|
Models: |
|
- Name: fcn_hr18s_4xb2-40k_cityscapes-512x1024 |
|
In Collection: FCN |
|
Results: |
|
Task: Semantic Segmentation |
|
Dataset: Cityscapes |
|
Metrics: |
|
mIoU: 73.86 |
|
mIoU(ms+flip): 75.91 |
|
Config: configs/hrnet/fcn_hr18s_4xb2-40k_cityscapes-512x1024.py |
|
Metadata: |
|
Training Data: Cityscapes |
|
Batch Size: 8 |
|
Architecture: |
|
- HRNetV2p-W18-Small |
|
- FCN |
|
Training Resources: 4x V100 GPUS |
|
Memory (GB): 1.7 |
|
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x1024_40k_cityscapes/fcn_hr18s_512x1024_40k_cityscapes_20200601_014216-93db27d0.pth |
|
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x1024_40k_cityscapes/fcn_hr18s_512x1024_40k_cityscapes_20200601_014216.log.json |
|
Paper: |
|
Title: Deep High-Resolution Representation Learning for Human Pose Estimation |
|
URL: https://arxiv.org/abs/1908.07919 |
|
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/hrnet.py#L218 |
|
Framework: PyTorch |
|
- Name: fcn_hr18_4xb2-40k_cityscapes-512x1024 |
|
In Collection: FCN |
|
Results: |
|
Task: Semantic Segmentation |
|
Dataset: Cityscapes |
|
Metrics: |
|
mIoU: 77.19 |
|
mIoU(ms+flip): 78.92 |
|
Config: configs/hrnet/fcn_hr18_4xb2-40k_cityscapes-512x1024.py |
|
Metadata: |
|
Training Data: Cityscapes |
|
Batch Size: 8 |
|
Architecture: |
|
- HRNetV2p-W18 |
|
- FCN |
|
Training Resources: 4x V100 GPUS |
|
Memory (GB): 2.9 |
|
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x1024_40k_cityscapes/fcn_hr18_512x1024_40k_cityscapes_20200601_014216-f196fb4e.pth |
|
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x1024_40k_cityscapes/fcn_hr18_512x1024_40k_cityscapes_20200601_014216.log.json |
|
Paper: |
|
Title: Deep High-Resolution Representation Learning for Human Pose Estimation |
|
URL: https://arxiv.org/abs/1908.07919 |
|
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/hrnet.py#L218 |
|
Framework: PyTorch |
|
- Name: fcn_hr48_4xb2-40k_cityscapes-512x1024 |
|
In Collection: FCN |
|
Results: |
|
Task: Semantic Segmentation |
|
Dataset: Cityscapes |
|
Metrics: |
|
mIoU: 78.48 |
|
mIoU(ms+flip): 79.69 |
|
Config: configs/hrnet/fcn_hr48_4xb2-40k_cityscapes-512x1024.py |
|
Metadata: |
|
Training Data: Cityscapes |
|
Batch Size: 8 |
|
Architecture: |
|
- HRNetV2p-W48 |
|
- FCN |
|
Training Resources: 4x V100 GPUS |
|
Memory (GB): 6.2 |
|
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x1024_40k_cityscapes/fcn_hr48_512x1024_40k_cityscapes_20200601_014240-a989b146.pth |
|
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x1024_40k_cityscapes/fcn_hr48_512x1024_40k_cityscapes_20200601_014240.log.json |
|
Paper: |
|
Title: Deep High-Resolution Representation Learning for Human Pose Estimation |
|
URL: https://arxiv.org/abs/1908.07919 |
|
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/hrnet.py#L218 |
|
Framework: PyTorch |
|
- Name: fcn_hr18s_4xb2-80k_cityscapes-512x1024 |
|
In Collection: FCN |
|
Results: |
|
Task: Semantic Segmentation |
|
Dataset: Cityscapes |
|
Metrics: |
|
mIoU: 75.31 |
|
mIoU(ms+flip): 77.48 |
|
Config: configs/hrnet/fcn_hr18s_4xb2-80k_cityscapes-512x1024.py |
|
Metadata: |
|
Training Data: Cityscapes |
|
Batch Size: 8 |
|
Architecture: |
|
- HRNetV2p-W18-Small |
|
- FCN |
|
Training Resources: 4x V100 GPUS |
|
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x1024_80k_cityscapes/fcn_hr18s_512x1024_80k_cityscapes_20200601_202700-1462b75d.pth |
|
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x1024_80k_cityscapes/fcn_hr18s_512x1024_80k_cityscapes_20200601_202700.log.json |
|
Paper: |
|
Title: Deep High-Resolution Representation Learning for Human Pose Estimation |
|
URL: https://arxiv.org/abs/1908.07919 |
|
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/hrnet.py#L218 |
|
Framework: PyTorch |
|
- Name: fcn_hr18_4xb2-80k_cityscapes-512x1024 |
|
In Collection: FCN |
|
Results: |
|
Task: Semantic Segmentation |
|
Dataset: Cityscapes |
|
Metrics: |
|
mIoU: 78.65 |
|
mIoU(ms+flip): 80.35 |
|
Config: configs/hrnet/fcn_hr18_4xb2-80k_cityscapes-512x1024.py |
|
Metadata: |
|
Training Data: Cityscapes |
|
Batch Size: 8 |
|
Architecture: |
|
- HRNetV2p-W18 |
|
- FCN |
|
Training Resources: 4x V100 GPUS |
|
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x1024_80k_cityscapes/fcn_hr18_512x1024_80k_cityscapes_20200601_223255-4e7b345e.pth |
|
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x1024_80k_cityscapes/fcn_hr18_512x1024_80k_cityscapes_20200601_223255.log.json |
