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mmsegmentation/configs/segformer/segformer.yml

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Collections:
- Name: Segformer
Metadata:
Training Data:
- ADE20K
- Cityscapes
Paper:
URL: https://arxiv.org/abs/2105.15203
Title: 'SegFormer: Simple and Efficient Design for Semantic Segmentation with
Transformers'
README: configs/segformer/README.md
Code:
URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/mit.py#L246
Version: v0.17.0
Converted From:
Code: https://github.com/NVlabs/SegFormer
Models:
- Name: segformer_mit-b0_512x512_160k_ade20k
In Collection: Segformer
Metadata:
backbone: MIT-B0
crop size: (512,512)
lr schd: 160000
inference time (ms/im):
- value: 26.2
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 2.1
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 37.85
mIoU(ms+flip): 38.97
Config: configs/segformer/segformer_mit-b0_512x512_160k_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b0_512x512_160k_ade20k/segformer_mit-b0_512x512_160k_ade20k_20220617_162207-c00b9603.pth
- Name: segformer_mit-b1_512x512_160k_ade20k
In Collection: Segformer
Metadata:
backbone: MIT-B1
crop size: (512,512)
lr schd: 160000
inference time (ms/im):
- value: 26.46
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 2.6
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 42.13
mIoU(ms+flip): 43.74
Config: configs/segformer/segformer_mit-b1_512x512_160k_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b1_512x512_160k_ade20k/segformer_mit-b1_512x512_160k_ade20k_20220620_112037-c3f39e00.pth
- Name: segformer_mit-b2_512x512_160k_ade20k
In Collection: Segformer
Metadata:
backbone: MIT-B2
crop size: (512,512)
lr schd: 160000
inference time (ms/im):
- value: 37.31
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 3.6
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 46.8
mIoU(ms+flip): 48.12
Config: configs/segformer/segformer_mit-b2_512x512_160k_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b2_512x512_160k_ade20k/segformer_mit-b2_512x512_160k_ade20k_20220620_114047-64e4feca.pth
- Name: segformer_mit-b3_512x512_160k_ade20k
In Collection: Segformer
Metadata:
backbone: MIT-B3
crop size: (512,512)
lr schd: 160000
inference time (ms/im):
- value: 52.11
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 4.8
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 48.25
mIoU(ms+flip): 49.58
Config: configs/segformer/segformer_mit-b3_512x512_160k_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b3_512x512_160k_ade20k/segformer_mit-b3_512x512_160k_ade20k_20220617_162254-3a4b7363.pth
- Name: segformer_mit-b4_512x512_160k_ade20k
In Collection: Segformer
Metadata:
backbone: MIT-B4
crop size: (512,512)
lr schd: 160000
inference time (ms/im):
- value: 68.78
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 6.1
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 49.09
mIoU(ms+flip): 50.72
Config: configs/segformer/segformer_mit-b4_512x512_160k_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b4_512x512_160k_ade20k/segformer_mit-b4_512x512_160k_ade20k_20220620_112216-4fa4f58f.pth
- Name: segformer_mit-b5_512x512_160k_ade20k
In Collection: Segformer
Metadata:
backbone: MIT-B5
crop size: (512,512)
lr schd: 160000
inference time (ms/im):
- value: 84.1
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 7.2
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 49.13
mIoU(ms+flip): 50.22
Config: configs/segformer/segformer_mit-b5_512x512_160k_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b5_512x512_160k_ade20k/segformer_mit-b5_512x512_160k_ade20k_20210726_145235-94cedf59.pth
- Name: segformer_mit-b5_640x640_160k_ade20k
In Collection: Segformer
Metadata:
backbone: MIT-B5
crop size: (640,640)
lr schd: 160000
inference time (ms/im):
- value: 94.34
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (640,640)
