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mmsegmentation/configs/mask2former/mask2former.yml

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Collections:
- Name: Mask2Former
Metadata:
Training Data:
- Usage
- Cityscapes
- ADE20K
Paper:
URL: https://arxiv.org/abs/2112.01527
Title: Masked-attention Mask Transformer for Universal Image Segmentation
README: configs/mask2former/README.md
Code:
URL: https://github.com/open-mmlab/mmdetection/blob/3.x/mmdet/models/dense_heads/mask2former_head.py
Version: 3.x
Converted From:
Code: https://github.com/facebookresearch/Mask2Former
Models:
- Name: mask2former_r50_8xb2-90k_cityscapes-512x1024
In Collection: Mask2Former
Metadata:
backbone: R-50-D32
crop size: (512,1024)
lr schd: 90000
inference time (ms/im):
- value: 109.05
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,1024)
Training Memory (GB): 5806.0
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 80.44
Config: configs/mask2former/mask2former_r50_8xb2-90k_cityscapes-512x1024.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_r50_8xb2-90k_cityscapes-512x1024/mask2former_r50_8xb2-90k_cityscapes-512x1024_20221202_140802-ffd9d750.pth
- Name: mask2former_r101_8xb2-90k_cityscapes-512x1024
In Collection: Mask2Former
Metadata:
backbone: R-101-D32
crop size: (512,1024)
lr schd: 90000
inference time (ms/im):
- value: 140.65
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,1024)
Training Memory (GB): 6971.0
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 80.8
Config: configs/mask2former/mask2former_r101_8xb2-90k_cityscapes-512x1024.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_r101_8xb2-90k_cityscapes-512x1024/mask2former_r101_8xb2-90k_cityscapes-512x1024_20221130_031628-43e68666.pth
- Name: mask2former_swin-t_8xb2-90k_cityscapes-512x1024
In Collection: Mask2Former
Metadata:
backbone: Swin-T
crop size: (512,1024)
lr schd: 90000
inference time (ms/im):
- value: 139.28
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,1024)
Training Memory (GB): 6511.0
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 81.71
Config: configs/mask2former/mask2former_swin-t_8xb2-90k_cityscapes-512x1024.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_swin-t_8xb2-90k_cityscapes-512x1024/mask2former_swin-t_8xb2-90k_cityscapes-512x1024_20221127_144501-36c59341.pth
- Name: mask2former_swin-s_8xb2-90k_cityscapes-512x1024
In Collection: Mask2Former
Metadata:
backbone: Swin-S
crop size: (512,1024)
lr schd: 90000
inference time (ms/im):
- value: 179.53
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,1024)
Training Memory (GB): 8282.0
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 82.57
Config: configs/mask2former/mask2former_swin-s_8xb2-90k_cityscapes-512x1024.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_swin-s_8xb2-90k_cityscapes-512x1024/mask2former_swin-s_8xb2-90k_cityscapes-512x1024_20221127_143802-9ab177f6.pth
- Name: mask2former_swin-b-in22k-384x384-pre_8xb2-90k_cityscapes-512x1024
In Collection: Mask2Former
Metadata:
backbone: Swin-B (in22k)
crop size: (512,1024)
lr schd: 90000
inference time (ms/im):
- value: 231.48
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,1024)
Training Memory (GB): 11152.0
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 83.52
Config: configs/mask2former/mask2former_swin-b-in22k-384x384-pre_8xb2-90k_cityscapes-512x1024.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_swin-b-in22k-384x384-pre_8xb2-90k_cityscapes-512x1024/mask2former_swin-b-in22k-384x384-pre_8xb2-90k_cityscapes-512x1024_20221203_045030-9a86a225.pth
- Name: mask2former_swin-l-in22k-384x384-pre_8xb2-90k_cityscapes-512x1024
In Collection: Mask2Former
Metadata:
backbone: Swin-L (in22k)
crop size: (512,1024)
lr schd: 90000
inference time (ms/im):
- value: 349.65
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,1024)
Training Memory (GB): 16207.0
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 83.65
Config: configs/mask2former/mask2former_swin-l-in22k-384x384-pre_8xb2-90k_cityscapes-512x1024.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_swin-l-in22k-384x384-pre_8xb2-90k_cityscapes-512x1024/mask2former_swin-l-in22k-384x384-pre_8xb2-90k_cityscapes-512x1024_20221202_141901-28ad20f1.pth