|
Paper: |
|
Title: Deep High-Resolution Representation Learning for Human Pose Estimation |
|
URL: https://arxiv.org/abs/1908.07919 |
|
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/hrnet.py#L218 |
|
Framework: PyTorch |
|
- Name: fcn_hr48_4xb2-80k_cityscapes-512x1024 |
|
In Collection: FCN |
|
Results: |
|
Task: Semantic Segmentation |
|
Dataset: Cityscapes |
|
Metrics: |
|
mIoU: 79.93 |
|
mIoU(ms+flip): 80.72 |
|
Config: configs/hrnet/fcn_hr48_4xb2-80k_cityscapes-512x1024.py |
|
Metadata: |
|
Training Data: Cityscapes |
|
Batch Size: 8 |
|
Architecture: |
|
- HRNetV2p-W48 |
|
- FCN |
|
Training Resources: 4x V100 GPUS |
|
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x1024_80k_cityscapes/fcn_hr48_512x1024_80k_cityscapes_20200601_202606-58ea95d6.pth |
|
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x1024_80k_cityscapes/fcn_hr48_512x1024_80k_cityscapes_20200601_202606.log.json |
|
Paper: |
|
Title: Deep High-Resolution Representation Learning for Human Pose Estimation |
|
URL: https://arxiv.org/abs/1908.07919 |
|
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/hrnet.py#L218 |
|
Framework: PyTorch |
|
- Name: fcn_hr18s_4xb2-160k_cityscapes-512x1024 |
|
In Collection: FCN |
|
Results: |
|
Task: Semantic Segmentation |
|
Dataset: Cityscapes |
|
Metrics: |
|
mIoU: 76.31 |
|
mIoU(ms+flip): 78.31 |
|
Config: configs/hrnet/fcn_hr18s_4xb2-160k_cityscapes-512x1024.py |
|
Metadata: |
|
Training Data: Cityscapes |
|
Batch Size: 8 |
|
Architecture: |
|
- HRNetV2p-W18-Small |
|
- FCN |
|
Training Resources: 4x V100 GPUS |
|
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x1024_160k_cityscapes/fcn_hr18s_512x1024_160k_cityscapes_20200602_190901-4a0797ea.pth |
|
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x1024_160k_cityscapes/fcn_hr18s_512x1024_160k_cityscapes_20200602_190901.log.json |
|
Paper: |
|
Title: Deep High-Resolution Representation Learning for Human Pose Estimation |
|
URL: https://arxiv.org/abs/1908.07919 |
|
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/hrnet.py#L218 |
|
Framework: PyTorch |
|
- Name: fcn_hr18_4xb2-160k_cityscapes-512x1024 |
|
In Collection: FCN |
|
Results: |
|
Task: Semantic Segmentation |
|
Dataset: Cityscapes |
|
Metrics: |
|
mIoU: 78.8 |
|
mIoU(ms+flip): 80.74 |
|
Config: configs/hrnet/fcn_hr18_4xb2-160k_cityscapes-512x1024.py |
|
Metadata: |
|
Training Data: Cityscapes |
|
Batch Size: 8 |
|
Architecture: |
|
- HRNetV2p-W18 |
|
- FCN |
|
Training Resources: 4x V100 GPUS |
|
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x1024_160k_cityscapes/fcn_hr18_512x1024_160k_cityscapes_20200602_190822-221e4a4f.pth |
|
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x1024_160k_cityscapes/fcn_hr18_512x1024_160k_cityscapes_20200602_190822.log.json |
|
Paper: |
|
Title: Deep High-Resolution Representation Learning for Human Pose Estimation |
|
URL: https://arxiv.org/abs/1908.07919 |
|
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/hrnet.py#L218 |
|
Framework: PyTorch |
|
- Name: fcn_hr48_4xb2-160k_cityscapes-512x1024 |
|
In Collection: FCN |
|
Results: |
|
Task: Semantic Segmentation |
|
Dataset: Cityscapes |
|
Metrics: |
|
mIoU: 80.65 |
|
mIoU(ms+flip): 81.92 |
|
Config: configs/hrnet/fcn_hr48_4xb2-160k_cityscapes-512x1024.py |
|
Metadata: |
|
Training Data: Cityscapes |
|
Batch Size: 8 |
|
Architecture: |
|
- HRNetV2p-W48 |
|
- FCN |
|
Training Resources: 4x V100 GPUS |
|
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x1024_160k_cityscapes/fcn_hr48_512x1024_160k_cityscapes_20200602_190946-59b7973e.pth |
|
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x1024_160k_cityscapes/fcn_hr48_512x1024_160k_cityscapes_20200602_190946.log.json |
|
Paper: |
|
Title: Deep High-Resolution Representation Learning for Human Pose Estimation |
|
URL: https://arxiv.org/abs/1908.07919 |
|
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/hrnet.py#L218 |
|
Framework: PyTorch |
|
- Name: fcn_hr18s_4xb4-80k_ade20k-512x512 |
|
In Collection: FCN |
|
Results: |
|
Task: Semantic Segmentation |
|
Dataset: ADE20K |
|
Metrics: |
|
mIoU: 31.38 |
|
mIoU(ms+flip): 32.45 |
|
Config: configs/hrnet/fcn_hr18s_4xb4-80k_ade20k-512x512.py |
|
Metadata: |
|
Training Data: ADE20K |
|
Batch Size: 16 |
|
Architecture: |
|
- HRNetV2p-W18-Small |
|
- FCN |
|
Training Resources: 4x V100 GPUS |
|
Memory (GB): 3.8 |
|
