Upload folder using huggingface_hub (part 2)

EgoHOS/raw/script/mmsegmentation/configs/deeplabv3/deeplabv3_r101-d8_512x512_4x4_20k_coco-stuff10k.py

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+_base_ = './deeplabv3_r50-d8_512x512_4x4_20k_coco-stuff10k.py'
+model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))

EgoHOS/raw/script/mmsegmentation/configs/deeplabv3/deeplabv3_r101-d8_512x512_4x4_320k_coco-stuff164k.py

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+_base_ = './deeplabv3_r50-d8_512x512_4x4_320k_coco-stuff164k.py'
+model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))

EgoHOS/raw/script/mmsegmentation/configs/deeplabv3/deeplabv3_r101-d8_512x512_4x4_40k_coco-stuff10k.py

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+_base_ = './deeplabv3_r50-d8_512x512_4x4_40k_coco-stuff10k.py'
+model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))

EgoHOS/raw/script/mmsegmentation/configs/deeplabv3/deeplabv3_r101-d8_512x512_4x4_80k_coco-stuff164k.py

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+_base_ = './deeplabv3_r50-d8_512x512_4x4_80k_coco-stuff164k.py'
+model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))

EgoHOS/raw/script/mmsegmentation/configs/deeplabv3/deeplabv3_r101-d8_512x512_80k_ade20k.py

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+_base_ = './deeplabv3_r50-d8_512x512_80k_ade20k.py'
+model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))

EgoHOS/raw/script/mmsegmentation/configs/deeplabv3/deeplabv3_r101-d8_769x769_40k_cityscapes.py

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+_base_ = './deeplabv3_r50-d8_769x769_40k_cityscapes.py'
+model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))

EgoHOS/raw/script/mmsegmentation/configs/deeplabv3/deeplabv3_r101-d8_769x769_80k_cityscapes.py

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+_base_ = './deeplabv3_r50-d8_769x769_80k_cityscapes.py'
+model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))

EgoHOS/raw/script/mmsegmentation/configs/deeplabv3/deeplabv3_r101-d8_fp16_512x1024_80k_cityscapes.py

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+_base_ = './deeplabv3_r101-d8_512x1024_80k_cityscapes.py'
+# fp16 settings
+optimizer_config = dict(type='Fp16OptimizerHook', loss_scale=512.)
+# fp16 placeholder
+fp16 = dict()

EgoHOS/raw/script/mmsegmentation/configs/deeplabv3/deeplabv3_r101b-d8_512x1024_80k_cityscapes.py

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+_base_ = './deeplabv3_r50-d8_512x1024_80k_cityscapes.py'
+model = dict(
+    pretrained='torchvision://resnet101',
+    backbone=dict(type='ResNet', depth=101))

EgoHOS/raw/script/mmsegmentation/configs/deeplabv3/deeplabv3_r101b-d8_769x769_80k_cityscapes.py

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+_base_ = './deeplabv3_r50-d8_769x769_80k_cityscapes.py'
+model = dict(
+    pretrained='torchvision://resnet101',
+    backbone=dict(type='ResNet', depth=101))

EgoHOS/raw/script/mmsegmentation/configs/deeplabv3/deeplabv3_r18-d8_512x1024_80k_cityscapes.py

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+_base_ = './deeplabv3_r50-d8_512x1024_80k_cityscapes.py'
+model = dict(
+    pretrained='open-mmlab://resnet18_v1c',
+    backbone=dict(depth=18),
+    decode_head=dict(
+        in_channels=512,
+        channels=128,
+    ),
+    auxiliary_head=dict(in_channels=256, channels=64))

EgoHOS/raw/script/mmsegmentation/configs/deeplabv3/deeplabv3_r18-d8_769x769_80k_cityscapes.py

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+_base_ = './deeplabv3_r50-d8_769x769_80k_cityscapes.py'
+model = dict(
+    pretrained='open-mmlab://resnet18_v1c',
+    backbone=dict(depth=18),
+    decode_head=dict(
+        in_channels=512,
+        channels=128,
+    ),
+    auxiliary_head=dict(in_channels=256, channels=64))

EgoHOS/raw/script/mmsegmentation/configs/deeplabv3/deeplabv3_r18b-d8_512x1024_80k_cityscapes.py

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+_base_ = './deeplabv3_r50-d8_512x1024_80k_cityscapes.py'
+model = dict(
+    pretrained='torchvision://resnet18',
+    backbone=dict(type='ResNet', depth=18),
+    decode_head=dict(
+        in_channels=512,
+        channels=128,
+    ),
+    auxiliary_head=dict(in_channels=256, channels=64))

EgoHOS/raw/script/mmsegmentation/configs/deeplabv3/deeplabv3_r18b-d8_769x769_80k_cityscapes.py

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+_base_ = './deeplabv3_r50-d8_769x769_80k_cityscapes.py'
+model = dict(
+    pretrained='torchvision://resnet18',
+    backbone=dict(type='ResNet', depth=18),
+    decode_head=dict(
+        in_channels=512,
+        channels=128,
+    ),
+    auxiliary_head=dict(in_channels=256, channels=64))

EgoHOS/raw/script/mmsegmentation/configs/deeplabv3/deeplabv3_r50-d8_480x480_40k_pascal_context.py

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+_base_ = [
+    '../_base_/models/deeplabv3_r50-d8.py',
+    '../_base_/datasets/pascal_context.py', '../_base_/default_runtime.py',
+    '../_base_/schedules/schedule_40k.py'
+]
+model = dict(
+    decode_head=dict(num_classes=60),
+    auxiliary_head=dict(num_classes=60),
+    test_cfg=dict(mode='slide', crop_size=(480, 480), stride=(320, 320)))
+optimizer = dict(type='SGD', lr=0.004, momentum=0.9, weight_decay=0.0001)

EgoHOS/raw/script/mmsegmentation/configs/deeplabv3/deeplabv3_r50-d8_480x480_40k_pascal_context_59.py

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+_base_ = [
+    '../_base_/models/deeplabv3_r50-d8.py',
+    '../_base_/datasets/pascal_context_59.py', '../_base_/default_runtime.py',
+    '../_base_/schedules/schedule_40k.py'
+]
+model = dict(
+    decode_head=dict(num_classes=59),
+    auxiliary_head=dict(num_classes=59),
+    test_cfg=dict(mode='slide', crop_size=(480, 480), stride=(320, 320)))
+optimizer = dict(type='SGD', lr=0.004, momentum=0.9, weight_decay=0.0001)

EgoHOS/raw/script/mmsegmentation/configs/deeplabv3/deeplabv3_r50-d8_480x480_80k_pascal_context.py

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+_base_ = [
+    '../_base_/models/deeplabv3_r50-d8.py',
+    '../_base_/datasets/pascal_context.py', '../_base_/default_runtime.py',
+    '../_base_/schedules/schedule_80k.py'
+]
+model = dict(
+    decode_head=dict(num_classes=60),
+    auxiliary_head=dict(num_classes=60),
+    test_cfg=dict(mode='slide', crop_size=(480, 480), stride=(320, 320)))
+optimizer = dict(type='SGD', lr=0.004, momentum=0.9, weight_decay=0.0001)

EgoHOS/raw/script/mmsegmentation/configs/deeplabv3/deeplabv3_r50-d8_480x480_80k_pascal_context_59.py

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+_base_ = [
+    '../_base_/models/deeplabv3_r50-d8.py',
+    '../_base_/datasets/pascal_context_59.py', '../_base_/default_runtime.py',
+    '../_base_/schedules/schedule_80k.py'
+]
+model = dict(
+    decode_head=dict(num_classes=59),
+    auxiliary_head=dict(num_classes=59),
+    test_cfg=dict(mode='slide', crop_size=(480, 480), stride=(320, 320)))
+optimizer = dict(type='SGD', lr=0.004, momentum=0.9, weight_decay=0.0001)

EgoHOS/raw/script/mmsegmentation/configs/deeplabv3/deeplabv3_r50-d8_512x1024_40k_cityscapes.py

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+_base_ = [
+    '../_base_/models/deeplabv3_r50-d8.py', '../_base_/datasets/cityscapes.py',
+    '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py'
+]

EgoHOS/raw/script/mmsegmentation/configs/deeplabv3/deeplabv3_r50-d8_512x1024_80k_cityscapes.py

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+_base_ = [
+    '../_base_/models/deeplabv3_r50-d8.py', '../_base_/datasets/cityscapes.py',
+    '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
+]

EgoHOS/raw/script/mmsegmentation/configs/deeplabv3/deeplabv3_r50-d8_512x512_160k_ade20k.py

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+_base_ = [
+    '../_base_/models/deeplabv3_r50-d8.py', '../_base_/datasets/ade20k.py',
+    '../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py'
+]
+model = dict(
+    decode_head=dict(num_classes=150), auxiliary_head=dict(num_classes=150))

