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| Collections: | |
| - Name: Mask2Former | |
| License: Apache License 2.0 | |
| Metadata: | |
| Training Data: | |
| - Usage | |
| - Cityscapes | |
| - ADE20K | |
| Paper: | |
| Title: Masked-attention Mask Transformer for Universal Image Segmentation | |
| URL: https://arxiv.org/abs/2112.01527 | |
| README: configs/mask2former/README.md | |
| Frameworks: | |
| - PyTorch | |
| Models: | |
| - Name: mask2former_r50_8xb2-90k_cityscapes-512x1024 | |
| In Collection: Mask2Former | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Cityscapes | |
| Metrics: | |
| mIoU: 80.44 | |
| Config: configs/mask2former/mask2former_r50_8xb2-90k_cityscapes-512x1024.py | |
| Metadata: | |
| Training Data: Cityscapes | |
| Batch Size: 16 | |
| Architecture: | |
| - R-50-D32 | |
| - Mask2Former | |
| Training Resources: 8x A100 GPUS | |
| Memory (GB): 5.67 | |
| 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 | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_r50_8xb2-90k_cityscapes-512x1024/mask2former_r50_8xb2-90k_cityscapes-512x1024_20221202_140802.json | |
| Paper: | |
| Title: Masked-attention Mask Transformer for Universal Image Segmentation | |
| URL: https://arxiv.org/abs/2112.01527 | |
| Code: https://github.com/open-mmlab/mmdetection/blob/3.x/mmdet/models/dense_heads/mask2former_head.py | |
| Framework: PyTorch | |
| - Name: mask2former_r101_8xb2-90k_cityscapes-512x1024 | |
| In Collection: Mask2Former | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Cityscapes | |
| Metrics: | |
| mIoU: 80.8 | |
| Config: configs/mask2former/mask2former_r101_8xb2-90k_cityscapes-512x1024.py | |
| Metadata: | |
| Training Data: Cityscapes | |
| Batch Size: 16 | |
| Architecture: | |
| - R-101-D32 | |
| - Mask2Former | |
| Training Resources: 8x A100 GPUS | |
| Memory (GB): 6.81 | |
| 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 | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_r101_8xb2-90k_cityscapes-512x1024/mask2former_r101_8xb2-90k_cityscapes-512x1024_20221130_031628.json | |
| Paper: | |
| Title: Masked-attention Mask Transformer for Universal Image Segmentation | |
| URL: https://arxiv.org/abs/2112.01527 | |
| Code: https://github.com/open-mmlab/mmdetection/blob/3.x/mmdet/models/dense_heads/mask2former_head.py | |
| Framework: PyTorch | |
| - Name: mask2former_swin-t_8xb2-90k_cityscapes-512x1024 | |
| In Collection: Mask2Former | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Cityscapes | |
| Metrics: | |
| mIoU: 81.71 | |
| Config: configs/mask2former/mask2former_swin-t_8xb2-90k_cityscapes-512x1024.py | |
| Metadata: | |
| Training Data: Cityscapes | |
| Batch Size: 16 | |
| Architecture: | |
| - Swin-T | |
| - Mask2Former | |
| Training Resources: 8x A100 GPUS | |
| Memory (GB): 6.36 | |
| 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 | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_swin-t_8xb2-90k_cityscapes-512x1024/mask2former_swin-t_8xb2-90k_cityscapes-512x1024_20221127_144501.json | |
| Paper: | |
| Title: Masked-attention Mask Transformer for Universal Image Segmentation | |
| URL: https://arxiv.org/abs/2112.01527 | |
| Code: https://github.com/open-mmlab/mmdetection/blob/3.x/mmdet/models/dense_heads/mask2former_head.py | |
| Framework: PyTorch | |
| - Name: mask2former_swin-s_8xb2-90k_cityscapes-512x1024 | |
| In Collection: Mask2Former | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Cityscapes | |
| Metrics: | |
| mIoU: 82.57 | |
| Config: configs/mask2former/mask2former_swin-s_8xb2-90k_cityscapes-512x1024.py | |
| Metadata: | |
| Training Data: Cityscapes | |
| Batch Size: 16 | |
| Architecture: | |
| - Swin-S | |
| - Mask2Former | |
| Training Resources: 8x A100 GPUS | |
| Memory (GB): 8.09 | |
| 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 | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_swin-s_8xb2-90k_cityscapes-512x1024/mask2former_swin-s_8xb2-90k_cityscapes-512x1024_20221127_143802.json | |
| Paper: | |
| Title: Masked-attention Mask Transformer for Universal Image Segmentation | |
| URL: https://arxiv.org/abs/2112.01527 | |
