Spaces:
Runtime error
Runtime error
| Collections: | |
| - Name: SETR | |
| License: Apache License 2.0 | |
| Metadata: | |
| Training Data: | |
| - ADE20K | |
| - Cityscapes | |
| Paper: | |
| Title: Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective | |
| with Transformers | |
| URL: https://arxiv.org/abs/2012.15840 | |
| README: configs/setr/README.md | |
| Frameworks: | |
| - PyTorch | |
| Models: | |
| - Name: setr_vit-l_naive_8xb2-160k_ade20k-512x512 | |
| In Collection: SETR | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: ADE20K | |
| Metrics: | |
| mIoU: 48.28 | |
| mIoU(ms+flip): 49.56 | |
| Config: configs/setr/setr_vit-l_naive_8xb2-160k_ade20k-512x512.py | |
| Metadata: | |
| Training Data: ADE20K | |
| Batch Size: 16 | |
| Architecture: | |
| - ViT-L | |
| - SETR | |
| - Naive | |
| Training Resources: 8x V100 GPUS | |
| Memory (GB): 18.4 | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/setr/setr_naive_512x512_160k_b16_ade20k/setr_naive_512x512_160k_b16_ade20k_20210619_191258-061f24f5.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/setr/setr_naive_512x512_160k_b16_ade20k/setr_naive_512x512_160k_b16_ade20k_20210619_191258.log.json | |
| Paper: | |
| Title: Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective | |
| with Transformers | |
| URL: https://arxiv.org/abs/2012.15840 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/setr_up_head.py#L11 | |
| Framework: PyTorch | |
| - Name: setr_vit-l_pup_8xb2-160k_ade20k-512x512 | |
| In Collection: SETR | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: ADE20K | |
| Metrics: | |
| mIoU: 48.24 | |
| mIoU(ms+flip): 49.99 | |
| Config: configs/setr/setr_vit-l_pup_8xb2-160k_ade20k-512x512.py | |
| Metadata: | |
| Training Data: ADE20K | |
| Batch Size: 16 | |
| Architecture: | |
| - ViT-L | |
| - SETR | |
| - PUP | |
| Training Resources: 8x V100 GPUS | |
| Memory (GB): 19.54 | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/setr/setr_pup_512x512_160k_b16_ade20k/setr_pup_512x512_160k_b16_ade20k_20210619_191343-7e0ce826.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/setr/setr_pup_512x512_160k_b16_ade20k/setr_pup_512x512_160k_b16_ade20k_20210619_191343.log.json | |
| Paper: | |
| Title: Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective | |
| with Transformers | |
| URL: https://arxiv.org/abs/2012.15840 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/setr_up_head.py#L11 | |
| Framework: PyTorch | |
| - Name: setr_vit-l-mla_8xb1-160k_ade20k-512x512 | |
| In Collection: SETR | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: ADE20K | |
| Metrics: | |
| mIoU: 47.34 | |
| mIoU(ms+flip): 49.05 | |
| Config: configs/setr/setr_vit-l-mla_8xb1-160k_ade20k-512x512.py | |
| Metadata: | |
| Training Data: ADE20K | |
| Batch Size: 8 | |
| Architecture: | |
| - ViT-L | |
| - SETR | |
| - MLA | |
| Training Resources: 8x V100 GPUS | |
| Memory (GB): 10.96 | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/setr/setr_mla_512x512_160k_b8_ade20k/setr_mla_512x512_160k_b8_ade20k_20210619_191118-c6d21df0.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/setr/setr_mla_512x512_160k_b8_ade20k/setr_mla_512x512_160k_b8_ade20k_20210619_191118.log.json | |
| Paper: | |
| Title: Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective | |
| with Transformers | |
| URL: https://arxiv.org/abs/2012.15840 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/setr_up_head.py#L11 | |
| Framework: PyTorch | |
| - Name: setr_vit-l_mla_8xb2-160k_ade20k-512x512 | |
| In Collection: SETR | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: ADE20K | |
| Metrics: | |
| mIoU: 47.39 | |
| mIoU(ms+flip): 49.37 | |
| Config: configs/setr/setr_vit-l_mla_8xb2-160k_ade20k-512x512.py | |
| Metadata: | |
| Training Data: ADE20K | |
| Batch Size: 16 | |
| Architecture: | |
| - ViT-L | |
| - SETR | |
| - MLA | |
