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mmsegmentation/configs/knet/metafile.yaml

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Collections:
- Name: KNet
License: Apache License 2.0
Metadata:
Training Data:
- ADE20K
Paper:
Title: 'K-Net: Towards Unified Image Segmentation'
URL: https://arxiv.org/abs/2106.14855
README: configs/knet/README.md
Frameworks:
- PyTorch
Models:
- Name: knet-s3_r50-d8_fcn_8xb2-adamw-80k_ade20k-512x512
In Collection: KNet
Results:
Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 43.6
mIoU(ms+flip): 45.12
Config: configs/knet/knet-s3_r50-d8_fcn_8xb2-adamw-80k_ade20k-512x512.py
Metadata:
Training Data: ADE20K
Batch Size: 16
Architecture:
- R-50-D8
- KNet
- FCN
Training Resources: 8x V100 GPUS
Memory (GB): 7.01
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/knet/knet_s3_fcn_r50-d8_8x2_512x512_adamw_80k_ade20k/knet_s3_fcn_r50-d8_8x2_512x512_adamw_80k_ade20k_20220228_043751-abcab920.pth
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/knet/knet_s3_fcn_r50-d8_8x2_512x512_adamw_80k_ade20k/knet_s3_fcn_r50-d8_8x2_512x512_adamw_80k_ade20k_20220228_043751.log.json
Paper:
Title: 'K-Net: Towards Unified Image Segmentation'
URL: https://arxiv.org/abs/2106.14855
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.23.0/mmseg/models/decode_heads/knet_head.py#L392
Framework: PyTorch
- Name: knet-s3_r50-d8_pspnet_8xb2-adamw-80k_ade20k-512x512
In Collection: KNet
Results:
Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 44.18
mIoU(ms+flip): 45.58
Config: configs/knet/knet-s3_r50-d8_pspnet_8xb2-adamw-80k_ade20k-512x512.py
Metadata:
Training Data: ADE20K
Batch Size: 16
Architecture:
- R-50-D8
- KNet
- PSPNet
Training Resources: 8x V100 GPUS
Memory (GB): 6.98
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/knet/knet_s3_pspnet_r50-d8_8x2_512x512_adamw_80k_ade20k/knet_s3_pspnet_r50-d8_8x2_512x512_adamw_80k_ade20k_20220228_054634-d2c72240.pth
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/knet/knet_s3_pspnet_r50-d8_8x2_512x512_adamw_80k_ade20k/knet_s3_pspnet_r50-d8_8x2_512x512_adamw_80k_ade20k_20220228_054634.log.json
Paper:
Title: 'K-Net: Towards Unified Image Segmentation'
URL: https://arxiv.org/abs/2106.14855
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.23.0/mmseg/models/decode_heads/knet_head.py#L392
Framework: PyTorch
- Name: knet-s3_r50-d8_deeplabv3_8xb2-adamw-80k_ade20k-512x512
In Collection: KNet
Results:
Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 45.06
mIoU(ms+flip): 46.11
Config: configs/knet/knet-s3_r50-d8_deeplabv3_8xb2-adamw-80k_ade20k-512x512.py
Metadata:
Training Data: ADE20K
Batch Size: 16
Architecture:
- R-50-D8
- KNet
- DeepLabV3
Training Resources: 8x V100 GPUS
Memory (GB): 7.42
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/knet/knet_s3_deeplabv3_r50-d8_8x2_512x512_adamw_80k_ade20k/knet_s3_deeplabv3_r50-d8_8x2_512x512_adamw_80k_ade20k_20220228_041642-00c8fbeb.pth
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/knet/knet_s3_deeplabv3_r50-d8_8x2_512x512_adamw_80k_ade20k/knet_s3_deeplabv3_r50-d8_8x2_512x512_adamw_80k_ade20k_20220228_041642.log.json
Paper:
Title: 'K-Net: Towards Unified Image Segmentation'
URL: https://arxiv.org/abs/2106.14855
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.23.0/mmseg/models/decode_heads/knet_head.py#L392
Framework: PyTorch
- Name: knet-s3_r50-d8_upernet_8xb2-adamw-80k_ade20k-512x512
In Collection: KNet
Results:
Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 43.45
mIoU(ms+flip): 44.07
Config: configs/knet/knet-s3_r50-d8_upernet_8xb2-adamw-80k_ade20k-512x512.py
Metadata:
Training Data: ADE20K
Batch Size: 16
Architecture:
- R-50-D8
- KNet
- UperNet