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_80k_ade20k/fcn_hr18s_512x512_80k_ade20k_20200614_144345-77fc814a.pth |
|
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_80k_ade20k/fcn_hr18s_512x512_80k_ade20k_20200614_144345.log.json |
|
Paper: |
|
Title: Deep High-Resolution Representation Learning for Human Pose Estimation |
|
URL: https://arxiv.org/abs/1908.07919 |
|
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/hrnet.py#L218 |
|
Framework: PyTorch |
|
- Name: fcn_hr18_4xb4-80k_ade20k-512x512 |
|
In Collection: FCN |
|
Results: |
|
Task: Semantic Segmentation |
|
Dataset: ADE20K |
|
Metrics: |
|
mIoU: 36.27 |
|
mIoU(ms+flip): 37.28 |
|
Config: configs/hrnet/fcn_hr18_4xb4-80k_ade20k-512x512.py |
|
Metadata: |
|
Training Data: ADE20K |
|
Batch Size: 16 |
|
Architecture: |
|
- HRNetV2p-W18 |
|
- FCN |
|
Training Resources: 4x V100 GPUS |
|
Memory (GB): 4.9 |
|
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_80k_ade20k/fcn_hr18_512x512_80k_ade20k_20210827_114910-6c9382c0.pth |
|
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_80k_ade20k/fcn_hr18_512x512_80k_ade20k_20210827_114910.log.json |
|
Paper: |
|
Title: Deep High-Resolution Representation Learning for Human Pose Estimation |
|
URL: https://arxiv.org/abs/1908.07919 |
|
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/hrnet.py#L218 |
|
Framework: PyTorch |
|
- Name: fcn_hr48_4xb4-80k_ade20k-512x512 |
|
In Collection: FCN |
|
Results: |
|
Task: Semantic Segmentation |
|
Dataset: ADE20K |
|
Metrics: |
|
mIoU: 41.9 |
|
mIoU(ms+flip): 43.27 |
|
Config: configs/hrnet/fcn_hr48_4xb4-80k_ade20k-512x512.py |
|
Metadata: |
|
Training Data: ADE20K |
|
Batch Size: 16 |
|
Architecture: |
|
- HRNetV2p-W48 |
|
- FCN |
|
Training Resources: 4x V100 GPUS |
|
Memory (GB): 8.2 |
|
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_80k_ade20k/fcn_hr48_512x512_80k_ade20k_20200614_193946-7ba5258d.pth |
|
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_80k_ade20k/fcn_hr48_512x512_80k_ade20k_20200614_193946.log.json |
|
Paper: |
|
Title: Deep High-Resolution Representation Learning for Human Pose Estimation |
|
URL: https://arxiv.org/abs/1908.07919 |
|
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/hrnet.py#L218 |
|
Framework: PyTorch |
|
- Name: fcn_hr18s_4xb4-160k_ade20k-512x512 |
|
In Collection: FCN |
|
Results: |
|
Task: Semantic Segmentation |
|
Dataset: ADE20K |
|
Metrics: |
|
mIoU: 33.07 |
|
mIoU(ms+flip): 34.56 |
|
Config: configs/hrnet/fcn_hr18s_4xb4-160k_ade20k-512x512.py |
|
Metadata: |
|
Training Data: ADE20K |
|
Batch Size: 16 |
|
Architecture: |
|
- HRNetV2p-W18-Small |
|
- FCN |
|
Training Resources: 4x V100 GPUS |
|
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_160k_ade20k/fcn_hr18s_512x512_160k_ade20k_20210829_174739-f1e7c2e7.pth |
|
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_160k_ade20k/fcn_hr18s_512x512_160k_ade20k_20210829_174739.log.json |
|
Paper: |
|
Title: Deep High-Resolution Representation Learning for Human Pose Estimation |
|
URL: https://arxiv.org/abs/1908.07919 |
|
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/hrnet.py#L218 |
|
Framework: PyTorch |
|
- Name: fcn_hr18_4xb4-160k_ade20k-512x512 |
|
In Collection: FCN |
|
Results: |
|
Task: Semantic Segmentation |
|
Dataset: ADE20K |
|
Metrics: |
|
mIoU: 36.79 |
|
mIoU(ms+flip): 38.58 |
|
Config: configs/hrnet/fcn_hr18_4xb4-160k_ade20k-512x512.py |
|
Metadata: |
|
Training Data: ADE20K |
|
Batch Size: 16 |
|
Architecture: |
|
- HRNetV2p-W18 |
|
- FCN |
|
Training Resources: 4x V100 GPUS |
|
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_160k_ade20k/fcn_hr18_512x512_160k_ade20k_20200614_214426-ca961836.pth |
|
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_160k_ade20k/fcn_hr18_512x512_160k_ade20k_20200614_214426.log.json |
|
Paper: |
|
Title: Deep High-Resolution Representation Learning for Human Pose Estimation |
|
URL: https://arxiv.org/abs/1908.07919 |
|
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/hrnet.py#L218 |
|
Framework: PyTorch |
|
- Name: fcn_hr48_4xb4-160k_ade20k-512x512 |
|
In Collection: FCN |
|
Results: |
|
Task: Semantic Segmentation |
|
Dataset: ADE20K |
|
Metrics: |
|
mIoU: 42.02 |
|
mIoU(ms+flip): 43.86 |
|
Config: configs/hrnet/fcn_hr48_4xb4-160k_ade20k-512x512.py |
|
Metadata: |
|
Training Data: ADE20K |
|
Batch Size: 16 |
|
Architecture: |
|
- HRNetV2p-W48 |
|
- FCN |
|
Training Resources: 4x V100 GPUS |
|