EgoHOS/raw/script/mmsegmentation/configs/deeplabv3/deeplabv3_r50-d8_512x512_20k_voc12aug.py

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+_base_ = [
+    '../_base_/models/deeplabv3_r50-d8.py',
+    '../_base_/datasets/pascal_voc12_aug.py', '../_base_/default_runtime.py',
+    '../_base_/schedules/schedule_20k.py'
+]
+model = dict(
+    decode_head=dict(num_classes=21), auxiliary_head=dict(num_classes=21))

EgoHOS/raw/script/mmsegmentation/configs/deeplabv3/deeplabv3_r50-d8_512x512_40k_voc12aug.py

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+_base_ = [
+    '../_base_/models/deeplabv3_r50-d8.py',
+    '../_base_/datasets/pascal_voc12_aug.py', '../_base_/default_runtime.py',
+    '../_base_/schedules/schedule_40k.py'
+]
+model = dict(
+    decode_head=dict(num_classes=21), auxiliary_head=dict(num_classes=21))

EgoHOS/raw/script/mmsegmentation/configs/deeplabv3/deeplabv3_r50-d8_512x512_4x4_160k_coco-stuff164k.py

@@ -0,0 +1,7 @@
+_base_ = [
+    '../_base_/models/deeplabv3_r50-d8.py',
+    '../_base_/datasets/coco-stuff164k.py', '../_base_/default_runtime.py',
+    '../_base_/schedules/schedule_160k.py'
+]
+model = dict(
+    decode_head=dict(num_classes=171), auxiliary_head=dict(num_classes=171))

EgoHOS/raw/script/mmsegmentation/configs/deeplabv3/deeplabv3_r50-d8_512x512_4x4_20k_coco-stuff10k.py

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+_base_ = [
+    '../_base_/models/deeplabv3_r50-d8.py',
+    '../_base_/datasets/coco-stuff10k.py', '../_base_/default_runtime.py',
+    '../_base_/schedules/schedule_20k.py'
+]
+model = dict(
+    decode_head=dict(num_classes=171), auxiliary_head=dict(num_classes=171))

EgoHOS/raw/script/mmsegmentation/configs/deeplabv3/deeplabv3_r50-d8_512x512_4x4_320k_coco-stuff164k.py

@@ -0,0 +1,7 @@
+_base_ = [
+    '../_base_/models/deeplabv3_r50-d8.py',
+    '../_base_/datasets/coco-stuff164k.py', '../_base_/default_runtime.py',
+    '../_base_/schedules/schedule_320k.py'
+]
+model = dict(
+    decode_head=dict(num_classes=171), auxiliary_head=dict(num_classes=171))

EgoHOS/raw/script/mmsegmentation/configs/deeplabv3/deeplabv3_r50-d8_512x512_4x4_40k_coco-stuff10k.py

@@ -0,0 +1,7 @@
+_base_ = [
+    '../_base_/models/deeplabv3_r50-d8.py',
+    '../_base_/datasets/coco-stuff10k.py', '../_base_/default_runtime.py',
+    '../_base_/schedules/schedule_40k.py'
+]
+model = dict(
+    decode_head=dict(num_classes=171), auxiliary_head=dict(num_classes=171))

EgoHOS/raw/script/mmsegmentation/configs/deeplabv3/deeplabv3_r50-d8_512x512_4x4_80k_coco-stuff164k.py

@@ -0,0 +1,7 @@
+_base_ = [
+    '../_base_/models/deeplabv3_r50-d8.py',
+    '../_base_/datasets/coco-stuff164k.py', '../_base_/default_runtime.py',
+    '../_base_/schedules/schedule_80k.py'
+]
+model = dict(
+    decode_head=dict(num_classes=171), auxiliary_head=dict(num_classes=171))

EgoHOS/raw/script/mmsegmentation/configs/deeplabv3/deeplabv3_r50-d8_512x512_80k_ade20k.py

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+_base_ = [
+    '../_base_/models/deeplabv3_r50-d8.py', '../_base_/datasets/ade20k.py',
+    '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
+]
+model = dict(
+    decode_head=dict(num_classes=150), auxiliary_head=dict(num_classes=150))

EgoHOS/raw/script/mmsegmentation/configs/deeplabv3/deeplabv3_r50-d8_769x769_40k_cityscapes.py

@@ -0,0 +1,9 @@
+_base_ = [
+    '../_base_/models/deeplabv3_r50-d8.py',
+    '../_base_/datasets/cityscapes_769x769.py', '../_base_/default_runtime.py',
+    '../_base_/schedules/schedule_40k.py'
+]
+model = dict(
+    decode_head=dict(align_corners=True),
+    auxiliary_head=dict(align_corners=True),
+    test_cfg=dict(mode='slide', crop_size=(769, 769), stride=(513, 513)))

EgoHOS/raw/script/mmsegmentation/configs/deeplabv3/deeplabv3_r50-d8_769x769_80k_cityscapes.py

@@ -0,0 +1,9 @@
+_base_ = [
+    '../_base_/models/deeplabv3_r50-d8.py',
+    '../_base_/datasets/cityscapes_769x769.py', '../_base_/default_runtime.py',
+    '../_base_/schedules/schedule_80k.py'
+]
+model = dict(
+    decode_head=dict(align_corners=True),
+    auxiliary_head=dict(align_corners=True),
+    test_cfg=dict(mode='slide', crop_size=(769, 769), stride=(513, 513)))

EgoHOS/raw/script/mmsegmentation/configs/deeplabv3/deeplabv3_r50b-d8_512x1024_80k_cityscapes.py

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+_base_ = './deeplabv3_r50-d8_512x1024_80k_cityscapes.py'
+model = dict(pretrained='torchvision://resnet50', backbone=dict(type='ResNet'))

EgoHOS/raw/script/mmsegmentation/configs/deeplabv3/deeplabv3_r50b-d8_769x769_80k_cityscapes.py

@@ -0,0 +1,2 @@
+_base_ = './deeplabv3_r50-d8_769x769_80k_cityscapes.py'
+model = dict(pretrained='torchvision://resnet50', backbone=dict(type='ResNet'))

