94709810464303,
|
||||
61.72942233949928,
|
||||
75.93763705236906,
|
||||
],
|
||||
std=[
|
||||
44.005506081132594,
|
||||
42.69595666984776,
|
||||
44.99354156225523,
|
||||
],
|
||||
bgr_to_rgb=False,
|
||||
)
|
||||
|
||||
model = dict(
|
||||
data_preprocessor=dict(
|
||||
size=(128, 128),
|
||||
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='CrossEntropyLoss',
|
||||
loss_name='loss_ce',
|
||||
loss_weight=1.0,
|
||||
),
|
||||
dict(
|
||||
type='DiceLoss',
|
||||
loss_name='loss_dice',
|
||||
loss_weight=3.0,
|
||||
),
|
||||
],
|
||||
align_corners=True,
|
||||
norm_cfg=dict(
|
||||
type='BN',
|
||||
),
|
||||
),
|
||||
test_cfg=dict(
|
||||
mode='slide',
|
||||
crop_size=(128, 128),
|
||||
stride=(85, 85),
|
||||
),
|
||||
auxiliary_head=dict(
|
||||
num_classes=36,
|
||||
norm_cfg=dict(
|
||||
type='BN',
|
||||
),
|
||||
),
|
||||
)
|
||||
|
||||
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 +1,9 @@
|
||||
_base_ = [
|
||||
'../_base_/models/deeplabv3_unet_s5-d16.py', '../_base_/datasets/drive.py',
|
||||
'../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py'
|
||||
]
|
||||
crop_size = (64, 64)
|
||||
data_preprocessor = dict(size=crop_size)
|
||||
model = dict(
|
||||
data_preprocessor=data_preprocessor,
|
||||
test_cfg=dict(crop_size=(64, 64), stride=(42, 42)))
|
||||
@@ -0,0 +1,9 @@
|
||||
_base_ = [
|
||||
'../_base_/models/deeplabv3_unet_s5-d16.py', '../_base_/datasets/hrf.py',
|
||||
'../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py'
|
||||
]
|
||||
crop_size = (256, 256)
|
||||
data_preprocessor = dict(size=crop_size)
|
||||
model = dict(
|
||||
data_preprocessor=data_preprocessor,
|
||||
test_cfg=dict(crop_size=(256, 256), stride=(170, 170)))
|
||||
@@ -0,0 +1,9 @@
|
||||
_base_ = [
|
||||
'../_base_/models/deeplabv3_unet_s5-d16.py', '../_base_/datasets/stare.py',
|
||||
'../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py'
|
||||
]
|
||||
crop_size = (128, 128)
|
||||
data_preprocessor = dict(size=crop_size)
|
||||
model = dict(
|
||||
data_preprocessor=data_preprocessor,
|
||||
test_cfg=dict(crop_size=(128, 128), stride=(85, 85)))
|
||||
@@ -0,0 +1,6 @@
|
||||
_base_ = './unet_s5-d16_deeplabv3_4xb4-40k_chase-db1-128x128.py'
|
||||
model = dict(
|
||||
decode_head=dict(loss_decode=[
|
||||
dict(type='CrossEntropyLoss', loss_name='loss_ce', loss_weight=1.0),
|
||||
dict(type='DiceLoss', loss_name='loss_dice', loss_weight=3.0)
|
||||
]))
|
||||
@@ -0,0 +1,6 @@
|
||||
_base_ = './unet-s5-d16_deeplabv3_4xb4-40k_drive-64x64.py'
|
||||
model = dict(
|
||||
decode_head=dict(loss_decode=[
|
||||
dict(type='CrossEntropyLoss', loss_name='loss_ce', loss_weight=1.0),
|
||||
dict(type='DiceLoss', loss_name='loss_dice', loss_weight=3.0)
|
||||
]))
|
||||