- Name: mask2former_r50_8xb2-160k_ade20k-512x512
In Collection: Mask2Former
Metadata:
backbone: R-50-D32
crop size: (512,512)
lr schd: 160000
inference time (ms/im):
- value: 37.61
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 3385.0
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 47.87
Config: configs/mask2former/mask2former_r50_8xb2-160k_ade20k-512x512.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_r50_8xb2-160k_ade20k-512x512/mask2former_r50_8xb2-160k_ade20k-512x512_20221204_000055-2d1f55f1.pth
- Name: mask2former_r101_8xb2-160k_ade20k-512x512
In Collection: Mask2Former
Metadata:
backbone: R-101-D32
crop size: (512,512)
lr schd: 160000
inference time (ms/im):
- value: 43.54
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 4190.0
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 48.6
Config: configs/mask2former/mask2former_r101_8xb2-160k_ade20k-512x512.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_r101_8xb2-160k_ade20k-512x512/mask2former_r101_8xb2-160k_ade20k-512x512_20221203_233905-b7135890.pth
- Name: mask2former_swin-t_8xb2-160k_ade20k-512x512
In Collection: Mask2Former
Metadata:
backbone: Swin-T
crop size: (512,512)
lr schd: 160000
inference time (ms/im):
- value: 41.98
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 3826.0
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 48.66
Config: configs/mask2former/mask2former_swin-t_8xb2-160k_ade20k-512x512.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_swin-t_8xb2-160k_ade20k-512x512/mask2former_swin-t_8xb2-160k_ade20k-512x512_20221203_234230-7d64e5dd.pth
- Name: mask2former_swin-s_8xb2-160k_ade20k-512x512
In Collection: Mask2Former
Metadata:
backbone: Swin-S
crop size: (512,512)
lr schd: 160000
inference time (ms/im):
- value: 50.79
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 5034.0
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 51.24
Config: configs/mask2former/mask2former_swin-s_8xb2-160k_ade20k-512x512.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_swin-s_8xb2-160k_ade20k-512x512/mask2former_swin-s_8xb2-160k_ade20k-512x512_20221204_143905-e715144e.pth
- Name: mask2former_swin-b-in1k-384x384-pre_8xb2-160k_ade20k-640x640
In Collection: Mask2Former
Metadata:
backbone: Swin-B
crop size: (640,640)
lr schd: 160000
inference time (ms/im):
- value: 80.13
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (640,640)
Training Memory (GB): 5795.0
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 52.44
Config: configs/mask2former/mask2former_swin-b-in1k-384x384-pre_8xb2-160k_ade20k-640x640.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_swin-b-in1k-384x384-pre_8xb2-160k_ade20k-640x640/mask2former_swin-b-in1k-384x384-pre_8xb2-160k_ade20k-640x640_20221129_125118-a4a086d2.pth
- Name: mask2former_swin-b-in22k-384x384-pre_8xb2-160k_ade20k-640x640
In Collection: Mask2Former
Metadata:
backbone: Swin-B (in22k)
crop size: (640,640)
lr schd: 160000
inference time (ms/im):
- value: 80.45
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (640,640)
Training Memory (GB): 5795.0
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 53.9
Config: configs/mask2former/mask2former_swin-b-in22k-384x384-pre_8xb2-160k_ade20k-640x640.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_swin-b-in22k-384x384-pre_8xb2-160k_ade20k-640x640/mask2former_swin-b-in22k-384x384-pre_8xb2-160k_ade20k-640x640_20221203_235230-7ec0f569.pth
- Name: mask2former_swin-l-in22k-384x384-pre_8xb2-160k_ade20k-640x640
In Collection: Mask2Former
Metadata:
backbone: Swin-L (in22k)
crop size: (640,640)
lr schd: 160000
inference time (ms/im):
- value: 113.51
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (640,640)
Training Memory (GB): 9077.0
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 56.01
Config: configs/mask2former/mask2former_swin-l-in22k-384x384-pre_8xb2-160k_ade20k-640x640.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_swin-l-in22k-384x384-pre_8xb2-160k_ade20k-640x640/mask2former_swin-l-in22k-384x384-pre_8xb2-160k_ade20k-640x640_20221203_235933-7120c214.pth
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