EgoHOS/raw/script/mmsegmentation/configs/deeplabv3plus/README.md

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+# DeepLabV3+
+
+[Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://arxiv.org/abs/1802.02611)
+
+## Introduction
+
+<!-- [ALGORITHM] -->
+
+<a href="https://github.com/tensorflow/models/tree/master/research/deeplab">Official Repo</a>
+
+<a href="https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/sep_aspp_head.py#L30">Code Snippet</a>
+
+## Abstract
+
+<!-- [ABSTRACT] -->
+
+Spatial pyramid pooling module or encode-decoder structure are used in deep neural networks for semantic segmentation task. The former networks are able to encode multi-scale contextual information by probing the incoming features with filters or pooling operations at multiple rates and multiple effective fields-of-view, while the latter networks can capture sharper object boundaries by gradually recovering the spatial information. In this work, we propose to combine the advantages from both methods. Specifically, our proposed model, DeepLabv3+, extends DeepLabv3 by adding a simple yet effective decoder module to refine the segmentation results especially along object boundaries. We further explore the Xception model and apply the depthwise separable convolution to both Atrous Spatial Pyramid Pooling and decoder modules, resulting in a faster and stronger encoder-decoder network. We demonstrate the effectiveness of the proposed model on PASCAL VOC 2012 and Cityscapes datasets, achieving the test set performance of 89.0\% and 82.1\% without any post-processing. Our paper is accompanied with a publicly available reference implementation of the proposed models in Tensorflow at [this https URL](https://github.com/tensorflow/models/tree/master/research/deeplab).
+
+<!-- [IMAGE] -->
+<div align=center>
+<img src="https://user-images.githubusercontent.com/24582831/142900680-3e2c3098-8341-4760-bbfd-b1d7d29968ea.png" width="70%"/>
+</div>
+
+## Citation
+
+```bibtex
+@inproceedings{deeplabv3plus2018,
+  title={Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation},
+  author={Liang-Chieh Chen and Yukun Zhu and George Papandreou and Florian Schroff and Hartwig Adam},
+  booktitle={ECCV},
+  year={2018}
+}
+```
+
+## Results and models
+
+### Cityscapes
+
+| Method     | Backbone        | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                                           | download                                                                                                                                                                                                                                                                                                                                                                                                                           |
+| ---------- | --------------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ------------------------------------------------------------------------------------------------------------------------------------------------ | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| DeepLabV3+ | R-50-D8         | 512x1024  |   40000 | 7.5      | 3.94           | 79.61 |         81.01 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r50-d8_512x1024_40k_cityscapes.py)         | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x1024_40k_cityscapes/deeplabv3plus_r50-d8_512x1024_40k_cityscapes_20200605_094610-d222ffcd.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x1024_40k_cityscapes/deeplabv3plus_r50-d8_512x1024_40k_cityscapes_20200605_094610.log.json)                                 |
+| DeepLabV3+ | R-101-D8        | 512x1024  |   40000 | 11       | 2.60           | 80.21 |         81.82 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r101-d8_512x1024_40k_cityscapes.py)        | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x1024_40k_cityscapes/deeplabv3plus_r101-d8_512x1024_40k_cityscapes_20200605_094614-3769eecf.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x1024_40k_cityscapes/deeplabv3plus_r101-d8_512x1024_40k_cityscapes_20200605_094614.log.json)                             |
+| DeepLabV3+ | R-50-D8         | 769x769   |   40000 | 8.5      | 1.72           | 78.97 |         80.46 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r50-d8_769x769_40k_cityscapes.py)          | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_769x769_40k_cityscapes/deeplabv3plus_r50-d8_769x769_40k_cityscapes_20200606_114143-1dcb0e3c.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_769x769_40k_cityscapes/deeplabv3plus_r50-d8_769x769_40k_cityscapes_20200606_114143.log.json)                                     |
+| DeepLabV3+ | R-101-D8        | 769x769   |   40000 | 12.5     | 1.15           | 79.46 |         80.50 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r101-d8_769x769_40k_cityscapes.py)         | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_769x769_40k_cityscapes/deeplabv3plus_r101-d8_769x769_40k_cityscapes_20200606_114304-ff414b9e.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_769x769_40k_cityscapes/deeplabv3plus_r101-d8_769x769_40k_cityscapes_20200606_114304.log.json)                                 |
+| DeepLabV3+ | R-18-D8         | 512x1024  |   80000 | 2.2      | 14.27          | 76.89 |         78.76 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r18-d8_512x1024_80k_cityscapes.py)         | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_512x1024_80k_cityscapes/deeplabv3plus_r18-d8_512x1024_80k_cityscapes_20201226_080942-cff257fe.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_512x1024_80k_cityscapes/deeplabv3plus_r18-d8_512x1024_80k_cityscapes-20201226_080942.log.json)                                 |
+| DeepLabV3+ | R-50-D8         | 512x1024  |   80000 | -        | -              | 80.09 |         81.13 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r50-d8_512x1024_80k_cityscapes.py)         | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x1024_80k_cityscapes/deeplabv3plus_r50-d8_512x1024_80k_cityscapes_20200606_114049-f9fb496d.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x1024_80k_cityscapes/deeplabv3plus_r50-d8_512x1024_80k_cityscapes_20200606_114049.log.json)                                 |
+| DeepLabV3+ | R-101-D8        | 512x1024  |   80000 | -        | -              | 80.97 |         82.03 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r101-d8_512x1024_80k_cityscapes.py)        | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x1024_80k_cityscapes/deeplabv3plus_r101-d8_512x1024_80k_cityscapes_20200606_114143-068fcfe9.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x1024_80k_cityscapes/deeplabv3plus_r101-d8_512x1024_80k_cityscapes_20200606_114143.log.json)                             |
+| DeepLabV3+ (FP16)| R-101-D8 | 512x1024  |   80000 | 6.35     | 7.87           | 80.46 |             - | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r101-d8_fp16_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_fp16_512x1024_80k_cityscapes/deeplabv3plus_r101-d8_fp16_512x1024_80k_cityscapes_20200717_230920-f1104f4b.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_fp16_512x1024_80k_cityscapes/deeplabv3plus_r101-d8_fp16_512x1024_80k_cityscapes_20200717_230920.log.json) |
+| DeepLabV3+ | R-18-D8         | 769x769   |   80000 | 2.5      | 5.74           | 76.26 |         77.91 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r18-d8_769x769_80k_cityscapes.py)          | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_769x769_80k_cityscapes/deeplabv3plus_r18-d8_769x769_80k_cityscapes_20201226_083346-f326e06a.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_769x769_80k_cityscapes/deeplabv3plus_r18-d8_769x769_80k_cityscapes-20201226_083346.log.json)                                     |
+| DeepLabV3+ | R-50-D8         | 769x769   |   80000 | -        | -              | 79.83 |         81.48 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r50-d8_769x769_80k_cityscapes.py)          | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_769x769_80k_cityscapes/deeplabv3plus_r50-d8_769x769_80k_cityscapes_20200606_210233-0e9dfdc4.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_769x769_80k_cityscapes/deeplabv3plus_r50-d8_769x769_80k_cityscapes_20200606_210233.log.json)                                     |
+| DeepLabV3+ | R-101-D8        | 769x769   |   80000 | -        | -              | 80.65 |         81.47 | [config<sup>[1]</sup>](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r101-d8_769x769_80k_cityscapes.py)         | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_769x769_80k_cityscapes/deeplabv3plus_r101-d8_769x769_80k_cityscapes_20220406_154720-dfcc0b68.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_769x769_80k_cityscapes/deeplabv3plus_r101-d8_769x769_80k_cityscapes_20220406_154720.log.json)                                 |
+| DeepLabV3+ | R-101-D16-MG124 | 512x1024  |   40000 | 5.8      | 7.48           | 79.09 |         80.36 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r101-d16-mg124_512x1024_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d16-mg124_512x1024_40k_cityscapes/deeplabv3plus_r101-d16-mg124_512x1024_40k_cityscapes_20200908_005644-cf9ce186.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d16-mg124_512x1024_40k_cityscapes/deeplabv3plus_r101-d16-mg124_512x1024_40k_cityscapes-20200908_005644.log.json) |
+| DeepLabV3+ | R-101-D16-MG124 | 512x1024  |   80000 | 9.9      | -              | 79.90 |         81.33 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r101-d16-mg124_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d16-mg124_512x1024_80k_cityscapes/deeplabv3plus_r101-d16-mg124_512x1024_80k_cityscapes_20200908_005644-ee6158e0.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d16-mg124_512x1024_80k_cityscapes/deeplabv3plus_r101-d16-mg124_512x1024_80k_cityscapes-20200908_005644.log.json) |
+| DeepLabV3+ | R-18b-D8        | 512x1024  |   80000 | 2.1      | 14.95          | 75.87 |         77.52 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r18b-d8_512x1024_80k_cityscapes.py)        | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18b-d8_512x1024_80k_cityscapes/deeplabv3plus_r18b-d8_512x1024_80k_cityscapes_20201226_090828-e451abd9.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18b-d8_512x1024_80k_cityscapes/deeplabv3plus_r18b-d8_512x1024_80k_cityscapes-20201226_090828.log.json)                             |
+| DeepLabV3+ | R-50b-D8        | 512x1024  |   80000 | 7.4      | 3.94           | 80.28 |         81.44 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r50b-d8_512x1024_80k_cityscapes.py)        | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50b-d8_512x1024_80k_cityscapes/deeplabv3plus_r50b-d8_512x1024_80k_cityscapes_20201225_213645-a97e4e43.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50b-d8_512x1024_80k_cityscapes/deeplabv3plus_r50b-d8_512x1024_80k_cityscapes-20201225_213645.log.json)                             |
+| DeepLabV3+ | R-101b-D8       | 512x1024  |   80000 | 10.9     | 2.60           | 80.16 |         81.41 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r101b-d8_512x1024_80k_cityscapes.py)       | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101b-d8_512x1024_80k_cityscapes/deeplabv3plus_r101b-d8_512x1024_80k_cityscapes_20201226_190843-9c3c93a4.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101b-d8_512x1024_80k_cityscapes/deeplabv3plus_r101b-d8_512x1024_80k_cityscapes-20201226_190843.log.json)                         |
+| DeepLabV3+ | R-18b-D8        | 769x769   |   80000 | 2.4      | 5.96           | 76.36 |         78.24 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r18b-d8_769x769_80k_cityscapes.py)         | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18b-d8_769x769_80k_cityscapes/deeplabv3plus_r18b-d8_769x769_80k_cityscapes_20201226_151312-2c868aff.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18b-d8_769x769_80k_cityscapes/deeplabv3plus_r18b-d8_769x769_80k_cityscapes-20201226_151312.log.json)                                 |
+| DeepLabV3+ | R-50b-D8        | 769x769   |   80000 | 8.4      | 1.72           | 79.41 |         80.56 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r50b-d8_769x769_80k_cityscapes.py)         | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50b-d8_769x769_80k_cityscapes/deeplabv3plus_r50b-d8_769x769_80k_cityscapes_20201225_224655-8b596d1c.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50b-d8_769x769_80k_cityscapes/deeplabv3plus_r50b-d8_769x769_80k_cityscapes-20201225_224655.log.json)                                 |
+| DeepLabV3+ | R-101b-D8       | 769x769   |   80000 | 12.3     | 1.10           | 79.88 |         81.46 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r101b-d8_769x769_80k_cityscapes.py)        | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101b-d8_769x769_80k_cityscapes/deeplabv3plus_r101b-d8_769x769_80k_cityscapes_20201226_205041-227cdf7c.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101b-d8_769x769_80k_cityscapes/deeplabv3plus_r101b-d8_769x769_80k_cityscapes-20201226_205041.log.json)                             |
+
+[1] The training of the model is sensitive to random seed, and the seed to train it is 1111.
+
+### ADE20K
+
+| Method     | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                                | download                                                                                                                                                                                                                                                                                                                                                                               |
+| ---------- | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| DeepLabV3+ | R-50-D8  | 512x512   |   80000 | 10.6     | 21.01          | 42.72 |         43.75 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r50-d8_512x512_80k_ade20k.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_80k_ade20k/deeplabv3plus_r50-d8_512x512_80k_ade20k_20200614_185028-bf1400d8.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_80k_ade20k/deeplabv3plus_r50-d8_512x512_80k_ade20k_20200614_185028.log.json)         |
+| DeepLabV3+ | R-101-D8 | 512x512   |   80000 | 14.1     | 14.16          | 44.60 |         46.06 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r101-d8_512x512_80k_ade20k.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_80k_ade20k/deeplabv3plus_r101-d8_512x512_80k_ade20k_20200615_014139-d5730af7.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_80k_ade20k/deeplabv3plus_r101-d8_512x512_80k_ade20k_20200615_014139.log.json)     |