| Code: https://github.com/open-mmlab/mmdetection/blob/3.x/mmdet/models/dense_heads/mask2former_head.py | |
| Framework: PyTorch | |
| - Name: mask2former_swin-b-in22k-384x384-pre_8xb2-90k_cityscapes-512x1024 | |
| In Collection: Mask2Former | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Cityscapes | |
| Metrics: | |
| mIoU: 83.52 | |
| Config: configs/mask2former/mask2former_swin-b-in22k-384x384-pre_8xb2-90k_cityscapes-512x1024.py | |
| Metadata: | |
| Training Data: Cityscapes | |
| Batch Size: 16 | |
| Architecture: | |
| - Swin-B | |
| - Mask2Former | |
| Training Resources: 8x A100 GPUS | |
| Memory (GB): 10.89 | |
| 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 | |
| Training log: 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.json | |
| Paper: | |
| Title: Masked-attention Mask Transformer for Universal Image Segmentation | |
| URL: https://arxiv.org/abs/2112.01527 | |
| Code: https://github.com/open-mmlab/mmdetection/blob/3.x/mmdet/models/dense_heads/mask2former_head.py | |
| Framework: PyTorch | |
| - Name: mask2former_swin-l-in22k-384x384-pre_8xb2-90k_cityscapes-512x1024 | |
| In Collection: Mask2Former | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Cityscapes | |
| Metrics: | |
| mIoU: 83.65 | |
| Config: configs/mask2former/mask2former_swin-l-in22k-384x384-pre_8xb2-90k_cityscapes-512x1024.py | |
| Metadata: | |
| Training Data: Cityscapes | |
| Batch Size: 16 | |
| Architecture: | |
| - Swin-L | |
| - Mask2Former | |
| Training Resources: 8x A100 GPUS | |
| Memory (GB): 15.83 | |
| 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 | |
| Training log: 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.json | |
| Paper: | |
| Title: Masked-attention Mask Transformer for Universal Image Segmentation | |
| URL: https://arxiv.org/abs/2112.01527 | |
| Code: https://github.com/open-mmlab/mmdetection/blob/3.x/mmdet/models/dense_heads/mask2former_head.py | |
| Framework: PyTorch | |
| - Name: mask2former_r50_8xb2-160k_ade20k-512x512 | |
| In Collection: Mask2Former | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: ADE20K | |
| Metrics: | |
| mIoU: 47.87 | |
| Config: configs/mask2former/mask2former_r50_8xb2-160k_ade20k-512x512.py | |
| Metadata: | |
| Training Data: ADE20K | |
| Batch Size: 16 | |
| Architecture: | |
| - R-50-D32 | |
| - Mask2Former | |
| Training Resources: 8x A100 GPUS | |
| Memory (GB): 3.31 | |
| 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 | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_r50_8xb2-160k_ade20k-512x512/mask2former_r50_8xb2-160k_ade20k-512x512_20221204_000055.json | |
| Paper: | |
| Title: Masked-attention Mask Transformer for Universal Image Segmentation | |
| URL: https://arxiv.org/abs/2112.01527 | |
| Code: https://github.com/open-mmlab/mmdetection/blob/3.x/mmdet/models/dense_heads/mask2former_head.py | |
| Framework: PyTorch | |
| - Name: mask2former_r101_8xb2-160k_ade20k-512x512 | |
| In Collection: Mask2Former | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: ADE20K | |
| Metrics: | |
| mIoU: 48.6 | |
| Config: configs/mask2former/mask2former_r101_8xb2-160k_ade20k-512x512.py | |
| Metadata: | |
| Training Data: ADE20K | |
| Batch Size: 16 | |
| Architecture: | |
| - R-101-D32 | |
| - Mask2Former | |
| Training Resources: 8x A100 GPUS | |
| Memory (GB): 4.09 | |
| 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 | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_r101_8xb2-160k_ade20k-512x512/mask2former_r101_8xb2-160k_ade20k-512x512_20221203_233905.json | |
| Paper: | |
| Title: Masked-attention Mask Transformer for Universal Image Segmentation | |
| URL: https://arxiv.org/abs/2112.01527 | |
| Code: https://github.com/open-mmlab/mmdetection/blob/3.x/mmdet/models/dense_heads/mask2former_head.py | |
| Framework: PyTorch | |
| - Name: mask2former_swin-t_8xb2-160k_ade20k-512x512 | |
| In Collection: Mask2Former | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: ADE20K | |
| Metrics: | |
| mIoU: 48.66 | |
| Config: configs/mask2former/mask2former_swin-t_8xb2-160k_ade20k-512x512.py | |
| Metadata: | |
| Training Data: ADE20K | |
| Batch Size: 16 | |
| Architecture: | |
| - Swin-T | |
| - Mask2Former | |
