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- Models:
- - Name: upernet_vit-b16_mln_512x512_80k_ade20k
- In Collection: UPerNet
- Metadata:
- backbone: ViT-B + MLN
- crop size: (512,512)
- lr schd: 80000
- inference time (ms/im):
- - value: 144.09
- hardware: V100
- backend: PyTorch
- batch size: 1
- mode: FP32
- resolution: (512,512)
- Training Memory (GB): 9.2
- Results:
- - Task: Semantic Segmentation
- Dataset: ADE20K
- Metrics:
- mIoU: 47.71
- mIoU(ms+flip): 49.51
- Config: configs/vit/upernet_vit-b16_mln_512x512_80k_ade20k.py
- Weights: https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_vit-b16_mln_512x512_80k_ade20k/upernet_vit-b16_mln_512x512_80k_ade20k_20210624_130547-0403cee1.pth
- - Name: upernet_vit-b16_mln_512x512_160k_ade20k
- In Collection: UPerNet
- Metadata:
- backbone: ViT-B + MLN
- crop size: (512,512)
- lr schd: 160000
- inference time (ms/im):
- - value: 131.93
- hardware: V100
- backend: PyTorch
- batch size: 1
- mode: FP32
- resolution: (512,512)
- Training Memory (GB): 9.2
- Results:
- - Task: Semantic Segmentation
- Dataset: ADE20K
- Metrics:
- mIoU: 46.75
- mIoU(ms+flip): 48.46
- Config: configs/vit/upernet_vit-b16_mln_512x512_160k_ade20k.py
- Weights: https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_vit-b16_mln_512x512_160k_ade20k/upernet_vit-b16_mln_512x512_160k_ade20k_20210624_130547-852fa768.pth
- - Name: upernet_vit-b16_ln_mln_512x512_160k_ade20k
- In Collection: UPerNet
- Metadata:
- backbone: ViT-B + LN + MLN
- crop size: (512,512)
- lr schd: 160000
- inference time (ms/im):
- - value: 146.63
- hardware: V100
- backend: PyTorch
- batch size: 1
- mode: FP32
- resolution: (512,512)
- Training Memory (GB): 9.21
- Results:
- - Task: Semantic Segmentation
- Dataset: ADE20K
- Metrics:
- mIoU: 47.73
- mIoU(ms+flip): 49.95
- Config: configs/vit/upernet_vit-b16_ln_mln_512x512_160k_ade20k.py
- Weights: https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_vit-b16_ln_mln_512x512_160k_ade20k/upernet_vit-b16_ln_mln_512x512_160k_ade20k_20210621_172828-f444c077.pth
- - Name: upernet_deit-s16_512x512_80k_ade20k
- In Collection: UPerNet
- Metadata:
- backbone: DeiT-S
- crop size: (512,512)
- lr schd: 80000
- inference time (ms/im):
- - value: 33.5
- hardware: V100
- backend: PyTorch
- batch size: 1
- mode: FP32
- resolution: (512,512)
- Training Memory (GB): 4.68
- Results:
- - Task: Semantic Segmentation
- Dataset: ADE20K
- Metrics:
- mIoU: 42.96
- mIoU(ms+flip): 43.79
- Config: configs/vit/upernet_deit-s16_512x512_80k_ade20k.py
- Weights: https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_deit-s16_512x512_80k_ade20k/upernet_deit-s16_512x512_80k_ade20k_20210624_095228-afc93ec2.pth
- - Name: upernet_deit-s16_512x512_160k_ade20k
- In Collection: UPerNet
- Metadata:
- backbone: DeiT-S
- crop size: (512,512)
- lr schd: 160000
- inference time (ms/im):
- - value: 34.26
- hardware: V100
- backend: PyTorch
- batch size: 1
- mode: FP32
- resolution: (512,512)
- Training Memory (GB): 4.68
- Results:
- - Task: Semantic Segmentation
- Dataset: ADE20K
- Metrics:
- mIoU: 42.87
- mIoU(ms+flip): 43.79
- Config: configs/vit/upernet_deit-s16_512x512_160k_ade20k.py
- Weights: https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_deit-s16_512x512_160k_ade20k/upernet_deit-s16_512x512_160k_ade20k_20210621_160903-5110d916.pth
- - Name: upernet_deit-s16_mln_512x512_160k_ade20k
- In Collection: UPerNet
- Metadata:
- backbone: DeiT-S + MLN
- crop size: (512,512)
- lr schd: 160000
- inference time (ms/im):
- - value: 89.45
- hardware: V100
- backend: PyTorch
- batch size: 1
- mode: FP32
- resolution: (512,512)
- Training Memory (GB): 5.69
- Results:
- - Task: Semantic Segmentation