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_160k_ade20k/fcn_hr48_512x512_160k_ade20k_20200614_214407-a52fc02c.pth |
|
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_160k_ade20k/fcn_hr48_512x512_160k_ade20k_20200614_214407.log.json |
|
Paper: |
|
Title: Deep High-Resolution Representation Learning for Human Pose Estimation |
|
URL: https://arxiv.org/abs/1908.07919 |
|
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/hrnet.py#L218 |
|
Framework: PyTorch |
|
- Name: fcn_hr18s_4xb4-20k_voc12aug-512x512 |
|
In Collection: FCN |
|
Results: |
|
Task: Semantic Segmentation |
|
Dataset: Pascal VOC 2012 + Aug |
|
Metrics: |
|
mIoU: 65.5 |
|
mIoU(ms+flip): 68.89 |
|
Config: configs/hrnet/fcn_hr18s_4xb4-20k_voc12aug-512x512.py |
|
Metadata: |
|
Training Data: Pascal VOC 2012 + Aug |
|
Batch Size: 16 |
|
Architecture: |
|
- HRNetV2p-W18-Small |
|
- FCN |
|
Training Resources: 4x V100 GPUS |
|
Memory (GB): 1.8 |
|
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_20k_voc12aug/fcn_hr18s_512x512_20k_voc12aug_20210829_174910-0aceadb4.pth |
|
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_20k_voc12aug/fcn_hr18s_512x512_20k_voc12aug_20210829_174910.log.json |
|
Paper: |
|
Title: Deep High-Resolution Representation Learning for Human Pose Estimation |
|
URL: https://arxiv.org/abs/1908.07919 |
|
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/hrnet.py#L218 |
|
Framework: PyTorch |
|
- Name: fcn_hr18_4xb4-20k_voc12aug-512x512 |
|
In Collection: FCN |
|
Results: |
|
Task: Semantic Segmentation |
|
Dataset: Pascal VOC 2012 + Aug |
|
Metrics: |
|
mIoU: 72.3 |
|
mIoU(ms+flip): 74.71 |
|
Config: configs/hrnet/fcn_hr18_4xb4-20k_voc12aug-512x512.py |
|
Metadata: |
|
Training Data: Pascal VOC 2012 + Aug |
|
Batch Size: 16 |
|
Architecture: |
|
- HRNetV2p-W18 |
|
- FCN |
|
Training Resources: 4x V100 GPUS |
|
Memory (GB): 2.9 |
|
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_20k_voc12aug/fcn_hr18_512x512_20k_voc12aug_20200617_224503-488d45f7.pth |
|
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_20k_voc12aug/fcn_hr18_512x512_20k_voc12aug_20200617_224503.log.json |
|
Paper: |
|
Title: Deep High-Resolution Representation Learning for Human Pose Estimation |
|
URL: https://arxiv.org/abs/1908.07919 |
|
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/hrnet.py#L218 |
|
Framework: PyTorch |
|
- Name: fcn_hr48_4xb4-20k_voc12aug-512x512 |
|
In Collection: FCN |
|
Results: |
|
Task: Semantic Segmentation |
|
Dataset: Pascal VOC 2012 + Aug |
|
Metrics: |
|
mIoU: 75.87 |
|
mIoU(ms+flip): 78.58 |
|
Config: configs/hrnet/fcn_hr48_4xb4-20k_voc12aug-512x512.py |
|
Metadata: |
|
Training Data: Pascal VOC 2012 + Aug |
|
Batch Size: 16 |
|
Architecture: |
|
- HRNetV2p-W48 |
|
- FCN |
|
Training Resources: 4x V100 GPUS |
|
Memory (GB): 6.2 |
|
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_20k_voc12aug/fcn_hr48_512x512_20k_voc12aug_20200617_224419-89de05cd.pth |
|
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_20k_voc12aug/fcn_hr48_512x512_20k_voc12aug_20200617_224419.log.json |
|
Paper: |
|
Title: Deep High-Resolution Representation Learning for Human Pose Estimation |
|
URL: https://arxiv.org/abs/1908.07919 |
|
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/hrnet.py#L218 |
|
Framework: PyTorch |
|
- Name: fcn_hr18s_4xb4-40k_voc12aug-512x512 |
|
In Collection: FCN |
|
Results: |
|
Task: Semantic Segmentation |
|
Dataset: Pascal VOC 2012 + Aug |
|
Metrics: |
|
mIoU: 66.61 |
|
mIoU(ms+flip): 70.0 |
|
Config: configs/hrnet/fcn_hr18s_4xb4-40k_voc12aug-512x512.py |
|
Metadata: |
|
Training Data: Pascal VOC 2012 + Aug |
|
Batch Size: 16 |
|
Architecture: |
|
- HRNetV2p-W18-Small |
|
- FCN |
|
Training Resources: 4x V100 GPUS |
|
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_40k_voc12aug/fcn_hr18s_512x512_40k_voc12aug_20200614_000648-4f8d6e7f.pth |
|
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_40k_voc12aug/fcn_hr18s_512x512_40k_voc12aug_20200614_000648.log.json |
|
Paper: |
|
Title: Deep High-Resolution Representation Learning for Human Pose Estimation |
|
URL: https://arxiv.org/abs/1908.07919 |
|
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/hrnet.py#L218 |
|
Framework: PyTorch |
|
- Name: fcn_hr18_4xb4-40k_voc12aug-512x512 |
|
In Collection: FCN |
|
Results: |
|
Task: Semantic Segmentation |
|
Dataset: Pascal VOC 2012 + Aug |
|
Metrics: |
|
mIoU: 72.9 |
|
mIoU(ms+flip): 75.59 |
|