+| DeepLabV3+ | R-50-D8  | 512x512   |  160000 | -        | -              | 43.95 |         44.93 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r50-d8_512x512_160k_ade20k.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_160k_ade20k/deeplabv3plus_r50-d8_512x512_160k_ade20k_20200615_124504-6135c7e0.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_160k_ade20k/deeplabv3plus_r50-d8_512x512_160k_ade20k_20200615_124504.log.json)     |
+| DeepLabV3+ | R-101-D8 | 512x512   |  160000 | -        | -              | 45.47 |         46.35 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r101-d8_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_160k_ade20k/deeplabv3plus_r101-d8_512x512_160k_ade20k_20200615_123232-38ed86bb.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_160k_ade20k/deeplabv3plus_r101-d8_512x512_160k_ade20k_20200615_123232.log.json) |
+
+### Pascal VOC 2012 + Aug
+
+| Method     | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                                 | download                                                                                                                                                                                                                                                                                                                                                                                   |
+| ---------- | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | -------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
+| DeepLabV3+ | R-50-D8  | 512x512   |   20000 | 7.6      | 21             | 75.93 |         77.50 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r50-d8_512x512_20k_voc12aug.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_20k_voc12aug/deeplabv3plus_r50-d8_512x512_20k_voc12aug_20200617_102323-aad58ef1.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_20k_voc12aug/deeplabv3plus_r50-d8_512x512_20k_voc12aug_20200617_102323.log.json)     |
+| DeepLabV3+ | R-101-D8 | 512x512   |   20000 | 11       | 13.88          | 77.22 |         78.59 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r101-d8_512x512_20k_voc12aug.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_20k_voc12aug/deeplabv3plus_r101-d8_512x512_20k_voc12aug_20200617_102345-c7ff3d56.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_20k_voc12aug/deeplabv3plus_r101-d8_512x512_20k_voc12aug_20200617_102345.log.json) |
+| DeepLabV3+ | R-50-D8  | 512x512   |   40000 | -        | -              | 76.81 |         77.57 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r50-d8_512x512_40k_voc12aug.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_40k_voc12aug/deeplabv3plus_r50-d8_512x512_40k_voc12aug_20200613_161759-e1b43aa9.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_40k_voc12aug/deeplabv3plus_r50-d8_512x512_40k_voc12aug_20200613_161759.log.json)     |
+| DeepLabV3+ | R-101-D8 | 512x512   |   40000 | -        | -              | 78.62 |         79.53 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r101-d8_512x512_40k_voc12aug.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_40k_voc12aug/deeplabv3plus_r101-d8_512x512_40k_voc12aug_20200613_205333-faf03387.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_40k_voc12aug/deeplabv3plus_r101-d8_512x512_40k_voc12aug_20200613_205333.log.json) |
+
+### Pascal Context
+
+| Method     | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                                       | download                                                                                                                                                                                                                                                                                                                                                                                                           |
+| ---------- | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | -------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
+| DeepLabV3+ | R-101-D8 | 480x480   |   40000 | -        | 9.09           | 47.30 |         48.47 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r101-d8_480x480_40k_pascal_context.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_480x480_40k_pascal_context/deeplabv3plus_r101-d8_480x480_40k_pascal_context_20200911_165459-d3c8a29e.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_480x480_40k_pascal_context/deeplabv3plus_r101-d8_480x480_40k_pascal_context-20200911_165459.log.json) |
+| DeepLabV3+ | R-101-D8 | 480x480   |   80000 | -        | -              | 47.23 |         48.26 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r101-d8_480x480_80k_pascal_context.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_480x480_80k_pascal_context/deeplabv3plus_r101-d8_480x480_80k_pascal_context_20200911_155322-145d3ee8.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_480x480_80k_pascal_context/deeplabv3plus_r101-d8_480x480_80k_pascal_context-20200911_155322.log.json) |
+
+### Pascal Context 59
+
+| Method     | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                                       | download                                                                                                                                                                                                                                                                                                                                                                                                           |
+| ---------- | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | -------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
+| DeepLabV3+ | R-101-D8 | 480x480   |   40000 | -        | -           | 52.86 |         54.54 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r101-d8_480x480_40k_pascal_context_59.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_480x480_40k_pascal_context_59/deeplabv3plus_r101-d8_480x480_40k_pascal_context_59_20210416_111233-ed937f15.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_480x480_40k_pascal_context_59/deeplabv3plus_r101-d8_480x480_40k_pascal_context_59-20210416_111233.log.json) |
+| DeepLabV3+ | R-101-D8 | 480x480   |   80000 | -        | -              | 53.2 |         54.67 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r101-d8_480x480_80k_pascal_context_59.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_480x480_80k_pascal_context_59/deeplabv3plus_r101-d8_480x480_80k_pascal_context_59_20210416_111127-7ca0331d.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_480x480_80k_pascal_context_59/deeplabv3plus_r101-d8_480x480_80k_pascal_context_59-20210416_111127.log.json) |
+
+### LoveDA
+
+| Method     | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                                 | download                                                                                                                                                                                                                                                                                                                                                                                   |
+| ---------- | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | -------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
+| DeepLabV3+ | R-18-D8  | 512x512   |   80000 | 1.93      | 25.57             | 50.28 |         50.47 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r18-d8_512x512_80k_loveda.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_512x512_80k_loveda/deeplabv3plus_r18-d8_512x512_80k_loveda_20211104_132800-ce0fa0ca.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_512x512_80k_loveda/deeplabv3plus_r18-d8_512x512_80k_loveda_20211104_132800.log.json)     |
+| DeepLabV3+ | R-50-D8  | 512x512   |   80000 | 7.37      | 6.00             | 50.99 |         50.65 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r50-d8_512x512_80k_loveda.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_80k_loveda/deeplabv3plus_r50-d8_512x512_80k_loveda_20211105_080442-f0720392.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_80k_loveda/deeplabv3plus_r50-d8_512x512_80k_loveda_20211105_080442.log.json)     |
+| DeepLabV3+ | R-101-D8 | 512x512   |   80000 | 10.84       | 4.33          | 51.47 |         51.32 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r101-d8_512x512_80k_loveda.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_80k_loveda/deeplabv3plus_r101-d8_512x512_80k_loveda_20211105_110759-4c1f297e.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_80k_loveda/deeplabv3plus_r101-d8_512x512_80k_loveda_20211105_110759.log.json) |
+
+### Potsdam
+
+| Method     | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                                 | download                                                                                                                                                                                                                                                                                                                                                                                   |
+| ---------- | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | -------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
+| DeepLabV3+ | R-18-D8  | 512x512   |   80000 | 1.91      | 81.68             | 77.09 |         78.44 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r18-d8_512x512_80k_potsdam.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_512x512_80k_potsdam/deeplabv3plus_r18-d8_512x512_80k_potsdam_20211219_020601-75fd5bc3.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_512x512_80k_potsdam/deeplabv3plus_r18-d8_512x512_80k_potsdam_20211219_020601.log.json)     |
+| DeepLabV3+ | R-50-D8  | 512x512   |   80000 | 7.36      | 26.44             | 78.33 |         79.27 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r50-d8_512x512_80k_potsdam.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_80k_potsdam/deeplabv3plus_r50-d8_512x512_80k_potsdam_20211219_031508-7e7a2b24.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_80k_potsdam/deeplabv3plus_r50-d8_512x512_80k_potsdam_20211219_031508.log.json)     |
+| DeepLabV3+ | R-101-D8 | 512x512   |   80000 | 10.83       | 17.56          | 78.7 |         79.47 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r101-d8_512x512_80k_potsdam.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_80k_potsdam/deeplabv3plus_r101-d8_512x512_80k_potsdam_20211219_031508-8b112708.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_80k_potsdam/deeplabv3plus_r101-d8_512x512_80k_potsdam_20211219_031508.log.json) |
+
+### Vaihingen
+
+| Method     | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                                 | download                                                                                                                                                                                                                                                                                                                                                                                   |
+| ---------- | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | -------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
+| DeepLabV3+ | R-18-D8  | 512x512   |   80000 | 1.91      | 72.79             | 72.50 |         74.13 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r18-d8_4x4_512x512_80k_vaihingen.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_4x4_512x512_80k_vaihingen/deeplabv3plus_r18-d8_4x4_512x512_80k_vaihingen_20211231_230805-7626a263.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_4x4_512x512_80k_vaihingen/deeplabv3plus_r18-d8_4x4_512x512_80k_vaihingen_20211231_230805.log.json)     |
+| DeepLabV3+ | R-50-D8  | 512x512   |   80000 | 7.36      | 26.91             | 73.97 |         75.05 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r50-d8_4x4_512x512_80k_vaihingen.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_4x4_512x512_80k_vaihingen/deeplabv3plus_r50-d8_4x4_512x512_80k_vaihingen_20211231_230816-5040938d.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_4x4_512x512_80k_vaihingen/deeplabv3plus_r50-d8_4x4_512x512_80k_vaihingen_20211231_230816.log.json)     |
+| DeepLabV3+ | R-101-D8 | 512x512   |   80000 | 10.83       | 18.59          | 73.06 |         74.14 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r101-d8_4x4_512x512_80k_vaihingen.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_4x4_512x512_80k_vaihingen/deeplabv3plus_r101-d8_4x4_512x512_80k_vaihingen_20211231_230816-8a095afa.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_4x4_512x512_80k_vaihingen/deeplabv3plus_r101-d8_4x4_512x512_80k_vaihingen_20211231_230816.log.json) |
+
+### iSAID
+
+| Method     | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                                 | download                                                                                                                                                                                                                                                                                                                                                                                   |
+| ---------- | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | -------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
+| DeepLabV3+ | R-18-D8  | 896x896   |   80000 | 6.19      | 24.81             | 61.35 |         62.61 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r18-d8_4x4_896x896_80k_isaid.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_4x4_896x896_80k_isaid/deeplabv3plus_r18-d8_4x4_896x896_80k_isaid_20220110_180526-7059991d.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_4x4_896x896_80k_isaid/deeplabv3plus_r18-d8_4x4_896x896_80k_isaid_20220110_180526.log.json)     |
+| DeepLabV3+ | R-50-D8  | 896x896   |   80000 | 21.45      | 8.42             | 67.06 |         68.02 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r50-d8_4x4_896x896_80k_isaid.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_4x4_896x896_80k_isaid/deeplabv3plus_r50-d8_4x4_896x896_80k_isaid_20220110_180526-598be439.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_4x4_896x896_80k_isaid/deeplabv3plus_r50-d8_4x4_896x896_80k_isaid_20220110_180526.log.json)     |
+
+Note:
+
+- `D-8`/`D-16` here corresponding to the output stride 8/16 setting for DeepLab series.
+- `MG-124` stands for multi-grid dilation in the last stage of ResNet.
+- `FP16` means Mixed Precision (FP16) is adopted in training.
+- `896x896` is the Crop Size of iSAID dataset, which is followed by the implementation of [PointFlow: Flowing Semantics Through Points for Aerial Image Segmentation](https://arxiv.org/pdf/2103.06564.pdf)

