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| Collections: | |
| - Name: EncNet | |
| License: Apache License 2.0 | |
| Metadata: | |
| Training Data: | |
| - Cityscapes | |
| - ADE20K | |
| Paper: | |
| Title: Context Encoding for Semantic Segmentation | |
| URL: https://arxiv.org/abs/1803.08904 | |
| README: configs/encnet/README.md | |
| Frameworks: | |
| - PyTorch | |
| Models: | |
| - Name: encnet_r50-d8_4xb2-40k_cityscapes-512x1024 | |
| In Collection: EncNet | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Cityscapes | |
| Metrics: | |
| mIoU: 75.67 | |
| mIoU(ms+flip): 77.08 | |
| Config: configs/encnet/encnet_r50-d8_4xb2-40k_cityscapes-512x1024.py | |
| Metadata: | |
| Training Data: Cityscapes | |
| Batch Size: 8 | |
| Architecture: | |
| - R-50-D8 | |
| - EncNet | |
| Training Resources: 4x V100 GPUS | |
| Memory (GB): 8.6 | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_512x1024_40k_cityscapes/encnet_r50-d8_512x1024_40k_cityscapes_20200621_220958-68638a47.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_512x1024_40k_cityscapes/encnet_r50-d8_512x1024_40k_cityscapes-20200621_220958.log.json | |
| Paper: | |
| Title: Context Encoding for Semantic Segmentation | |
| URL: https://arxiv.org/abs/1803.08904 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/enc_head.py#L63 | |
| Framework: PyTorch | |
| - Name: encnet_r101-d8_4xb2-40k_cityscapes-512x1024 | |
| In Collection: EncNet | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Cityscapes | |
| Metrics: | |
| mIoU: 75.81 | |
| mIoU(ms+flip): 77.21 | |
| Config: configs/encnet/encnet_r101-d8_4xb2-40k_cityscapes-512x1024.py | |
| Metadata: | |
| Training Data: Cityscapes | |
| Batch Size: 8 | |
| Architecture: | |
| - R-101-D8 | |
| - EncNet | |
| Training Resources: 4x V100 GPUS | |
| Memory (GB): 12.1 | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_512x1024_40k_cityscapes/encnet_r101-d8_512x1024_40k_cityscapes_20200621_220933-35e0a3e8.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_512x1024_40k_cityscapes/encnet_r101-d8_512x1024_40k_cityscapes-20200621_220933.log.json | |
| Paper: | |
| Title: Context Encoding for Semantic Segmentation | |
| URL: https://arxiv.org/abs/1803.08904 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/enc_head.py#L63 | |
| Framework: PyTorch | |
| - Name: encnet_r50-d8_4xb2-40k_cityscapes-769x769 | |
| In Collection: EncNet | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Cityscapes | |
| Metrics: | |
| mIoU: 76.24 | |
| mIoU(ms+flip): 77.85 | |
| Config: configs/encnet/encnet_r50-d8_4xb2-40k_cityscapes-769x769.py | |
| Metadata: | |
| Training Data: Cityscapes | |
| Batch Size: 8 | |
| Architecture: | |
| - R-50-D8 | |
| - EncNet | |
| Training Resources: 4x V100 GPUS | |
| Memory (GB): 9.8 | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_769x769_40k_cityscapes/encnet_r50-d8_769x769_40k_cityscapes_20200621_220958-3bcd2884.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_769x769_40k_cityscapes/encnet_r50-d8_769x769_40k_cityscapes-20200621_220958.log.json | |
| Paper: | |
| Title: Context Encoding for Semantic Segmentation | |
| URL: https://arxiv.org/abs/1803.08904 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/enc_head.py#L63 | |
| Framework: PyTorch | |
| - Name: encnet_r101-d8_4xb2-40k_cityscapes-769x769 | |
| In Collection: EncNet | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Cityscapes | |
| Metrics: | |
| mIoU: 74.25 | |
| mIoU(ms+flip): 76.25 | |
| Config: configs/encnet/encnet_r101-d8_4xb2-40k_cityscapes-769x769.py | |
| Metadata: | |
| Training Data: Cityscapes | |
| Batch Size: 8 | |
| Architecture: | |
| - R-101-D8 | |
| - EncNet | |
| Training Resources: 4x V100 GPUS | |
| Memory (GB): 13.7 | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_769x769_40k_cityscapes/encnet_r101-d8_769x769_40k_cityscapes_20200621_220933-2fafed55.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_769x769_40k_cityscapes/encnet_r101-d8_769x769_40k_cityscapes-20200621_220933.log.json | |
| Paper: | |
| Title: Context Encoding for Semantic Segmentation | |
| URL: https://arxiv.org/abs/1803.08904 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/enc_head.py#L63 | |
| Framework: PyTorch | |
| - Name: encnet_r50-d8_4xb2-80k_cityscapes-512x1024 | |
| In Collection: EncNet | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Cityscapes | |
| Metrics: | |
| mIoU: 77.94 | |
| mIoU(ms+flip): 79.13 | |
| Config: configs/encnet/encnet_r50-d8_4xb2-80k_cityscapes-512x1024.py | |
| Metadata: | |
| Training Data: Cityscapes | |
| Batch Size: 8 | |
| Architecture: | |
| - R-50-D8 | |
| - EncNet | |