@@ -0,0 +1,6 @@
|
||||
_base_ = './unet-s5-d16_deeplabv3_4xb4-40k_hrf-256x256.py'
|
||||
model = dict(
|
||||
decode_head=dict(loss_decode=[
|
||||
dict(type='CrossEntropyLoss', loss_name='loss_ce', loss_weight=1.0),
|
||||
dict(type='DiceLoss', loss_name='loss_dice', loss_weight=3.0)
|
||||
]))
|
||||
@@ -0,0 +1,6 @@
|
||||
_base_ = './unet-s5-d16_deeplabv3_4xb4-40k_stare-128x128.py'
|
||||
model = dict(
|
||||
decode_head=dict(loss_decode=[
|
||||
dict(type='CrossEntropyLoss', loss_name='loss_ce', loss_weight=1.0),
|
||||
dict(type='DiceLoss', loss_name='loss_dice', loss_weight=3.0)
|
||||
]))
|
||||
@@ -0,0 +1,16 @@
|
||||
_base_ = [
|
||||
'../_base_/models/fcn_unet_s5-d16.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,
|
||||
decode_head=dict(num_classes=19),
|
||||
auxiliary_head=dict(num_classes=19),
|
||||
# model training and testing settings
|
||||
train_cfg=dict(),
|
||||
test_cfg=dict(mode='whole'))
|
||||
train_dataloader = dict(batch_size=4, num_workers=4)
|
||||
val_dataloader = dict(batch_size=1, num_workers=4)
|
||||
test_dataloader = val_dataloader
|
||||
@@ -0,0 +1,36 @@
|
||||
_base_ = [
|
||||
'../_base_/models/fcn_unet_s5-d16.py', '../_base_/datasets/hsi_drive.py',
|
||||
'../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py'
|
||||
]
|
||||
crop_size = (192, 384)
|
||||
data_preprocessor = dict(
|
||||
type='SegDataPreProcessor',
|
||||
size=crop_size,
|
||||
mean=None,
|
||||
std=None,
|
||||
bgr_to_rgb=None,
|
||||
pad_val=0,
|
||||
seg_pad_val=255)
|
||||
|
||||
model = dict(
|
||||
data_preprocessor=data_preprocessor,
|
||||
backbone=dict(in_channels=25),
|
||||
decode_head=dict(
|
||||
ignore_index=0,
|
||||
num_classes=11,
|
||||
loss_decode=dict(
|
||||
type='CrossEntropyLoss',
|
||||
use_sigmoid=False,
|
||||
loss_weight=1.0,
|
||||
avg_non_ignore=True)),
|
||||
auxiliary_head=dict(
|
||||
ignore_index=0,
|
||||
num_classes=11,
|
||||
loss_decode=dict(
|
||||
type='CrossEntropyLoss',
|
||||
use_sigmoid=False,
|
||||
loss_weight=1.0,
|
||||
avg_non_ignore=True)),
|
||||
# model training and testing settings
|
||||
train_cfg=dict(),
|
||||
test_cfg=dict(mode='whole'))
|
||||
@@ -0,0 +1,9 @@
|
||||
_base_ = [
|
||||
'../_base_/models/fcn_unet_s5-d16.py', '../_base_/datasets/chase_db1.py',
|
||||
'../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py'
|
||||
]
|
||||
crop_size = (128, 128)
|
||||
data_preprocessor = dict(size=crop_size)
|
||||
model = dict(
|
||||
data_preprocessor=data_preprocessor,
|
||||
test_cfg=dict(crop_size=(128, 128), stride=(85, 85)))
|
||||
@@ -0,0 +1,9 @@
|
||||
_base_ = [
|
||||
'../_base_/models/fcn_unet_s5-d16.py', '../_base_/datasets/drive.py',
|
||||
'../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py'
|
||||
]
|