EgoHOS/raw/script/mmsegmentation/configs/deeplabv3plus/deeplabv3plus.yml

@@ -0,0 +1,850 @@
+Collections:
+- Name: DeepLabV3+
+  Metadata:
+    Training Data:
+    - Cityscapes
+    - ADE20K
+    - Pascal VOC 2012 + Aug
+    - Pascal Context
+    - Pascal Context 59
+    - LoveDA
+    - Potsdam
+    - Vaihingen
+    - iSAID
+  Paper:
+    URL: https://arxiv.org/abs/1802.02611
+    Title: Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
+  README: configs/deeplabv3plus/README.md
+  Code:
+    URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/sep_aspp_head.py#L30
+    Version: v0.17.0
+  Converted From:
+    Code: https://github.com/tensorflow/models/tree/master/research/deeplab
+Models:
+- Name: deeplabv3plus_r50-d8_512x1024_40k_cityscapes
+  In Collection: DeepLabV3+
+  Metadata:
+    backbone: R-50-D8
+    crop size: (512,1024)
+    lr schd: 40000
+    inference time (ms/im):
+    - value: 253.81
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,1024)
+    Training Memory (GB): 7.5
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 79.61
+      mIoU(ms+flip): 81.01
+  Config: configs/deeplabv3plus/deeplabv3plus_r50-d8_512x1024_40k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x1024_40k_cityscapes/deeplabv3plus_r50-d8_512x1024_40k_cityscapes_20200605_094610-d222ffcd.pth
+- Name: deeplabv3plus_r101-d8_512x1024_40k_cityscapes
+  In Collection: DeepLabV3+
+  Metadata:
+    backbone: R-101-D8
+    crop size: (512,1024)
+    lr schd: 40000
+    inference time (ms/im):
+    - value: 384.62
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,1024)
+    Training Memory (GB): 11.0
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 80.21
+      mIoU(ms+flip): 81.82
+  Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_512x1024_40k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x1024_40k_cityscapes/deeplabv3plus_r101-d8_512x1024_40k_cityscapes_20200605_094614-3769eecf.pth
+- Name: deeplabv3plus_r50-d8_769x769_40k_cityscapes
+  In Collection: DeepLabV3+
+  Metadata:
+    backbone: R-50-D8
+    crop size: (769,769)
+    lr schd: 40000
+    inference time (ms/im):
+    - value: 581.4
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (769,769)
+    Training Memory (GB): 8.5
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 78.97
+      mIoU(ms+flip): 80.46
+  Config: configs/deeplabv3plus/deeplabv3plus_r50-d8_769x769_40k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_769x769_40k_cityscapes/deeplabv3plus_r50-d8_769x769_40k_cityscapes_20200606_114143-1dcb0e3c.pth
+- Name: deeplabv3plus_r101-d8_769x769_40k_cityscapes
+  In Collection: DeepLabV3+
+  Metadata:
+    backbone: R-101-D8
+    crop size: (769,769)
+    lr schd: 40000
+    inference time (ms/im):
+    - value: 869.57
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (769,769)
+    Training Memory (GB): 12.5
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 79.46
+      mIoU(ms+flip): 80.5
+  Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_769x769_40k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_769x769_40k_cityscapes/deeplabv3plus_r101-d8_769x769_40k_cityscapes_20200606_114304-ff414b9e.pth
+- Name: deeplabv3plus_r18-d8_512x1024_80k_cityscapes
+  In Collection: DeepLabV3+
+  Metadata:
+    backbone: R-18-D8
+    crop size: (512,1024)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 70.08
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,1024)
+    Training Memory (GB): 2.2
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 76.89
+      mIoU(ms+flip): 78.76
+  Config: configs/deeplabv3plus/deeplabv3plus_r18-d8_512x1024_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_512x1024_80k_cityscapes/deeplabv3plus_r18-d8_512x1024_80k_cityscapes_20201226_080942-cff257fe.pth
+- Name: deeplabv3plus_r50-d8_512x1024_80k_cityscapes
+  In Collection: DeepLabV3+
+  Metadata:
+    backbone: R-50-D8
+    crop size: (512,1024)
+    lr schd: 80000
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 80.09
+      mIoU(ms+flip): 81.13
+  Config: configs/deeplabv3plus/deeplabv3plus_r50-d8_512x1024_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x1024_80k_cityscapes/deeplabv3plus_r50-d8_512x1024_80k_cityscapes_20200606_114049-f9fb496d.pth
+- Name: deeplabv3plus_r101-d8_512x1024_80k_cityscapes
+  In Collection: DeepLabV3+
+  Metadata:
+    backbone: R-101-D8
+    crop size: (512,1024)
+    lr schd: 80000
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 80.97
+      mIoU(ms+flip): 82.03
+  Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_512x1024_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x1024_80k_cityscapes/deeplabv3plus_r101-d8_512x1024_80k_cityscapes_20200606_114143-068fcfe9.pth
+- Name: deeplabv3plus_r101-d8_fp16_512x1024_80k_cityscapes
+  In Collection: DeepLabV3+
+  Metadata:
+    backbone: R-101-D8
+    crop size: (512,1024)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 127.06
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP16
+      resolution: (512,1024)
+    Training Memory (GB): 6.35
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 80.46
+  Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_fp16_512x1024_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_fp16_512x1024_80k_cityscapes/deeplabv3plus_r101-d8_fp16_512x1024_80k_cityscapes_20200717_230920-f1104f4b.pth
+- Name: deeplabv3plus_r18-d8_769x769_80k_cityscapes
+  In Collection: DeepLabV3+
+  Metadata:
+    backbone: R-18-D8
+    crop size: (769,769)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 174.22
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (769,769)
+    Training Memory (GB): 2.5
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 76.26
+      mIoU(ms+flip): 77.91
+  Config: configs/deeplabv3plus/deeplabv3plus_r18-d8_769x769_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_769x769_80k_cityscapes/deeplabv3plus_r18-d8_769x769_80k_cityscapes_20201226_083346-f326e06a.pth
+- Name: deeplabv3plus_r50-d8_769x769_80k_cityscapes
+  In Collection: DeepLabV3+
+  Metadata:
+    backbone: R-50-D8
+    crop size: (769,769)
+    lr schd: 80000
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 79.83
+      mIoU(ms+flip): 81.48
+  Config: configs/deeplabv3plus/deeplabv3plus_r50-d8_769x769_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_769x769_80k_cityscapes/deeplabv3plus_r50-d8_769x769_80k_cityscapes_20200606_210233-0e9dfdc4.pth
+- Name: deeplabv3plus_r101-d8_769x769_80k_cityscapes
+  In Collection: DeepLabV3+
+  Metadata:
+    backbone: R-101-D8
+    crop size: (769,769)
+    lr schd: 80000
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 80.65
+      mIoU(ms+flip): 81.47
+  Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_769x769_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_769x769_80k_cityscapes/deeplabv3plus_r101-d8_769x769_80k_cityscapes_20220406_154720-dfcc0b68.pth
+- Name: deeplabv3plus_r101-d16-mg124_512x1024_40k_cityscapes
+  In Collection: DeepLabV3+
+  Metadata:
+    backbone: R-101-D16-MG124
+    crop size: (512,1024)
+    lr schd: 40000
+    inference time (ms/im):
+    - value: 133.69
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,1024)
+    Training Memory (GB): 5.8
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 79.09
+      mIoU(ms+flip): 80.36
+  Config: configs/deeplabv3plus/deeplabv3plus_r101-d16-mg124_512x1024_40k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d16-mg124_512x1024_40k_cityscapes/deeplabv3plus_r101-d16-mg124_512x1024_40k_cityscapes_20200908_005644-cf9ce186.pth
+- Name: deeplabv3plus_r101-d16-mg124_512x1024_80k_cityscapes
+  In Collection: DeepLabV3+
+  Metadata:
+    backbone: R-101-D16-MG124
+    crop size: (512,1024)
+    lr schd: 80000
+    Training Memory (GB): 9.9
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 79.9
+      mIoU(ms+flip): 81.33
+  Config: configs/deeplabv3plus/deeplabv3plus_r101-d16-mg124_512x1024_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d16-mg124_512x1024_80k_cityscapes/deeplabv3plus_r101-d16-mg124_512x1024_80k_cityscapes_20200908_005644-ee6158e0.pth
+- Name: deeplabv3plus_r18b-d8_512x1024_80k_cityscapes
+  In Collection: DeepLabV3+
+  Metadata:
+    backbone: R-18b-D8
+    crop size: (512,1024)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 66.89
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,1024)
+    Training Memory (GB): 2.1
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 75.87
+      mIoU(ms+flip): 77.52