| Training Resources: 8x V100 GPUS | |
| Memory (GB): 17.3 | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/setr/setr_mla_512x512_160k_b16_ade20k/setr_mla_512x512_160k_b16_ade20k_20210619_191057-f9741de7.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/setr/setr_mla_512x512_160k_b16_ade20k/setr_mla_512x512_160k_b16_ade20k_20210619_191057.log.json | |
| Paper: | |
| Title: Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective | |
| with Transformers | |
| URL: https://arxiv.org/abs/2012.15840 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/setr_up_head.py#L11 | |
| Framework: PyTorch | |
| - Name: setr_vit-l_naive_8xb1-80k_cityscapes-768x768 | |
| In Collection: SETR | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Cityscapes | |
| Metrics: | |
| mIoU: 78.1 | |
| mIoU(ms+flip): 80.22 | |
| Config: configs/setr/setr_vit-l_naive_8xb1-80k_cityscapes-768x768.py | |
| Metadata: | |
| Training Data: Cityscapes | |
| Batch Size: 8 | |
| Architecture: | |
| - ViT-L | |
| - SETR | |
| - Naive | |
| Training Resources: 8x V100 GPUS | |
| Memory (GB): 24.06 | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/setr/setr_naive_vit-large_8x1_768x768_80k_cityscapes/setr_naive_vit-large_8x1_768x768_80k_cityscapes_20211123_000505-20728e80.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/setr/setr_naive_vit-large_8x1_768x768_80k_cityscapes/setr_naive_vit-large_8x1_768x768_80k_cityscapes_20211123_000505.log.json | |
| Paper: | |
| Title: Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective | |
| with Transformers | |
| URL: https://arxiv.org/abs/2012.15840 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/setr_up_head.py#L11 | |
| Framework: PyTorch | |
| - Name: setr_vit-l_pup_8xb1-80k_cityscapes-768x768 | |
| In Collection: SETR | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Cityscapes | |
| Metrics: | |
| mIoU: 79.21 | |
| mIoU(ms+flip): 81.02 | |
| Config: configs/setr/setr_vit-l_pup_8xb1-80k_cityscapes-768x768.py | |
| Metadata: | |
| Training Data: Cityscapes | |
| Batch Size: 8 | |
| Architecture: | |
| - ViT-L | |
| - SETR | |
| - PUP | |
| Training Resources: 8x V100 GPUS | |
| Memory (GB): 27.96 | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/setr/setr_pup_vit-large_8x1_768x768_80k_cityscapes/setr_pup_vit-large_8x1_768x768_80k_cityscapes_20211122_155115-f6f37b8f.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/setr/setr_pup_vit-large_8x1_768x768_80k_cityscapes/setr_pup_vit-large_8x1_768x768_80k_cityscapes_20211122_155115.log.json | |
| Paper: | |
| Title: Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective | |
| with Transformers | |
| URL: https://arxiv.org/abs/2012.15840 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/setr_up_head.py#L11 | |
| Framework: PyTorch | |
| - Name: setr_vit-l_mla_8xb1-80k_cityscapes-768x768 | |
| In Collection: SETR | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Cityscapes | |
| Metrics: | |
| mIoU: 77.0 | |
| mIoU(ms+flip): 79.59 | |
| Config: configs/setr/setr_vit-l_mla_8xb1-80k_cityscapes-768x768.py | |
| Metadata: | |
| Training Data: Cityscapes | |
| Batch Size: 8 | |
| Architecture: | |
| - ViT-L | |
| - SETR | |
| - MLA | |
| Training Resources: 8x V100 GPUS | |
| Memory (GB): 24.1 | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/setr/setr_mla_vit-large_8x1_768x768_80k_cityscapes/setr_mla_vit-large_8x1_768x768_80k_cityscapes_20211119_101003-7f8dccbe.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/setr/setr_mla_vit-large_8x1_768x768_80k_cityscapes/setr_mla_vit-large_8x1_768x768_80k_cityscapes_20211119_101003.log.json | |
| Paper: | |
| Title: Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective | |
| with Transformers | |
| URL: https://arxiv.org/abs/2012.15840 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/setr_up_head.py#L11 | |
| Framework: PyTorch | |