Config: configs/hrnet/fcn_hr18_4xb4-40k_voc12aug-512x512.py |
|
Metadata: |
|
Training Data: Pascal VOC 2012 + Aug |
|
Batch Size: 16 |
|
Architecture: |
|
- HRNetV2p-W18 |
|
- FCN |
|
Training Resources: 4x V100 GPUS |
|
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_40k_voc12aug/fcn_hr18_512x512_40k_voc12aug_20200613_224401-1b4b76cd.pth |
|
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_40k_voc12aug/fcn_hr18_512x512_40k_voc12aug_20200613_224401.log.json |
|
Paper: |
|
Title: Deep High-Resolution Representation Learning for Human Pose Estimation |
|
URL: https://arxiv.org/abs/1908.07919 |
|
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/hrnet.py#L218 |
|
Framework: PyTorch |
|
- Name: fcn_hr48_4xb4-40k_voc12aug-512x512 |
|
In Collection: FCN |
|
Results: |
|
Task: Semantic Segmentation |
|
Dataset: Pascal VOC 2012 + Aug |
|
Metrics: |
|
mIoU: 76.24 |
|
mIoU(ms+flip): 78.49 |
|
Config: configs/hrnet/fcn_hr48_4xb4-40k_voc12aug-512x512.py |
|
Metadata: |
|
Training Data: Pascal VOC 2012 + Aug |
|
Batch Size: 16 |
|
Architecture: |
|
- HRNetV2p-W48 |
|
- FCN |
|
Training Resources: 4x V100 GPUS |
|
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_40k_voc12aug/fcn_hr48_512x512_40k_voc12aug_20200613_222111-1b0f18bc.pth |
|
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_40k_voc12aug/fcn_hr48_512x512_40k_voc12aug_20200613_222111.log.json |
|
Paper: |
|
Title: Deep High-Resolution Representation Learning for Human Pose Estimation |
|
URL: https://arxiv.org/abs/1908.07919 |
|
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/hrnet.py#L218 |
|
Framework: PyTorch |
|
- Name: fcn_hr48_4xb4-40k_pascal-context-480x480 |
|
In Collection: FCN |
|
Results: |
|
Task: Semantic Segmentation |
|
Dataset: Pascal Context |
|
Metrics: |
|
mIoU: 45.14 |
|
mIoU(ms+flip): 47.42 |
|
Config: configs/hrnet/fcn_hr48_4xb4-40k_pascal-context-480x480.py |
|
Metadata: |
|
Training Data: Pascal Context |
|
Batch Size: 16 |
|
Architecture: |
|
- HRNetV2p-W48 |
|
- FCN |
|
Training Resources: 4x V100 GPUS |
|
Memory (GB): 6.1 |
|
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_480x480_40k_pascal_context/fcn_hr48_480x480_40k_pascal_context_20200911_164852-667d00b0.pth |
|
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_480x480_40k_pascal_context/fcn_hr48_480x480_40k_pascal_context-20200911_164852.log.json |
|
Paper: |
|
Title: Deep High-Resolution Representation Learning for Human Pose Estimation |
|
URL: https://arxiv.org/abs/1908.07919 |
|
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/hrnet.py#L218 |
|
Framework: PyTorch |
|
- Name: fcn_hr48_4xb4-80k_pascal-context-480x480 |
|
In Collection: FCN |
|
Results: |
|
Task: Semantic Segmentation |
|
Dataset: Pascal Context |
|
Metrics: |
|
mIoU: 45.84 |
|
mIoU(ms+flip): 47.84 |
|
Config: configs/hrnet/fcn_hr48_4xb4-80k_pascal-context-480x480.py |
|
Metadata: |
|
Training Data: Pascal Context |
|
Batch Size: 16 |
|
Architecture: |
|
- HRNetV2p-W48 |
|
- FCN |
|
Training Resources: 4x V100 GPUS |
|
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_480x480_80k_pascal_context/fcn_hr48_480x480_80k_pascal_context_20200911_155322-847a6711.pth |
|
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_480x480_80k_pascal_context/fcn_hr48_480x480_80k_pascal_context-20200911_155322.log.json |
|
Paper: |
|
Title: Deep High-Resolution Representation Learning for Human Pose Estimation |
|
URL: https://arxiv.org/abs/1908.07919 |
|
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/hrnet.py#L218 |
|
Framework: PyTorch |
|
- Name: fcn_hr48_4xb4-40k_pascal-context-59-480x480 |
|
In Collection: FCN |
|
Results: |
|
Task: Semantic Segmentation |
|
Dataset: Pascal Context 59 |
|
Metrics: |
|
mIoU: 50.33 |
|
mIoU(ms+flip): 52.83 |
|
Config: configs/hrnet/fcn_hr48_4xb4-40k_pascal-context-59-480x480.py |
|
Metadata: |
|
Training Data: Pascal Context 59 |
|
Batch Size: 16 |
|
Architecture: |
|
- HRNetV2p-W48 |
|
- FCN |
|
Training Resources: 4x V100 GPUS |
|
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_480x480_40k_pascal_context_59/fcn_hr48_480x480_40k_pascal_context_59_20210410_122738-b808b8b2.pth |
|
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_480x480_40k_pascal_context_59/fcn_hr48_480x480_40k_pascal_context_59-20210410_122738.log.json |
|
Paper: |
|
Title: Deep High-Resolution Representation Learning for Human Pose Estimation |
|
URL: https://arxiv.org/abs/1908.07919 |
|