| Training Resources: 8x A100 GPUS | |
| Memory (GB): 3826.0 | |
| 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 | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_swin-t_8xb2-160k_ade20k-512x512/mask2former_swin-t_8xb2-160k_ade20k-512x512_20221203_234230.json | |
| Paper: | |
| Title: Masked-attention Mask Transformer for Universal Image Segmentation | |
| URL: https://arxiv.org/abs/2112.01527 | |
| Code: https://github.com/open-mmlab/mmdetection/blob/3.x/mmdet/models/dense_heads/mask2former_head.py | |
| Framework: PyTorch | |
| - Name: mask2former_swin-s_8xb2-160k_ade20k-512x512 | |
| In Collection: Mask2Former | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: ADE20K | |
| Metrics: | |
| mIoU: 51.24 | |
| Config: configs/mask2former/mask2former_swin-s_8xb2-160k_ade20k-512x512.py | |
| Metadata: | |
| Training Data: ADE20K | |
| Batch Size: 16 | |
| Architecture: | |
| - Swin-S | |
| - Mask2Former | |
| Training Resources: 8x A100 GPUS | |
| Memory (GB): 3.74 | |
| 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 | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_swin-s_8xb2-160k_ade20k-512x512/mask2former_swin-s_8xb2-160k_ade20k-512x512_20221204_143905.json | |
| Paper: | |
| Title: Masked-attention Mask Transformer for Universal Image Segmentation | |
| URL: https://arxiv.org/abs/2112.01527 | |
| Code: https://github.com/open-mmlab/mmdetection/blob/3.x/mmdet/models/dense_heads/mask2former_head.py | |
| Framework: PyTorch | |
| - Name: mask2former_swin-b-in1k-384x384-pre_8xb2-160k_ade20k-640x640 | |
| In Collection: Mask2Former | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: ADE20K | |
| Metrics: | |
| mIoU: 52.44 | |
| Config: configs/mask2former/mask2former_swin-b-in1k-384x384-pre_8xb2-160k_ade20k-640x640.py | |
| Metadata: | |
| Training Data: ADE20K | |
| Batch Size: 16 | |
| Architecture: | |
| - Swin-B | |
| - Mask2Former | |
| Training Resources: 8x A100 GPUS | |
| Memory (GB): 5.66 | |
| 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 | |
| Training log: 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.json | |
| Paper: | |
| Title: Masked-attention Mask Transformer for Universal Image Segmentation | |
| URL: https://arxiv.org/abs/2112.01527 | |
| Code: https://github.com/open-mmlab/mmdetection/blob/3.x/mmdet/models/dense_heads/mask2former_head.py | |
| Framework: PyTorch | |
| - Name: mask2former_swin-b-in22k-384x384-pre_8xb2-160k_ade20k-640x640 | |
| In Collection: Mask2Former | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: ADE20K | |
| Metrics: | |
| mIoU: 53.9 | |
| Config: configs/mask2former/mask2former_swin-b-in22k-384x384-pre_8xb2-160k_ade20k-640x640.py | |
| Metadata: | |
| Training Data: ADE20K | |
| Batch Size: 16 | |
| Architecture: | |
| - Swin-B | |
| - Mask2Former | |
| Training Resources: 8x A100 GPUS | |
| Memory (GB): 5.66 | |
| 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 | |
| Training log: 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.json | |
| Paper: | |
| Title: Masked-attention Mask Transformer for Universal Image Segmentation | |
| URL: https://arxiv.org/abs/2112.01527 | |
| Code: https://github.com/open-mmlab/mmdetection/blob/3.x/mmdet/models/dense_heads/mask2former_head.py | |
| Framework: PyTorch | |
| - Name: mask2former_swin-l-in22k-384x384-pre_8xb2-160k_ade20k-640x640 | |
| In Collection: Mask2Former | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: ADE20K | |
| Metrics: | |
| mIoU: 56.01 | |
| Config: configs/mask2former/mask2former_swin-l-in22k-384x384-pre_8xb2-160k_ade20k-640x640.py | |
| Metadata: | |
| Training Data: ADE20K | |
| Batch Size: 16 | |
| Architecture: | |
| - Swin-L | |
| - Mask2Former | |
| Training Resources: 8x A100 GPUS | |
| Memory (GB): 8.86 | |
| 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 | |
| Training log: 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.json | |
| Paper: | |
| Title: Masked-attention Mask Transformer for Universal Image Segmentation | |
| URL: https://arxiv.org/abs/2112.01527 | |
| Code: https://github.com/open-mmlab/mmdetection/blob/3.x/mmdet/models/dense_heads/mask2former_head.py | |
| Framework: PyTorch | |