+  Config: configs/deeplabv3plus/deeplabv3plus_r18b-d8_512x1024_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18b-d8_512x1024_80k_cityscapes/deeplabv3plus_r18b-d8_512x1024_80k_cityscapes_20201226_090828-e451abd9.pth
+- Name: deeplabv3plus_r50b-d8_512x1024_80k_cityscapes
+  In Collection: DeepLabV3+
+  Metadata:
+    backbone: R-50b-D8
+    crop size: (512,1024)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 253.81
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,1024)
+    Training Memory (GB): 7.4
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 80.28
+      mIoU(ms+flip): 81.44
+  Config: configs/deeplabv3plus/deeplabv3plus_r50b-d8_512x1024_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50b-d8_512x1024_80k_cityscapes/deeplabv3plus_r50b-d8_512x1024_80k_cityscapes_20201225_213645-a97e4e43.pth
+- Name: deeplabv3plus_r101b-d8_512x1024_80k_cityscapes
+  In Collection: DeepLabV3+
+  Metadata:
+    backbone: R-101b-D8
+    crop size: (512,1024)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 384.62
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,1024)
+    Training Memory (GB): 10.9
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 80.16
+      mIoU(ms+flip): 81.41
+  Config: configs/deeplabv3plus/deeplabv3plus_r101b-d8_512x1024_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101b-d8_512x1024_80k_cityscapes/deeplabv3plus_r101b-d8_512x1024_80k_cityscapes_20201226_190843-9c3c93a4.pth
+- Name: deeplabv3plus_r18b-d8_769x769_80k_cityscapes
+  In Collection: DeepLabV3+
+  Metadata:
+    backbone: R-18b-D8
+    crop size: (769,769)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 167.79
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (769,769)
+    Training Memory (GB): 2.4
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 76.36
+      mIoU(ms+flip): 78.24
+  Config: configs/deeplabv3plus/deeplabv3plus_r18b-d8_769x769_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18b-d8_769x769_80k_cityscapes/deeplabv3plus_r18b-d8_769x769_80k_cityscapes_20201226_151312-2c868aff.pth
+- Name: deeplabv3plus_r50b-d8_769x769_80k_cityscapes
+  In Collection: DeepLabV3+
+  Metadata:
+    backbone: R-50b-D8
+    crop size: (769,769)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 581.4
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (769,769)
+    Training Memory (GB): 8.4
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 79.41
+      mIoU(ms+flip): 80.56
+  Config: configs/deeplabv3plus/deeplabv3plus_r50b-d8_769x769_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50b-d8_769x769_80k_cityscapes/deeplabv3plus_r50b-d8_769x769_80k_cityscapes_20201225_224655-8b596d1c.pth
+- Name: deeplabv3plus_r101b-d8_769x769_80k_cityscapes
+  In Collection: DeepLabV3+
+  Metadata:
+    backbone: R-101b-D8
+    crop size: (769,769)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 909.09
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (769,769)
+    Training Memory (GB): 12.3
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 79.88
+      mIoU(ms+flip): 81.46
+  Config: configs/deeplabv3plus/deeplabv3plus_r101b-d8_769x769_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101b-d8_769x769_80k_cityscapes/deeplabv3plus_r101b-d8_769x769_80k_cityscapes_20201226_205041-227cdf7c.pth
+- Name: deeplabv3plus_r50-d8_512x512_80k_ade20k
+  In Collection: DeepLabV3+
+  Metadata:
+    backbone: R-50-D8
+    crop size: (512,512)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 47.6
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    Training Memory (GB): 10.6
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 42.72
+      mIoU(ms+flip): 43.75
+  Config: configs/deeplabv3plus/deeplabv3plus_r50-d8_512x512_80k_ade20k.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_80k_ade20k/deeplabv3plus_r50-d8_512x512_80k_ade20k_20200614_185028-bf1400d8.pth
+- Name: deeplabv3plus_r101-d8_512x512_80k_ade20k
+  In Collection: DeepLabV3+
+  Metadata:
+    backbone: R-101-D8
+    crop size: (512,512)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 70.62
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    Training Memory (GB): 14.1
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 44.6
+      mIoU(ms+flip): 46.06
+  Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_512x512_80k_ade20k.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_80k_ade20k/deeplabv3plus_r101-d8_512x512_80k_ade20k_20200615_014139-d5730af7.pth
+- Name: deeplabv3plus_r50-d8_512x512_160k_ade20k
+  In Collection: DeepLabV3+
+  Metadata:
+    backbone: R-50-D8
+    crop size: (512,512)
+    lr schd: 160000
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 43.95
+      mIoU(ms+flip): 44.93
+  Config: configs/deeplabv3plus/deeplabv3plus_r50-d8_512x512_160k_ade20k.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_160k_ade20k/deeplabv3plus_r50-d8_512x512_160k_ade20k_20200615_124504-6135c7e0.pth
+- Name: deeplabv3plus_r101-d8_512x512_160k_ade20k
+  In Collection: DeepLabV3+
+  Metadata:
+    backbone: R-101-D8
+    crop size: (512,512)
+    lr schd: 160000
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 45.47
+      mIoU(ms+flip): 46.35
+  Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_512x512_160k_ade20k.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_160k_ade20k/deeplabv3plus_r101-d8_512x512_160k_ade20k_20200615_123232-38ed86bb.pth
+- Name: deeplabv3plus_r50-d8_512x512_20k_voc12aug
+  In Collection: DeepLabV3+
+  Metadata:
+    backbone: R-50-D8
+    crop size: (512,512)
+    lr schd: 20000
+    inference time (ms/im):
+    - value: 47.62
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    Training Memory (GB): 7.6
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: Pascal VOC 2012 + Aug
+    Metrics:
+      mIoU: 75.93
+      mIoU(ms+flip): 77.5
+  Config: configs/deeplabv3plus/deeplabv3plus_r50-d8_512x512_20k_voc12aug.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_20k_voc12aug/deeplabv3plus_r50-d8_512x512_20k_voc12aug_20200617_102323-aad58ef1.pth
+- Name: deeplabv3plus_r101-d8_512x512_20k_voc12aug
+  In Collection: DeepLabV3+
+  Metadata:
+    backbone: R-101-D8
+    crop size: (512,512)
+    lr schd: 20000
+    inference time (ms/im):
+    - value: 72.05
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    Training Memory (GB): 11.0
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: Pascal VOC 2012 + Aug
+    Metrics:
+      mIoU: 77.22
+      mIoU(ms+flip): 78.59
+  Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_512x512_20k_voc12aug.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_20k_voc12aug/deeplabv3plus_r101-d8_512x512_20k_voc12aug_20200617_102345-c7ff3d56.pth
+- Name: deeplabv3plus_r50-d8_512x512_40k_voc12aug
+  In Collection: DeepLabV3+
+  Metadata:
+    backbone: R-50-D8
+    crop size: (512,512)
+    lr schd: 40000
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: Pascal VOC 2012 + Aug
+    Metrics:
+      mIoU: 76.81
+      mIoU(ms+flip): 77.57
+  Config: configs/deeplabv3plus/deeplabv3plus_r50-d8_512x512_40k_voc12aug.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_40k_voc12aug/deeplabv3plus_r50-d8_512x512_40k_voc12aug_20200613_161759-e1b43aa9.pth
+- Name: deeplabv3plus_r101-d8_512x512_40k_voc12aug
+  In Collection: DeepLabV3+
+  Metadata:
+    backbone: R-101-D8
+    crop size: (512,512)
+    lr schd: 40000
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: Pascal VOC 2012 + Aug
+    Metrics:
+      mIoU: 78.62
+      mIoU(ms+flip): 79.53
+  Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_512x512_40k_voc12aug.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_40k_voc12aug/deeplabv3plus_r101-d8_512x512_40k_voc12aug_20200613_205333-faf03387.pth
+- Name: deeplabv3plus_r101-d8_480x480_40k_pascal_context
+  In Collection: DeepLabV3+
+  Metadata:
+    backbone: R-101-D8
+    crop size: (480,480)
+    lr schd: 40000
+    inference time (ms/im):
+    - value: 110.01
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (480,480)
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: Pascal Context
+    Metrics:
+      mIoU: 47.3
+      mIoU(ms+flip): 48.47
+  Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_480x480_40k_pascal_context.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_480x480_40k_pascal_context/deeplabv3plus_r101-d8_480x480_40k_pascal_context_20200911_165459-d3c8a29e.pth
+- Name: deeplabv3plus_r101-d8_480x480_80k_pascal_context
+  In Collection: DeepLabV3+
+  Metadata:
+    backbone: R-101-D8
+    crop size: (480,480)
+    lr schd: 80000