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/hrnet.py#L218 |
|
Framework: PyTorch |
|
- Name: fcn_hr48_4xb4-80k_pascal-context-59-480x480 |
|
In Collection: FCN |
|
Results: |
|
Task: Semantic Segmentation |
|
Dataset: Pascal Context 59 |
|
Metrics: |
|
mIoU: 51.12 |
|
mIoU(ms+flip): 53.56 |
|
Config: configs/hrnet/fcn_hr48_4xb4-80k_pascal-context-59-480x480.py |
|
Metadata: |
|
Training Data: Pascal Context 59 |
|
Batch Size: 16 |
|
Architecture: |
|
- HRNetV2p-W48 |
|
- FCN |
|
Training Resources: 4x V100 GPUS |
|
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_480x480_80k_pascal_context_59/fcn_hr48_480x480_80k_pascal_context_59_20210411_003240-3ae7081e.pth |
|
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_480x480_80k_pascal_context_59/fcn_hr48_480x480_80k_pascal_context_59-20210411_003240.log.json |
|
Paper: |
|
Title: Deep High-Resolution Representation Learning for Human Pose Estimation |
|
URL: https://arxiv.org/abs/1908.07919 |
|
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/hrnet.py#L218 |
|
Framework: PyTorch |
|
- Name: fcn_hr18s_4xb4-80k_loveda-512x512 |
|
In Collection: FCN |
|
Results: |
|
Task: Semantic Segmentation |
|
Dataset: LoveDA |
|
Metrics: |
|
mIoU: 49.28 |
|
mIoU(ms+flip): 49.42 |
|
Config: configs/hrnet/fcn_hr18s_4xb4-80k_loveda-512x512.py |
|
Metadata: |
|
Training Data: LoveDA |
|
Batch Size: 16 |
|
Architecture: |
|
- HRNetV2p-W18-Small |
|
- FCN |
|
Training Resources: 4x V100 GPUS |
|
Memory (GB): 1.59 |
|
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_80k_loveda/fcn_hr18s_512x512_80k_loveda_20211210_203228-60a86a7a.pth |
|
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_80k_loveda/fcn_hr18s_512x512_80k_loveda_20211210_203228.log.json |
|
Paper: |
|
Title: Deep High-Resolution Representation Learning for Human Pose Estimation |
|
URL: https://arxiv.org/abs/1908.07919 |
|
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/hrnet.py#L218 |
|
Framework: PyTorch |
|
- Name: fcn_hr18_4xb4-80k_loveda-512x512 |
|
In Collection: FCN |
|
Results: |
|
Task: Semantic Segmentation |
|
Dataset: LoveDA |
|
Metrics: |
|
mIoU: 50.81 |
|
mIoU(ms+flip): 50.95 |
|
Config: configs/hrnet/fcn_hr18_4xb4-80k_loveda-512x512.py |
|
Metadata: |
|
Training Data: LoveDA |
|
Batch Size: 16 |
|
Architecture: |
|
- HRNetV2p-W18 |
|
- FCN |
|
Training Resources: 4x V100 GPUS |
|
Memory (GB): 2.76 |
|
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_80k_loveda/fcn_hr18_512x512_80k_loveda_20211210_203952-93d9c3b3.pth |
|
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_80k_loveda/fcn_hr18_512x512_80k_loveda_20211210_203952.log.json |
|
Paper: |
|
Title: Deep High-Resolution Representation Learning for Human Pose Estimation |
|
URL: https://arxiv.org/abs/1908.07919 |
|
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/hrnet.py#L218 |
|
Framework: PyTorch |
|
- Name: fcn_hr48_4xb4-80k_loveda-512x512 |
|
In Collection: FCN |
|
Results: |
|
Task: Semantic Segmentation |
|
Dataset: LoveDA |
|
Metrics: |
|
mIoU: 51.42 |
|
mIoU(ms+flip): 51.64 |
|
Config: configs/hrnet/fcn_hr48_4xb4-80k_loveda-512x512.py |
|
Metadata: |
|
Training Data: LoveDA |
|
Batch Size: 16 |
|
Architecture: |
|
- HRNetV2p-W48 |
|
- FCN |
|
Training Resources: 4x V100 GPUS |
|
Memory (GB): 6.2 |
|
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_80k_loveda/fcn_hr48_512x512_80k_loveda_20211211_044756-67072f55.pth |
|
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_80k_loveda/fcn_hr48_512x512_80k_loveda_20211211_044756.log.json |
|
Paper: |
|
Title: Deep High-Resolution Representation Learning for Human Pose Estimation |
|
URL: https://arxiv.org/abs/1908.07919 |
|
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/hrnet.py#L218 |
|
Framework: PyTorch |
|
- Name: fcn_hr18s_4xb4-80k_potsdam-512x512 |
|
In Collection: FCN |
|
Results: |
|
Task: Semantic Segmentation |
|
Dataset: Potsdam |
|
Metrics: |
|
mIoU: 77.64 |
|
mIoU(ms+flip): 78.8 |
|
Config: configs/hrnet/fcn_hr18s_4xb4-80k_potsdam-512x512.py |
|
Metadata: |
|
Training Data: Potsdam |
|
Batch Size: 16 |
|
Architecture: |
|
- HRNetV2p-W18-Small |
|
- FCN |
|
Training Resources: 4x V100 GPUS |
|
Memory (GB): 1.58 |
|
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_80k_potsdam/fcn_hr18s_512x512_80k_potsdam_20211218_205517-ba32af63.pth |
|
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_80k_potsdam/fcn_hr18s_512x512_80k_potsdam_20211218_205517.log.json |