||||
crop_size = (64, 64)
|
||||
data_preprocessor = dict(size=crop_size)
|
||||
model = dict(
|
||||
data_preprocessor=data_preprocessor,
|
||||
test_cfg=dict(crop_size=(64, 64), stride=(42, 42)))
|
||||
@@ -0,0 +1,9 @@
|
||||
_base_ = [
|
||||
'../_base_/models/fcn_unet_s5-d16.py', '../_base_/datasets/hrf.py',
|
||||
'../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py'
|
||||
]
|
||||
crop_size = (256, 256)
|
||||
data_preprocessor = dict(size=crop_size)
|
||||
model = dict(
|
||||
data_preprocessor=data_preprocessor,
|
||||
test_cfg=dict(crop_size=(256, 256), stride=(170, 170)))
|
||||
@@ -0,0 +1,9 @@
|
||||
_base_ = [
|
||||
'../_base_/models/fcn_unet_s5-d16.py', '../_base_/datasets/stare.py',
|
||||
'../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py'
|
||||
]
|
||||
crop_size = (128, 128)
|
||||
data_preprocessor = dict(size=crop_size)
|
||||
model = dict(
|
||||
data_preprocessor=data_preprocessor,
|
||||
test_cfg=dict(crop_size=(128, 128), stride=(85, 85)))
|
||||
@@ -0,0 +1,6 @@
|
||||
_base_ = './unet-s5-d16_fcn_4xb4-40k_chase-db1-128x128.py'
|
||||
model = dict(
|
||||
decode_head=dict(loss_decode=[
|
||||
dict(type='CrossEntropyLoss', loss_name='loss_ce', loss_weight=1.0),
|
||||
dict(type='DiceLoss', loss_name='loss_dice', loss_weight=3.0)
|
||||
]))
|
||||
@@ -0,0 +1,6 @@
|
||||
_base_ = './unet-s5-d16_fcn_4xb4-40k_drive-64x64.py'
|
||||
model = dict(
|
||||
decode_head=dict(loss_decode=[
|
||||
dict(type='CrossEntropyLoss', loss_name='loss_ce', loss_weight=1.0),
|
||||
dict(type='DiceLoss', loss_name='loss_dice', loss_weight=3.0)
|
||||
]))
|
||||
@@ -0,0 +1,6 @@
|
||||
_base_ = './unet-s5-d16_fcn_4xb4-40k_hrf-256x256.py'
|
||||
model = dict(
|
||||
decode_head=dict(loss_decode=[
|
||||
dict(type='CrossEntropyLoss', loss_name='loss_ce', loss_weight=1.0),
|
||||
dict(type='DiceLoss', loss_name='loss_dice', loss_weight=3.0)
|
||||
]))
|
||||
@@ -0,0 +1,6 @@
|
||||
_base_ = './unet-s5-d16_fcn_4xb4-40k_stare-128x128.py'
|
||||
model = dict(
|
||||
decode_head=dict(loss_decode=[
|
||||
dict(type='CrossEntropyLoss', loss_name='loss_ce', loss_weight=1.0),
|
||||
dict(type='DiceLoss', loss_name='loss_dice', loss_weight=3.0)
|
||||
]))
|
||||
@@ -0,0 +1,10 @@
|
||||
_base_ = [
|
||||
'../_base_/models/pspnet_unet_s5-d16.py',
|
||||
'../_base_/datasets/chase_db1.py', '../_base_/default_runtime.py',
|
||||
'../_base_/schedules/schedule_40k.py'
|
||||
]
|
||||
crop_size = (128, 128)
|
||||
data_preprocessor = dict(size=crop_size)
|
||||
model = dict(
|
||||
data_preprocessor=data_preprocessor,
|
||||
test_cfg=dict(crop_size=(128, 128), stride=(85, 85)))
|
||||
@@ -0,0 +1,9 @@
|
||||
_base_ = [
|
||||
'../_base_/models/pspnet_unet_s5-d16.py', '../_base_/datasets/drive.py',
|
||||