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: Pascal Context
+    Metrics:
+      mIoU: 47.23
+      mIoU(ms+flip): 48.26
+  Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_480x480_80k_pascal_context.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_480x480_80k_pascal_context/deeplabv3plus_r101-d8_480x480_80k_pascal_context_20200911_155322-145d3ee8.pth
+- Name: deeplabv3plus_r101-d8_480x480_40k_pascal_context_59
+  In Collection: DeepLabV3+
+  Metadata:
+    backbone: R-101-D8
+    crop size: (480,480)
+    lr schd: 40000
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: Pascal Context 59
+    Metrics:
+      mIoU: 52.86
+      mIoU(ms+flip): 54.54
+  Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_480x480_40k_pascal_context_59.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_480x480_40k_pascal_context_59/deeplabv3plus_r101-d8_480x480_40k_pascal_context_59_20210416_111233-ed937f15.pth
+- Name: deeplabv3plus_r101-d8_480x480_80k_pascal_context_59
+  In Collection: DeepLabV3+
+  Metadata:
+    backbone: R-101-D8
+    crop size: (480,480)
+    lr schd: 80000
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: Pascal Context 59
+    Metrics:
+      mIoU: 53.2
+      mIoU(ms+flip): 54.67
+  Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_480x480_80k_pascal_context_59.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_480x480_80k_pascal_context_59/deeplabv3plus_r101-d8_480x480_80k_pascal_context_59_20210416_111127-7ca0331d.pth
+- Name: deeplabv3plus_r18-d8_512x512_80k_loveda
+  In Collection: DeepLabV3+
+  Metadata:
+    backbone: R-18-D8
+    crop size: (512,512)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 39.11
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    Training Memory (GB): 1.93
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: LoveDA
+    Metrics:
+      mIoU: 50.28
+      mIoU(ms+flip): 50.47
+  Config: configs/deeplabv3plus/deeplabv3plus_r18-d8_512x512_80k_loveda.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_512x512_80k_loveda/deeplabv3plus_r18-d8_512x512_80k_loveda_20211104_132800-ce0fa0ca.pth
+- Name: deeplabv3plus_r50-d8_512x512_80k_loveda
+  In Collection: DeepLabV3+
+  Metadata:
+    backbone: R-50-D8
+    crop size: (512,512)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 166.67
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    Training Memory (GB): 7.37
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: LoveDA
+    Metrics:
+      mIoU: 50.99
+      mIoU(ms+flip): 50.65
+  Config: configs/deeplabv3plus/deeplabv3plus_r50-d8_512x512_80k_loveda.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_80k_loveda/deeplabv3plus_r50-d8_512x512_80k_loveda_20211105_080442-f0720392.pth
+- Name: deeplabv3plus_r101-d8_512x512_80k_loveda
+  In Collection: DeepLabV3+
+  Metadata:
+    backbone: R-101-D8
+    crop size: (512,512)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 230.95
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    Training Memory (GB): 10.84
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: LoveDA
+    Metrics:
+      mIoU: 51.47
+      mIoU(ms+flip): 51.32
+  Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_512x512_80k_loveda.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_80k_loveda/deeplabv3plus_r101-d8_512x512_80k_loveda_20211105_110759-4c1f297e.pth
+- Name: deeplabv3plus_r18-d8_512x512_80k_potsdam
+  In Collection: DeepLabV3+
+  Metadata:
+    backbone: R-18-D8
+    crop size: (512,512)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 12.24
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    Training Memory (GB): 1.91
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: Potsdam
+    Metrics:
+      mIoU: 77.09
+      mIoU(ms+flip): 78.44
+  Config: configs/deeplabv3plus/deeplabv3plus_r18-d8_512x512_80k_potsdam.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_512x512_80k_potsdam/deeplabv3plus_r18-d8_512x512_80k_potsdam_20211219_020601-75fd5bc3.pth
+- Name: deeplabv3plus_r50-d8_512x512_80k_potsdam
+  In Collection: DeepLabV3+
+  Metadata:
+    backbone: R-50-D8
+    crop size: (512,512)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 37.82
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    Training Memory (GB): 7.36
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: Potsdam
+    Metrics:
+      mIoU: 78.33
+      mIoU(ms+flip): 79.27
+  Config: configs/deeplabv3plus/deeplabv3plus_r50-d8_512x512_80k_potsdam.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_80k_potsdam/deeplabv3plus_r50-d8_512x512_80k_potsdam_20211219_031508-7e7a2b24.pth
+- Name: deeplabv3plus_r101-d8_512x512_80k_potsdam
+  In Collection: DeepLabV3+
+  Metadata:
+    backbone: R-101-D8
+    crop size: (512,512)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 56.95
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    Training Memory (GB): 10.83
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: Potsdam
+    Metrics:
+      mIoU: 78.7
+      mIoU(ms+flip): 79.47
+  Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_512x512_80k_potsdam.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_80k_potsdam/deeplabv3plus_r101-d8_512x512_80k_potsdam_20211219_031508-8b112708.pth
+- Name: deeplabv3plus_r18-d8_4x4_512x512_80k_vaihingen
+  In Collection: DeepLabV3+
+  Metadata:
+    backbone: R-18-D8
+    crop size: (512,512)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 13.74
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    Training Memory (GB): 1.91
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: Vaihingen
+    Metrics:
+      mIoU: 72.5
+      mIoU(ms+flip): 74.13
+  Config: configs/deeplabv3plus/deeplabv3plus_r18-d8_4x4_512x512_80k_vaihingen.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_4x4_512x512_80k_vaihingen/deeplabv3plus_r18-d8_4x4_512x512_80k_vaihingen_20211231_230805-7626a263.pth
+- Name: deeplabv3plus_r50-d8_4x4_512x512_80k_vaihingen
+  In Collection: DeepLabV3+
+  Metadata:
+    backbone: R-50-D8
+    crop size: (512,512)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 37.16
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    Training Memory (GB): 7.36
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: Vaihingen
+    Metrics:
+      mIoU: 73.97
+      mIoU(ms+flip): 75.05
+  Config: configs/deeplabv3plus/deeplabv3plus_r50-d8_4x4_512x512_80k_vaihingen.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_4x4_512x512_80k_vaihingen/deeplabv3plus_r50-d8_4x4_512x512_80k_vaihingen_20211231_230816-5040938d.pth
+- Name: deeplabv3plus_r101-d8_4x4_512x512_80k_vaihingen
+  In Collection: DeepLabV3+
+  Metadata:
+    backbone: R-101-D8
+    crop size: (512,512)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 53.79
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    Training Memory (GB): 10.83
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: Vaihingen
+    Metrics:
+      mIoU: 73.06
+      mIoU(ms+flip): 74.14
+  Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_4x4_512x512_80k_vaihingen.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_4x4_512x512_80k_vaihingen/deeplabv3plus_r101-d8_4x4_512x512_80k_vaihingen_20211231_230816-8a095afa.pth
+- Name: deeplabv3plus_r18-d8_4x4_896x896_80k_isaid
+  In Collection: DeepLabV3+
+  Metadata:
+    backbone: R-18-D8
+    crop size: (896,896)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 40.31
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (896,896)
+    Training Memory (GB): 6.19
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: iSAID
+    Metrics:
+      mIoU: 61.35
+      mIoU(ms+flip): 62.61
+  Config: configs/deeplabv3plus/deeplabv3plus_r18-d8_4x4_896x896_80k_isaid.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_4x4_896x896_80k_isaid/deeplabv3plus_r18-d8_4x4_896x896_80k_isaid_20220110_180526-7059991d.pth
+- Name: deeplabv3plus_r50-d8_4x4_896x896_80k_isaid
+  In Collection: DeepLabV3+
+  Metadata:
+    backbone: R-50-D8
+    crop size: (896,896)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 118.76
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (896,896)
+    Training Memory (GB): 21.45
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: iSAID
+    Metrics:
+      mIoU: 67.06
+      mIoU(ms+flip): 68.02
+  Config: configs/deeplabv3plus/deeplabv3plus_r50-d8_4x4_896x896_80k_isaid.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_4x4_896x896_80k_isaid/deeplabv3plus_r50-d8_4x4_896x896_80k_isaid_20220110_180526-598be439.pth