|
Paper: |
|
Title: Deep High-Resolution Representation Learning for Human Pose Estimation |
|
URL: https://arxiv.org/abs/1908.07919 |
|
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/hrnet.py#L218 |
|
Framework: PyTorch |
|
- Name: fcn_hr18_4xb4-80k_potsdam-512x512 |
|
In Collection: FCN |
|
Results: |
|
Task: Semantic Segmentation |
|
Dataset: Potsdam |
|
Metrics: |
|
mIoU: 78.26 |
|
mIoU(ms+flip): 79.24 |
|
Config: configs/hrnet/fcn_hr18_4xb4-80k_potsdam-512x512.py |
|
Metadata: |
|
Training Data: Potsdam |
|
Batch Size: 16 |
|
Architecture: |
|
- HRNetV2p-W18 |
|
- FCN |
|
Training Resources: 4x V100 GPUS |
|
Memory (GB): 2.76 |
|
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_80k_potsdam/fcn_hr18_512x512_80k_potsdam_20211218_205517-5d0387ad.pth |
|
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_80k_potsdam/fcn_hr18_512x512_80k_potsdam_20211218_205517.log.json |
|
Paper: |
|
Title: Deep High-Resolution Representation Learning for Human Pose Estimation |
|
URL: https://arxiv.org/abs/1908.07919 |
|
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/hrnet.py#L218 |
|
Framework: PyTorch |
|
- Name: fcn_hr48_4xb4-80k_potsdam-512x512 |
|
In Collection: FCN |
|
Results: |
|
Task: Semantic Segmentation |
|
Dataset: Potsdam |
|
Metrics: |
|
mIoU: 78.39 |
|
mIoU(ms+flip): 79.34 |
|
Config: configs/hrnet/fcn_hr48_4xb4-80k_potsdam-512x512.py |
|
Metadata: |
|
Training Data: Potsdam |
|
Batch Size: 16 |
|
Architecture: |
|
- HRNetV2p-W48 |
|
- FCN |
|
Training Resources: 4x V100 GPUS |
|
Memory (GB): 6.2 |
|
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_80k_potsdam/fcn_hr48_512x512_80k_potsdam_20211219_020601-97434c78.pth |
|
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_80k_potsdam/fcn_hr48_512x512_80k_potsdam_20211219_020601.log.json |
|
Paper: |
|
Title: Deep High-Resolution Representation Learning for Human Pose Estimation |
|
URL: https://arxiv.org/abs/1908.07919 |
|
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/hrnet.py#L218 |
|
Framework: PyTorch |
|
- Name: fcn_hr18s_4xb4-80k_vaihingen-512x512 |
|
In Collection: FCN |
|
Results: |
|
Task: Semantic Segmentation |
|
Dataset: Vaihingen |
|
Metrics: |
|
mIoU: 71.81 |
|
mIoU(ms+flip): 73.1 |
|
Config: configs/hrnet/fcn_hr18s_4xb4-80k_vaihingen-512x512.py |
|
Metadata: |
|
Training Data: Vaihingen |
|
Batch Size: 16 |
|
Architecture: |
|
- HRNetV2p-W18-Small |
|
- FCN |
|
Training Resources: 4x V100 GPUS |
|
Memory (GB): 1.58 |
|
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_4x4_512x512_80k_vaihingen/fcn_hr18s_4x4_512x512_80k_vaihingen_20211231_230909-b23aae02.pth |
|
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_4x4_512x512_80k_vaihingen/fcn_hr18s_4x4_512x512_80k_vaihingen_20211231_230909.log.json |
|
Paper: |
|
Title: Deep High-Resolution Representation Learning for Human Pose Estimation |
|
URL: https://arxiv.org/abs/1908.07919 |
|
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/hrnet.py#L218 |
|
Framework: PyTorch |
|
- Name: fcn_hr18_4xb4-80k_vaihingen-512x512 |
|
In Collection: FCN |
|
Results: |
|
Task: Semantic Segmentation |
|
Dataset: Vaihingen |
|
Metrics: |
|
mIoU: 72.57 |
|
mIoU(ms+flip): 74.09 |
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Config: configs/hrnet/fcn_hr18_4xb4-80k_vaihingen-512x512.py |
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Metadata: |
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Training Data: Vaihingen |
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Batch Size: 16 |
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Architecture: |
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- HRNetV2p-W18 |
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- FCN |
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Training Resources: 4x V100 GPUS |
|
Memory (GB): 2.76 |
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_4x4_512x512_80k_vaihingen/fcn_hr18_4x4_512x512_80k_vaihingen_20211231_231216-2ec3ae8a.pth |
|
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_4x4_512x512_80k_vaihingen/fcn_hr18_4x4_512x512_80k_vaihingen_20211231_231216.log.json |
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Paper: |
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Title: Deep High-Resolution Representation Learning for Human Pose Estimation |
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URL: https://arxiv.org/abs/1908.07919 |
|
