| Collections: | |
| - Name: Segformer | |
| License: Apache License 2.0 | |
| Metadata: | |
| Training Data: | |
| - ADE20K | |
| - Cityscapes | |
| Paper: | |
| Title: 'SegFormer: Simple and Efficient Design for Semantic Segmentation with | |
| Transformers' | |
| URL: https://arxiv.org/abs/2105.15203 | |
| README: configs/segformer/README.md | |
| Frameworks: | |
| - PyTorch | |
| Models: | |
| - Name: segformer_mit-b0_8xb2-160k_ade20k-512x512 | |
| In Collection: Segformer | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: ADE20K | |
| Metrics: | |
| mIoU: 37.41 | |
| mIoU(ms+flip): 38.34 | |
| Config: configs/segformer/segformer_mit-b0_8xb2-160k_ade20k-512x512.py | |
| Metadata: | |
| Training Data: ADE20K | |
| Batch Size: 16 | |
| Architecture: | |
| - MIT-B0 | |
| - Segformer | |
| Training Resources: 8x 1080 Ti GPUS | |
| Memory (GB): 2.1 | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b0_512x512_160k_ade20k/segformer_mit-b0_512x512_160k_ade20k_20210726_101530-8ffa8fda.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b0_512x512_160k_ade20k/segformer_mit-b0_512x512_160k_ade20k_20210726_101530.log.json | |
| Paper: | |
| Title: 'SegFormer: Simple and Efficient Design for Semantic Segmentation with | |
| Transformers' | |
| URL: https://arxiv.org/abs/2105.15203 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/mit.py#L246 | |
| Framework: PyTorch | |
| - Name: segformer_mit-b1_8xb2-160k_ade20k-512x512 | |
| In Collection: Segformer | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: ADE20K | |
| Metrics: | |
| mIoU: 40.97 | |
| mIoU(ms+flip): 42.54 | |
| Config: configs/segformer/segformer_mit-b1_8xb2-160k_ade20k-512x512.py | |
| Metadata: | |
| Training Data: ADE20K | |
| Batch Size: 16 | |
| Architecture: | |
| - MIT-B1 | |
| - Segformer | |
| Training Resources: 8x TITAN Xp GPUS | |
| Memory (GB): 2.6 | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b1_512x512_160k_ade20k/segformer_mit-b1_512x512_160k_ade20k_20210726_112106-d70e859d.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b1_512x512_160k_ade20k/segformer_mit-b1_512x512_160k_ade20k_20210726_112106.log.json | |
| Paper: | |
| Title: 'SegFormer: Simple and Efficient Design for Semantic Segmentation with | |
| Transformers' | |
| URL: https://arxiv.org/abs/2105.15203 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/mit.py#L246 | |
| Framework: PyTorch | |
| - Name: segformer_mit-b2_8xb2-160k_ade20k-512x512 | |
| In Collection: Segformer | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: ADE20K | |
| Metrics: | |
| mIoU: 45.58 | |
| mIoU(ms+flip): 47.03 | |
| Config: configs/segformer/segformer_mit-b2_8xb2-160k_ade20k-512x512.py | |
| Metadata: | |
| Training Data: ADE20K | |
| Batch Size: 16 | |
| Architecture: | |
| - MIT-B2 | |
| - Segformer | |
| Training Resources: 8x TITAN Xp GPUS | |
| Memory (GB): 3.6 | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b2_512x512_160k_ade20k/segformer_mit-b2_512x512_160k_ade20k_20210726_112103-cbd414ac.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b2_512x512_160k_ade20k/segformer_mit-b2_512x512_160k_ade20k_20210726_112103.log.json | |
| Paper: | |
| Title: 'SegFormer: Simple and Efficient Design for Semantic Segmentation with | |
| Transformers' | |
| URL: https://arxiv.org/abs/2105.15203 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/mit.py#L246 | |
| Framework: PyTorch | |
| - Name: segformer_mit-b3_8xb2-160k_ade20k-512x512 | |
| In Collection: Segformer | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: ADE20K | |
| Metrics: | |
| mIoU: 47.82 | |
| mIoU(ms+flip): 48.81 | |
| Config: configs/segformer/segformer_mit-b3_8xb2-160k_ade20k-512x512.py | |
| Metadata: | |
| Training Data: ADE20K | |
| Batch Size: 16 | |
| Architecture: | |
| - MIT-B3 | |
| - Segformer | |
| Training Resources: 8x V100 GPUS | |
| Memory (GB): 4.8 | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b3_512x512_160k_ade20k/segformer_mit-b3_512x512_160k_ade20k_20210726_081410-962b98d2.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b3_512x512_160k_ade20k/segformer_mit-b3_512x512_160k_ade20k_20210726_081410.log.json | |
| Paper: | |
| Title: 'SegFormer: Simple and Efficient Design for Semantic Segmentation with | |
| Transformers' | |
| URL: https://arxiv.org/abs/2105.15203 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/mit.py#L246 | |