EgoHOS/raw/script/mmsegmentation/configs/deeplabv3plus/deeplabv3plus_r101-d16-mg124_512x1024_40k_cityscapes.py

@@ -0,0 +1,11 @@
+_base_ = './deeplabv3plus_r50-d8_512x1024_40k_cityscapes.py'
+model = dict(
+    pretrained='open-mmlab://resnet101_v1c',
+    backbone=dict(
+        depth=101,
+        dilations=(1, 1, 1, 2),
+        strides=(1, 2, 2, 1),
+        multi_grid=(1, 2, 4)),
+    decode_head=dict(
+        dilations=(1, 6, 12, 18),
+        sampler=dict(type='OHEMPixelSampler', min_kept=100000)))

EgoHOS/raw/script/mmsegmentation/configs/deeplabv3plus/deeplabv3plus_r101-d16-mg124_512x1024_80k_cityscapes.py

@@ -0,0 +1,11 @@
+_base_ = './deeplabv3plus_r50-d8_512x1024_80k_cityscapes.py'
+model = dict(
+    pretrained='open-mmlab://resnet101_v1c',
+    backbone=dict(
+        depth=101,
+        dilations=(1, 1, 1, 2),
+        strides=(1, 2, 2, 1),
+        multi_grid=(1, 2, 4)),
+    decode_head=dict(
+        dilations=(1, 6, 12, 18),
+        sampler=dict(type='OHEMPixelSampler', min_kept=100000)))

EgoHOS/raw/script/mmsegmentation/configs/deeplabv3plus/deeplabv3plus_r101-d8_480x480_40k_pascal_context.py

@@ -0,0 +1,2 @@
+_base_ = './deeplabv3plus_r50-d8_480x480_40k_pascal_context.py'
+model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))

EgoHOS/raw/script/mmsegmentation/configs/deeplabv3plus/deeplabv3plus_r101-d8_480x480_40k_pascal_context_59.py

@@ -0,0 +1,2 @@
+_base_ = './deeplabv3plus_r50-d8_480x480_40k_pascal_context_59.py'
+model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))

EgoHOS/raw/script/mmsegmentation/configs/deeplabv3plus/deeplabv3plus_r101-d8_480x480_80k_pascal_context.py

@@ -0,0 +1,2 @@
+_base_ = './deeplabv3plus_r50-d8_480x480_80k_pascal_context.py'
+model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))

EgoHOS/raw/script/mmsegmentation/configs/deeplabv3plus/deeplabv3plus_r101-d8_480x480_80k_pascal_context_59.py

@@ -0,0 +1,2 @@
+_base_ = './deeplabv3plus_r50-d8_480x480_80k_pascal_context_59.py'
+model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))

EgoHOS/raw/script/mmsegmentation/configs/deeplabv3plus/deeplabv3plus_r101-d8_4x4_512x512_80k_vaihingen.py

@@ -0,0 +1,2 @@
+_base_ = './deeplabv3plus_r50-d8_4x4_512x512_80k_vaihingen.py'
+model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))

EgoHOS/raw/script/mmsegmentation/configs/deeplabv3plus/deeplabv3plus_r101-d8_512x1024_40k_cityscapes.py

@@ -0,0 +1,2 @@
+_base_ = './deeplabv3plus_r50-d8_512x1024_40k_cityscapes.py'
+model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))

EgoHOS/raw/script/mmsegmentation/configs/deeplabv3plus/deeplabv3plus_r101-d8_512x1024_80k_cityscapes.py

@@ -0,0 +1,2 @@
+_base_ = './deeplabv3plus_r50-d8_512x1024_80k_cityscapes.py'
+model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))

EgoHOS/raw/script/mmsegmentation/configs/deeplabv3plus/deeplabv3plus_r101-d8_512x512_160k_ade20k.py

@@ -0,0 +1,2 @@
+_base_ = './deeplabv3plus_r50-d8_512x512_160k_ade20k.py'
+model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))

EgoHOS/raw/script/mmsegmentation/configs/deeplabv3plus/deeplabv3plus_r101-d8_512x512_20k_voc12aug.py

@@ -0,0 +1,2 @@
+_base_ = './deeplabv3plus_r50-d8_512x512_20k_voc12aug.py'
+model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))

EgoHOS/raw/script/mmsegmentation/configs/deeplabv3plus/deeplabv3plus_r101-d8_512x512_40k_voc12aug.py

@@ -0,0 +1,2 @@
+_base_ = './deeplabv3plus_r50-d8_512x512_40k_voc12aug.py'
+model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))

EgoHOS/raw/script/mmsegmentation/configs/deeplabv3plus/deeplabv3plus_r101-d8_512x512_80k_ade20k.py

@@ -0,0 +1,2 @@
+_base_ = './deeplabv3plus_r50-d8_512x512_80k_ade20k.py'
+model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))

EgoHOS/raw/script/mmsegmentation/configs/deeplabv3plus/deeplabv3plus_r101-d8_512x512_80k_loveda.py

@@ -0,0 +1,6 @@
+_base_ = './deeplabv3plus_r50-d8_512x512_80k_loveda.py'
+model = dict(
+    backbone=dict(
+        depth=101,
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://resnet101_v1c')))

EgoHOS/raw/script/mmsegmentation/configs/deeplabv3plus/deeplabv3plus_r101-d8_512x512_80k_potsdam.py

@@ -0,0 +1,2 @@
+_base_ = './deeplabv3plus_r50-d8_512x512_80k_potsdam.py'
+model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))