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knet.yml
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knet.yml
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Collections:
- Name: KNet
Metadata:
Training Data:
- ADE20K
Paper:
URL: https://arxiv.org/abs/2106.14855
Title: 'K-Net: Towards Unified Image Segmentation'
README: configs/knet/README.md
Code:
URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.23.0/mmseg/models/decode_heads/knet_head.py#L392
Version: v0.23.0
Converted From:
Code: https://github.com/ZwwWayne/K-Net/
Models:
- Name: knet_s3_fcn_r50-d8_8x2_512x512_adamw_80k_ade20k
In Collection: KNet
Metadata:
backbone: R-50-D8
crop size: (512,512)
lr schd: 80000
inference time (ms/im):
- value: 51.98
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 7.01
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 43.6
mIoU(ms+flip): 45.12
Config: configs/knet/knet_s3_fcn_r50-d8_8x2_512x512_adamw_80k_ade20k.py
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
- Name: knet_s3_pspnet_r50-d8_8x2_512x512_adamw_80k_ade20k
In Collection: KNet
Metadata:
backbone: R-50-D8
crop size: (512,512)
lr schd: 80000
inference time (ms/im):
- value: 49.9
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 6.98
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 44.18
mIoU(ms+flip): 45.58
Config: configs/knet/knet_s3_pspnet_r50-d8_8x2_512x512_adamw_80k_ade20k.py
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
- Name: knet_s3_deeplabv3_r50-d8_8x2_512x512_adamw_80k_ade20k
In Collection: KNet
Metadata:
backbone: R-50-D8
crop size: (512,512)
lr schd: 80000
inference time (ms/im):
- value: 82.64
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 7.42
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 45.06
mIoU(ms+flip): 46.11
Config: configs/knet/knet_s3_deeplabv3_r50-d8_8x2_512x512_adamw_80k_ade20k.py
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
- Name: knet_s3_upernet_r50-d8_8x2_512x512_adamw_80k_ade20k
In Collection: KNet
Metadata:
backbone: R-50-D8
crop size: (512,512)
lr schd: 80000
inference time (ms/im):
- value: 58.45
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 7.34
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 43.45
mIoU(ms+flip): 44.07
Config: configs/knet/knet_s3_upernet_r50-d8_8x2_512x512_adamw_80k_ade20k.py
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
- Name: knet_s3_upernet_swin-t_8x2_512x512_adamw_80k_ade20k
In Collection: KNet
Metadata:
backbone: Swin-T
crop size: (512,512)
lr schd: 80000
inference time (ms/im):
- value: 64.27
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 7.57
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 45.84
mIoU(ms+flip): 46.27
Config: configs/knet/knet_s3_upernet_swin-t_8x2_512x512_adamw_80k_ade20k.py
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
- Name: knet_s3_upernet_swin-l_8x2_512x512_adamw_80k_ade20k
In Collection: KNet
Metadata:
backbone: Swin-L
crop size: (512,512)
lr schd: 80000
inference time (ms/im):
- value: 120.63
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 13.5
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 52.05
mIoU(ms+flip): 53.24
Config: configs/knet/knet_s3_upernet_swin-l_8x2_512x512_adamw_80k_ade20k.py
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
- Name: knet_s3_upernet_swin-l_8x2_640x640_adamw_80k_ade20k
In Collection: KNet
Metadata:
backbone: Swin-L
crop size: (640,640)
lr schd: 80000
inference time (ms/im):
- value: 180.18
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (640,640)
Training Memory (GB): 18.31
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 52.46
mIoU(ms+flip): 53.78
Config: configs/knet/knet_s3_upernet_swin-l_8x2_640x640_adamw_80k_ade20k.py
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_20220720_165636-cbcaed32.pth