Training Resources: 8x V100 GPUS
Memory (GB): 7.34
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/knet/knet_s3_upernet_r50-d8_8x2_512x512_adamw_80k_ade20k/knet_s3_upernet_r50-d8_8x2_512x512_adamw_80k_ade20k_20220304_125657-215753b0.pth
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/knet/knet_s3_upernet_r50-d8_8x2_512x512_adamw_80k_ade20k/knet_s3_upernet_r50-d8_8x2_512x512_adamw_80k_ade20k_20220304_125657.log.json
Paper:
Title: 'K-Net: Towards Unified Image Segmentation'
URL: https://arxiv.org/abs/2106.14855
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.23.0/mmseg/models/decode_heads/knet_head.py#L392
Framework: PyTorch
- Name: knet-s3_swin-t_upernet_8xb2-adamw-80k_ade20k-512x512
In Collection: KNet
Results:
Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 45.84
mIoU(ms+flip): 46.27
Config: configs/knet/knet-s3_swin-t_upernet_8xb2-adamw-80k_ade20k-512x512.py
Metadata:
Training Data: ADE20K
Batch Size: 16
Architecture:
- Swin-T
- KNet
- UperNet
Training Resources: 8x V100 GPUS
Memory (GB): 7.57
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/knet/knet_s3_upernet_swin-t_8x2_512x512_adamw_80k_ade20k/knet_s3_upernet_swin-t_8x2_512x512_adamw_80k_ade20k_20220303_133059-7545e1dc.pth
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/knet/knet_s3_upernet_swin-t_8x2_512x512_adamw_80k_ade20k/knet_s3_upernet_swin-t_8x2_512x512_adamw_80k_ade20k_20220303_133059.log.json
Paper:
Title: 'K-Net: Towards Unified Image Segmentation'
URL: https://arxiv.org/abs/2106.14855
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.23.0/mmseg/models/decode_heads/knet_head.py#L392
Framework: PyTorch
- Name: knet-s3_swin-l_upernet_8xb2-adamw-80k_ade20k-512x512
In Collection: KNet
Results:
Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 52.05
mIoU(ms+flip): 53.24
Config: configs/knet/knet-s3_swin-l_upernet_8xb2-adamw-80k_ade20k-512x512.py
Metadata:
Training Data: ADE20K
Batch Size: 16
Architecture:
- Swin-L
- KNet
- UperNet
Training Resources: 8x V100 GPUS
Memory (GB): 13.5
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/knet/knet_s3_upernet_swin-l_8x2_512x512_adamw_80k_ade20k/knet_s3_upernet_swin-l_8x2_512x512_adamw_80k_ade20k_20220303_154559-d8da9a90.pth
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/knet/knet_s3_upernet_swin-l_8x2_512x512_adamw_80k_ade20k/knet_s3_upernet_swin-l_8x2_512x512_adamw_80k_ade20k_20220303_154559.log.json
Paper:
Title: 'K-Net: Towards Unified Image Segmentation'
URL: https://arxiv.org/abs/2106.14855
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.23.0/mmseg/models/decode_heads/knet_head.py#L392
Framework: PyTorch
- Name: knet-s3_swin-l_upernet_8xb2-adamw-80k_ade20k-640x640
In Collection: KNet
Results:
Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 52.21
mIoU(ms+flip): 53.34
Config: configs/knet/knet-s3_swin-l_upernet_8xb2-adamw-80k_ade20k-640x640.py
Metadata:
Training Data: ADE20K
Batch Size: 16
Architecture:
- Swin-L
- KNet
- UperNet
Training Resources: 8x V100 GPUS
Memory (GB): 13.54
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/knet/knet_s3_upernet_swin-l_8x2_640x640_adamw_80k_ade20k/knet_s3_upernet_swin-l_8x2_640x640_adamw_80k_ade20k_20220301_220747-8787fc71.pth
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/knet/knet_s3_upernet_swin-l_8x2_640x640_adamw_80k_ade20k/knet_s3_upernet_swin-l_8x2_640x640_adamw_80k_ade20k_20220301_220747.log.json
Paper:
Title: 'K-Net: Towards Unified Image Segmentation'
URL: https://arxiv.org/abs/2106.14855
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.23.0/mmseg/models/decode_heads/knet_head.py#L392
Framework: PyTorch
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