| Framework: PyTorch | |
| - Name: segformer_mit-b4_8xb2-160k_ade20k-512x512 | |
| In Collection: Segformer | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: ADE20K | |
| Metrics: | |
| mIoU: 48.46 | |
| mIoU(ms+flip): 49.76 | |
| Config: configs/segformer/segformer_mit-b4_8xb2-160k_ade20k-512x512.py | |
| Metadata: | |
| Training Data: ADE20K | |
| Batch Size: 16 | |
| Architecture: | |
| - MIT-B4 | |
| - Segformer | |
| Training Resources: 8x V100 GPUS | |
| Memory (GB): 6.1 | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b4_512x512_160k_ade20k/segformer_mit-b4_512x512_160k_ade20k_20210728_183055-7f509d7d.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b4_512x512_160k_ade20k/segformer_mit-b4_512x512_160k_ade20k_20210728_183055.log.json | |
| Paper: | |
| Title: 'SegFormer: Simple and Efficient Design for Semantic Segmentation with | |
| Transformers' | |
| URL: https://arxiv.org/abs/2105.15203 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/mit.py#L246 | |
| Framework: PyTorch | |
| - Name: segformer_mit-b5_8xb2-160k_ade20k-512x512 | |
| In Collection: Segformer | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: ADE20K | |
| Metrics: | |
| mIoU: 49.13 | |
| mIoU(ms+flip): 50.22 | |
| Config: configs/segformer/segformer_mit-b5_8xb2-160k_ade20k-512x512.py | |
| Metadata: | |
| Training Data: ADE20K | |
| Batch Size: 16 | |
| Architecture: | |
| - MIT-B5 | |
| - Segformer | |
| Training Resources: 8x V100 GPUS | |
| Memory (GB): 7.2 | |
| 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 | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b5_512x512_160k_ade20k/segformer_mit-b5_512x512_160k_ade20k_20210726_145235.log.json | |
| Paper: | |
| Title: 'SegFormer: Simple and Efficient Design for Semantic Segmentation with | |
| Transformers' | |
| URL: https://arxiv.org/abs/2105.15203 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/mit.py#L246 | |
| Framework: PyTorch | |
| - Name: segformer_mit-b5_8xb2-160k_ade20k-640x640 | |
| In Collection: Segformer | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: ADE20K | |
| Metrics: | |
| mIoU: 49.62 | |
| mIoU(ms+flip): 50.36 | |
| Config: configs/segformer/segformer_mit-b5_8xb2-160k_ade20k-640x640.py | |
| Metadata: | |
| Training Data: ADE20K | |
| Batch Size: 16 | |
| Architecture: | |
| - MIT-B5 | |
| - Segformer | |
| Training Resources: 8x V100 GPUS | |
| Memory (GB): 11.5 | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b5_640x640_160k_ade20k/segformer_mit-b5_640x640_160k_ade20k_20210801_121243-41d2845b.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b5_640x640_160k_ade20k/segformer_mit-b5_640x640_160k_ade20k_20210801_121243.log.json | |
| Paper: | |
| Title: 'SegFormer: Simple and Efficient Design for Semantic Segmentation with | |
| Transformers' | |
| URL: https://arxiv.org/abs/2105.15203 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/mit.py#L246 | |
| Framework: PyTorch | |
| - Name: segformer_mit-b0_8xb1-160k_cityscapes-1024x1024 | |
| In Collection: Segformer | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Cityscapes | |
| Metrics: | |
| mIoU: 76.54 | |
| mIoU(ms+flip): 78.22 | |
| Config: configs/segformer/segformer_mit-b0_8xb1-160k_cityscapes-1024x1024.py | |
| Metadata: | |
| Training Data: Cityscapes | |
| Batch Size: 8 | |
| Architecture: | |
| - MIT-B0 | |
| - Segformer | |
| Training Resources: 8x V100 GPUS | |
| Memory (GB): 3.64 | |
| 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 | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b0_8x1_1024x1024_160k_cityscapes/segformer_mit-b0_8x1_1024x1024_160k_cityscapes_20211208_101857.log.json | |
| Paper: | |
| Title: 'SegFormer: Simple and Efficient Design for Semantic Segmentation with | |
| Transformers' | |
| URL: https://arxiv.org/abs/2105.15203 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/mit.py#L246 | |
| Framework: PyTorch | |
| - Name: segformer_mit-b1_8xb1-160k_cityscapes-1024x1024 | |
| In Collection: Segformer | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Cityscapes | |
| Metrics: | |
| mIoU: 78.56 | |
| mIoU(ms+flip): 79.73 | |
| Config: configs/segformer/segformer_mit-b1_8xb1-160k_cityscapes-1024x1024.py | |
| Metadata: | |
| Training Data: Cityscapes | |
| Batch Size: 8 | |
| Architecture: | |
| - MIT-B1 | |
| - Segformer | |