- Dataset: ADE20K
- Metrics:
- mIoU: 43.82
- mIoU(ms+flip): 45.07
- Config: configs/vit/upernet_deit-s16_mln_512x512_160k_ade20k.py
- Weights: https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_deit-s16_mln_512x512_160k_ade20k/upernet_deit-s16_mln_512x512_160k_ade20k_20210621_161021-fb9a5dfb.pth
- - Name: upernet_deit-s16_ln_mln_512x512_160k_ade20k
- In Collection: UPerNet
- Metadata:
- backbone: DeiT-S + LN + MLN
- crop size: (512,512)
- lr schd: 160000
- inference time (ms/im):
- - value: 80.71
- hardware: V100
- backend: PyTorch
- batch size: 1
- mode: FP32
- resolution: (512,512)
- Training Memory (GB): 5.69
- Results:
- - Task: Semantic Segmentation
- Dataset: ADE20K
- Metrics:
- mIoU: 43.52
- mIoU(ms+flip): 45.01
- Config: configs/vit/upernet_deit-s16_ln_mln_512x512_160k_ade20k.py
- Weights: https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_deit-s16_ln_mln_512x512_160k_ade20k/upernet_deit-s16_ln_mln_512x512_160k_ade20k_20210621_161021-c0cd652f.pth
- - Name: upernet_deit-b16_512x512_80k_ade20k
- In Collection: UPerNet
- Metadata:
- backbone: DeiT-B
- crop size: (512,512)
- lr schd: 80000
- inference time (ms/im):
- - value: 103.2
- hardware: V100
- backend: PyTorch
- batch size: 1
- mode: FP32
- resolution: (512,512)
- Training Memory (GB): 7.75
- Results:
- - Task: Semantic Segmentation
- Dataset: ADE20K
- Metrics:
- mIoU: 45.24
- mIoU(ms+flip): 46.73
- Config: configs/vit/upernet_deit-b16_512x512_80k_ade20k.py
- Weights: https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_deit-b16_512x512_80k_ade20k/upernet_deit-b16_512x512_80k_ade20k_20210624_130529-1e090789.pth
- - Name: upernet_deit-b16_512x512_160k_ade20k
- In Collection: UPerNet
- Metadata:
- backbone: DeiT-B
- crop size: (512,512)
- lr schd: 160000
- inference time (ms/im):
- - value: 96.25
- hardware: V100
- backend: PyTorch
- batch size: 1
- mode: FP32
- resolution: (512,512)
- Training Memory (GB): 7.75
- Results:
- - Task: Semantic Segmentation
- Dataset: ADE20K
- Metrics:
- mIoU: 45.36
- mIoU(ms+flip): 47.16
- Config: configs/vit/upernet_deit-b16_512x512_160k_ade20k.py
- Weights: https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_deit-b16_512x512_160k_ade20k/upernet_deit-b16_512x512_160k_ade20k_20210621_180100-828705d7.pth
- - Name: upernet_deit-b16_mln_512x512_160k_ade20k
- In Collection: UPerNet
- Metadata:
- backbone: DeiT-B + MLN
- crop size: (512,512)
- lr schd: 160000
- inference time (ms/im):
- - value: 128.53
- hardware: V100
- backend: PyTorch
- batch size: 1
- mode: FP32
- resolution: (512,512)
- Training Memory (GB): 9.21
- Results:
- - Task: Semantic Segmentation
- Dataset: ADE20K
- Metrics:
- mIoU: 45.46
- mIoU(ms+flip): 47.16
- Config: configs/vit/upernet_deit-b16_mln_512x512_160k_ade20k.py
- Weights: https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_deit-b16_mln_512x512_160k_ade20k/upernet_deit-b16_mln_512x512_160k_ade20k_20210621_191949-4e1450f3.pth
- - Name: upernet_deit-b16_ln_mln_512x512_160k_ade20k
- In Collection: UPerNet
- Metadata:
- backbone: DeiT-B + LN + MLN
- crop size: (512,512)
- lr schd: 160000
- inference time (ms/im):
- - value: 129.03
- hardware: V100
- backend: PyTorch
- batch size: 1
- mode: FP32
- resolution: (512,512)
- Training Memory (GB): 9.21
- Results:
- - Task: Semantic Segmentation
- Dataset: ADE20K
- Metrics:
- mIoU: 45.37
- mIoU(ms+flip): 47.23
- Config: configs/vit/upernet_deit-b16_ln_mln_512x512_160k_ade20k.py
- Weights: https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_deit-b16_ln_mln_512x512_160k_ade20k/upernet_deit-b16_ln_mln_512x512_160k_ade20k_20210623_153535-8a959c14.pth
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