'../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py'
|
||||
]
|
||||
crop_size = (64, 64)
|
||||
data_preprocessor = dict(size=crop_size)
|
||||
model = dict(
|
||||
data_preprocessor=data_preprocessor,
|
||||
test_cfg=dict(crop_size=(64, 64), stride=(42, 42)))
|
||||
@@ -0,0 +1,9 @@
|
||||
_base_ = [
|
||||
'../_base_/models/pspnet_unet_s5-d16.py', '../_base_/datasets/hrf.py',
|
||||
'../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py'
|
||||
]
|
||||
crop_size = (256, 256)
|
||||
data_preprocessor = dict(size=crop_size)
|
||||
model = dict(
|
||||
data_preprocessor=data_preprocessor,
|
||||
test_cfg=dict(crop_size=(256, 256), stride=(170, 170)))
|
||||
@@ -0,0 +1,9 @@
|
||||
_base_ = [
|
||||
'../_base_/models/pspnet_unet_s5-d16.py', '../_base_/datasets/stare.py',
|
||||
'../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py'
|
||||
]
|
||||
crop_size = (128, 128)
|
||||
data_preprocessor = dict(size=crop_size)
|
||||
model = dict(
|
||||
data_preprocessor=data_preprocessor,
|
||||
test_cfg=dict(crop_size=(128, 128), stride=(85, 85)))
|
||||
@@ -0,0 +1,6 @@
|
||||
_base_ = './unet-s5-d16_pspnet_4xb4-40k_chase-db1-128x128.py'
|
||||
model = dict(
|
||||
decode_head=dict(loss_decode=[
|
||||
dict(type='CrossEntropyLoss', loss_name='loss_ce', loss_weight=1.0),
|
||||
dict(type='DiceLoss', loss_name='loss_dice', loss_weight=3.0)
|
||||
]))
|
||||
@@ -0,0 +1,6 @@
|
||||
_base_ = './unet-s5-d16_pspnet_4xb4-40k_drive-64x64.py'
|
||||
model = dict(
|
||||
decode_head=dict(loss_decode=[
|
||||
dict(type='CrossEntropyLoss', loss_name='loss_ce', loss_weight=1.0),
|
||||
dict(type='DiceLoss', loss_name='loss_dice', loss_weight=3.0)
|
||||
]))
|
||||
@@ -0,0 +1,6 @@
|
||||
_base_ = './unet-s5-d16_pspnet_4xb4-40k_hrf-256x256.py'
|
||||
model = dict(
|
||||
decode_head=dict(loss_decode=[
|
||||
dict(type='CrossEntropyLoss', loss_name='loss_ce', loss_weight=1.0),
|
||||
dict(type='DiceLoss', loss_name='loss_dice', loss_weight=3.0)
|
||||
]))
|
||||
@@ -0,0 +1,6 @@
|
||||
_base_ = './unet-s5-d16_pspnet_4xb4-40k_stare-128x128.py'
|
||||
model = dict(
|
||||
decode_head=dict(loss_decode=[
|
||||
dict(type='CrossEntropyLoss', loss_name='loss_ce', loss_weight=1.0),
|
||||
dict(type='DiceLoss', loss_name='loss_dice', loss_weight=3.0)
|
||||
]))
|
||||
@@ -0,0 +1,10 @@
|
||||
_base_ = [
|
||||
'../_base_/models/deeplabv3_unet_s5-d16.py',
|
||||
'../_base_/datasets/chase_db1.py', '../_base_/default_runtime.py',
|
||||
'../_base_/schedules/schedule_40k.py'
|
||||
]
|
||||
crop_size = (128, 128)
|
||||
data_preprocessor = dict(size=crop_size)
|
||||
model = dict(
|
||||
data_preprocessor=data_preprocessor,
|
||||
test_cfg=dict(crop_size=(128, 128), stride=(85, 85)))
|
||||
Reference in New Issue
Block a user