| Training Resources: 8x V100 GPUS | |
| Memory (GB): 4.49 | |
| 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 | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b1_8x1_1024x1024_160k_cityscapes/segformer_mit-b1_8x1_1024x1024_160k_cityscapes_20211208_064213.log.json | |
| Paper: | |
| Title: 'SegFormer: Simple and Efficient Design for Semantic Segmentation with | |
| Transformers' | |
| URL: https://arxiv.org/abs/2105.15203 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/mit.py#L246 | |
| Framework: PyTorch | |
| - Name: segformer_mit-b2_8xb1-160k_cityscapes-1024x1024 | |
| In Collection: Segformer | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Cityscapes | |
| Metrics: | |
| mIoU: 81.08 | |
| mIoU(ms+flip): 82.18 | |
| Config: configs/segformer/segformer_mit-b2_8xb1-160k_cityscapes-1024x1024.py | |
| Metadata: | |
| Training Data: Cityscapes | |
| Batch Size: 8 | |
| Architecture: | |
| - MIT-B2 | |
| - Segformer | |
| Training Resources: 8x V100 GPUS | |
| Memory (GB): 7.42 | |
| 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 | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b2_8x1_1024x1024_160k_cityscapes/segformer_mit-b2_8x1_1024x1024_160k_cityscapes_20211207_134205.log.json | |
| Paper: | |
| Title: 'SegFormer: Simple and Efficient Design for Semantic Segmentation with | |
| Transformers' | |
| URL: https://arxiv.org/abs/2105.15203 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/mit.py#L246 | |
| Framework: PyTorch | |
| - Name: segformer_mit-b3_8xb1-160k_cityscapes-1024x1024 | |
| In Collection: Segformer | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Cityscapes | |
| Metrics: | |
| mIoU: 81.94 | |
| mIoU(ms+flip): 83.14 | |
| Config: configs/segformer/segformer_mit-b3_8xb1-160k_cityscapes-1024x1024.py | |
| Metadata: | |
| Training Data: Cityscapes | |
| Batch Size: 8 | |
| Architecture: | |
| - MIT-B3 | |
| - Segformer | |
| Training Resources: 8x V100 GPUS | |
| Memory (GB): 10.86 | |
| 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 | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b3_8x1_1024x1024_160k_cityscapes/segformer_mit-b3_8x1_1024x1024_160k_cityscapes_20211206_224823.log.json | |
| Paper: | |
| Title: 'SegFormer: Simple and Efficient Design for Semantic Segmentation with | |
| Transformers' | |
| URL: https://arxiv.org/abs/2105.15203 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/mit.py#L246 | |
| Framework: PyTorch | |
| - Name: segformer_mit-b4_8xb1-160k_cityscapes-1024x1024 | |
| In Collection: Segformer | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Cityscapes | |
| Metrics: | |
| mIoU: 81.89 | |
| mIoU(ms+flip): 83.38 | |
| Config: configs/segformer/segformer_mit-b4_8xb1-160k_cityscapes-1024x1024.py | |
| Metadata: | |
| Training Data: Cityscapes | |
| Batch Size: 8 | |
| Architecture: | |
| - MIT-B4 | |
| - Segformer | |
| Training Resources: 8x V100 GPUS | |
| Memory (GB): 15.07 | |
| 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 | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b4_8x1_1024x1024_160k_cityscapes/segformer_mit-b4_8x1_1024x1024_160k_cityscapes_20211207_080709.log.json | |
| Paper: | |
| Title: 'SegFormer: Simple and Efficient Design for Semantic Segmentation with | |
| Transformers' | |
| URL: https://arxiv.org/abs/2105.15203 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/mit.py#L246 | |
| Framework: PyTorch | |
| - Name: segformer_mit-b5_8xb1-160k_cityscapes-1024x1024 | |
| In Collection: Segformer | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Cityscapes | |
| Metrics: | |
| mIoU: 82.25 | |
| mIoU(ms+flip): 83.48 | |
| Config: configs/segformer/segformer_mit-b5_8xb1-160k_cityscapes-1024x1024.py | |
| Metadata: | |
| Training Data: Cityscapes | |
| Batch Size: 8 | |
| Architecture: | |
| - MIT-B5 | |
| - Segformer | |
| Training Resources: 8x V100 GPUS | |
| Memory (GB): 18.0 | |
| 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 | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b5_8x1_1024x1024_160k_cityscapes/segformer_mit-b5_8x1_1024x1024_160k_cityscapes_20211206_072934.log.json | |
| Paper: | |
| Title: 'SegFormer: Simple and Efficient Design for Semantic Segmentation with | |
| Transformers' | |
| URL: https://arxiv.org/abs/2105.15203 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/mit.py#L246 | |
| Framework: PyTorch | |