Training Memory (GB): 11.5
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 50.19
mIoU(ms+flip): 51.41
Config: configs/segformer/segformer_mit-b5_640x640_160k_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b5_640x640_160k_ade20k/segformer_mit-b5_640x640_160k_ade20k_20220617_203542-940a6bd8.pth
- Name: segformer_mit-b0_8x1_1024x1024_160k_cityscapes
In Collection: Segformer
Metadata:
backbone: MIT-B0
crop size: (1024,1024)
lr schd: 160000
inference time (ms/im):
- value: 210.97
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (1024,1024)
Training Memory (GB): 3.64
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 76.54
mIoU(ms+flip): 78.22
Config: configs/segformer/segformer_mit-b0_8x1_1024x1024_160k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b0_8x1_1024x1024_160k_cityscapes/segformer_mit-b0_8x1_1024x1024_160k_cityscapes_20211208_101857-e7f88502.pth
- Name: segformer_mit-b1_8x1_1024x1024_160k_cityscapes
In Collection: Segformer
Metadata:
backbone: MIT-B1
crop size: (1024,1024)
lr schd: 160000
inference time (ms/im):
- value: 232.56
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (1024,1024)
Training Memory (GB): 4.49
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 78.56
mIoU(ms+flip): 79.73
Config: configs/segformer/segformer_mit-b1_8x1_1024x1024_160k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b1_8x1_1024x1024_160k_cityscapes/segformer_mit-b1_8x1_1024x1024_160k_cityscapes_20211208_064213-655c7b3f.pth
- Name: segformer_mit-b2_8x1_1024x1024_160k_cityscapes
In Collection: Segformer
Metadata:
backbone: MIT-B2
crop size: (1024,1024)
lr schd: 160000
inference time (ms/im):
- value: 297.62
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (1024,1024)
Training Memory (GB): 7.42
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 81.08
mIoU(ms+flip): 82.18
Config: configs/segformer/segformer_mit-b2_8x1_1024x1024_160k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b2_8x1_1024x1024_160k_cityscapes/segformer_mit-b2_8x1_1024x1024_160k_cityscapes_20211207_134205-6096669a.pth
- Name: segformer_mit-b3_8x1_1024x1024_160k_cityscapes
In Collection: Segformer
Metadata:
backbone: MIT-B3
crop size: (1024,1024)
lr schd: 160000
inference time (ms/im):
- value: 395.26
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (1024,1024)
Training Memory (GB): 10.86
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 81.94
mIoU(ms+flip): 83.14
Config: configs/segformer/segformer_mit-b3_8x1_1024x1024_160k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b3_8x1_1024x1024_160k_cityscapes/segformer_mit-b3_8x1_1024x1024_160k_cityscapes_20211206_224823-a8f8a177.pth
- Name: segformer_mit-b4_8x1_1024x1024_160k_cityscapes
In Collection: Segformer
Metadata:
backbone: MIT-B4
crop size: (1024,1024)
lr schd: 160000
inference time (ms/im):
- value: 531.91
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (1024,1024)
Training Memory (GB): 15.07
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 81.89
mIoU(ms+flip): 83.38
Config: configs/segformer/segformer_mit-b4_8x1_1024x1024_160k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b4_8x1_1024x1024_160k_cityscapes/segformer_mit-b4_8x1_1024x1024_160k_cityscapes_20211207_080709-07f6c333.pth
- Name: segformer_mit-b5_8x1_1024x1024_160k_cityscapes
In Collection: Segformer
Metadata:
backbone: MIT-B5
crop size: (1024,1024)
lr schd: 160000
inference time (ms/im):
- value: 719.42
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (1024,1024)
Training Memory (GB): 18.0
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 82.25
mIoU(ms+flip): 83.48
Config: configs/segformer/segformer_mit-b5_8x1_1024x1024_160k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b5_8x1_1024x1024_160k_cityscapes/segformer_mit-b5_8x1_1024x1024_160k_cityscapes_20211206_072934-87a052ec.pth
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