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/hrnet.py#L218 |
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Framework: PyTorch |
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- Name: fcn_hr48_4xb4-80k_vaihingen-512x512 |
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In Collection: FCN |
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Results: |
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Task: Semantic Segmentation |
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Dataset: Vaihingen |
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Metrics: |
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mIoU: 72.5 |
|
mIoU(ms+flip): 73.52 |
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Config: configs/hrnet/fcn_hr48_4xb4-80k_vaihingen-512x512.py |
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Metadata: |
|
Training Data: Vaihingen |
|
Batch Size: 16 |
|
Architecture: |
|
- HRNetV2p-W48 |
|
- FCN |
|
Training Resources: 4x V100 GPUS |
|
Memory (GB): 6.2 |
|
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_4x4_512x512_80k_vaihingen/fcn_hr48_4x4_512x512_80k_vaihingen_20211231_231244-7133cb22.pth |
|
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_4x4_512x512_80k_vaihingen/fcn_hr48_4x4_512x512_80k_vaihingen_20211231_231244.log.json |
|
Paper: |
|
Title: Deep High-Resolution Representation Learning for Human Pose Estimation |
|
URL: https://arxiv.org/abs/1908.07919 |
|
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/hrnet.py#L218 |
|
Framework: PyTorch |
|
- Name: fcn_hr18s_4xb4-80k_isaid-896x896 |
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In Collection: FCN |
|
Results: |
|
Task: Semantic Segmentation |
|
Dataset: iSAID |
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Metrics: |
|
mIoU: 62.3 |
|
mIoU(ms+flip): 62.97 |
|
Config: configs/hrnet/fcn_hr18s_4xb4-80k_isaid-896x896.py |
|
Metadata: |
|
Training Data: iSAID |
|
Batch Size: 16 |
|
Architecture: |
|
- HRNetV2p-W18-Small |
|
- FCN |
|
Training Resources: 4x V100 GPUS |
|
Memory (GB): 4.95 |
|
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_4x4_896x896_80k_isaid/fcn_hr18s_4x4_896x896_80k_isaid_20220118_001603-3cc0769b.pth |
|
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_4x4_896x896_80k_isaid/fcn_hr18s_4x4_896x896_80k_isaid_20220118_001603.log.json |
|
Paper: |
|
Title: Deep High-Resolution Representation Learning for Human Pose Estimation |
|
URL: https://arxiv.org/abs/1908.07919 |
|
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/hrnet.py#L218 |
|
Framework: PyTorch |
|
- Name: fcn_hr18_4xb4-80k_isaid-896x896 |
|
In Collection: FCN |
|
Results: |
|
Task: Semantic Segmentation |
|
Dataset: iSAID |
|
Metrics: |
|
mIoU: 65.06 |
|
mIoU(ms+flip): 65.6 |
|
Config: configs/hrnet/fcn_hr18_4xb4-80k_isaid-896x896.py |
|
Metadata: |
|
Training Data: iSAID |
|
Batch Size: 16 |
|
Architecture: |
|
- HRNetV2p-W18 |
|
- FCN |
|
Training Resources: 4x V100 GPUS |
|
Memory (GB): 8.3 |
|
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_4x4_896x896_80k_isaid/fcn_hr18_4x4_896x896_80k_isaid_20220110_182230-49bf752e.pth |
|
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_4x4_896x896_80k_isaid/fcn_hr18_4x4_896x896_80k_isaid_20220110_182230.log.json |
|
Paper: |
|
Title: Deep High-Resolution Representation Learning for Human Pose Estimation |
|
URL: https://arxiv.org/abs/1908.07919 |
|
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/hrnet.py#L218 |
|
Framework: PyTorch |
|
- Name: fcn_hr48_4xb4-80k_isaid-896x896 |
|
In Collection: FCN |
|
Results: |
|
Task: Semantic Segmentation |
|
Dataset: iSAID |
|
Metrics: |
|
mIoU: 67.8 |
|
mIoU(ms+flip): 68.53 |
|
Config: configs/hrnet/fcn_hr48_4xb4-80k_isaid-896x896.py |
|
Metadata: |
|
Training Data: iSAID |
|
Batch Size: 16 |
|
Architecture: |
|
- HRNetV2p-W48 |
|
- FCN |
|
Training Resources: 4x V100 GPUS |
|
Memory (GB): 16.89 |
|
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_4x4_896x896_80k_isaid/fcn_hr48_4x4_896x896_80k_isaid_20220114_174643-547fc420.pth |
|
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_4x4_896x896_80k_isaid/fcn_hr48_4x4_896x896_80k_isaid_20220114_174643.log.json |
|
Paper: |
|
Title: Deep High-Resolution Representation Learning for Human Pose Estimation |
|
URL: https://arxiv.org/abs/1908.07919 |
|
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/hrnet.py#L218 |
|
Framework: PyTorch |
|
|