| Training Resources: 4x V100 GPUS | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_512x1024_80k_cityscapes/encnet_r50-d8_512x1024_80k_cityscapes_20200622_003554-fc5c5624.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_512x1024_80k_cityscapes/encnet_r50-d8_512x1024_80k_cityscapes-20200622_003554.log.json | |
| Paper: | |
| Title: Context Encoding for Semantic Segmentation | |
| URL: https://arxiv.org/abs/1803.08904 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/enc_head.py#L63 | |
| Framework: PyTorch | |
| - Name: encnet_r101-d8_4xb2-80k_cityscapes-512x1024 | |
| In Collection: EncNet | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Cityscapes | |
| Metrics: | |
| mIoU: 78.55 | |
| mIoU(ms+flip): 79.47 | |
| Config: configs/encnet/encnet_r101-d8_4xb2-80k_cityscapes-512x1024.py | |
| Metadata: | |
| Training Data: Cityscapes | |
| Batch Size: 8 | |
| Architecture: | |
| - R-101-D8 | |
| - EncNet | |
| Training Resources: 4x V100 GPUS | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_512x1024_80k_cityscapes/encnet_r101-d8_512x1024_80k_cityscapes_20200622_003555-1de64bec.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_512x1024_80k_cityscapes/encnet_r101-d8_512x1024_80k_cityscapes-20200622_003555.log.json | |
| Paper: | |
| Title: Context Encoding for Semantic Segmentation | |
| URL: https://arxiv.org/abs/1803.08904 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/enc_head.py#L63 | |
| Framework: PyTorch | |
| - Name: encnet_r50-d8_4xb2-80k_cityscapes-769x769 | |
| In Collection: EncNet | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Cityscapes | |
| Metrics: | |
| mIoU: 77.44 | |
| mIoU(ms+flip): 78.72 | |
| Config: configs/encnet/encnet_r50-d8_4xb2-80k_cityscapes-769x769.py | |
| Metadata: | |
| Training Data: Cityscapes | |
| Batch Size: 8 | |
| Architecture: | |
| - R-50-D8 | |
| - EncNet | |
| Training Resources: 4x V100 GPUS | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_769x769_80k_cityscapes/encnet_r50-d8_769x769_80k_cityscapes_20200622_003554-55096dcb.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_769x769_80k_cityscapes/encnet_r50-d8_769x769_80k_cityscapes-20200622_003554.log.json | |
| Paper: | |
| Title: Context Encoding for Semantic Segmentation | |
| URL: https://arxiv.org/abs/1803.08904 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/enc_head.py#L63 | |
| Framework: PyTorch | |
| - Name: encnet_r101-d8_4xb2-80k_cityscapes-769x769 | |
| In Collection: EncNet | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Cityscapes | |
| Metrics: | |
| mIoU: 76.1 | |
| mIoU(ms+flip): 76.97 | |
| Config: configs/encnet/encnet_r101-d8_4xb2-80k_cityscapes-769x769.py | |
| Metadata: | |
| Training Data: Cityscapes | |
| Batch Size: 8 | |
| Architecture: | |
| - R-101-D8 | |
| - EncNet | |
| Training Resources: 4x V100 GPUS | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_769x769_80k_cityscapes/encnet_r101-d8_769x769_80k_cityscapes_20200622_003555-470ef79d.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_769x769_80k_cityscapes/encnet_r101-d8_769x769_80k_cityscapes-20200622_003555.log.json | |
| Paper: | |
| Title: Context Encoding for Semantic Segmentation | |
| URL: https://arxiv.org/abs/1803.08904 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/enc_head.py#L63 | |
| Framework: PyTorch | |
| - Name: encnet_r50-d8_4xb4-80k_ade20k-512x512 | |
| In Collection: EncNet | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: ADE20K | |
| Metrics: | |
| mIoU: 39.53 | |
| mIoU(ms+flip): 41.17 | |
| Config: configs/encnet/encnet_r50-d8_4xb4-80k_ade20k-512x512.py | |
| Metadata: | |
| Training Data: ADE20K | |
| Batch Size: 16 | |
| Architecture: | |
| - R-50-D8 | |
| - EncNet | |
| Training Resources: 4x V100 GPUS | |
| Memory (GB): 10.1 | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_512x512_80k_ade20k/encnet_r50-d8_512x512_80k_ade20k_20200622_042412-44b46b04.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_512x512_80k_ade20k/encnet_r50-d8_512x512_80k_ade20k-20200622_042412.log.json | |
| Paper: | |
| Title: Context Encoding for Semantic Segmentation | |
| URL: https://arxiv.org/abs/1803.08904 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/enc_head.py#L63 | |
| Framework: PyTorch | |
| - Name: encnet_r101-d8_4xb4-80k_ade20k-512x512 | |
| In Collection: EncNet | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: ADE20K | |
| Metrics: | |
| mIoU: 42.11 | |
| mIoU(ms+flip): 43.61 | |
| Config: configs/encnet/encnet_r101-d8_4xb4-80k_ade20k-512x512.py | |
| Metadata: | |
| Training Data: ADE20K | |
| Batch Size: 16 | |
| Architecture: | |
| - R-101-D8 | |
| - EncNet | |
| Training Resources: 4x V100 GPUS | |
| Memory (GB): 13.6 | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_512x512_80k_ade20k/encnet_r101-d8_512x512_80k_ade20k_20200622_101128-dd35e237.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_512x512_80k_ade20k/encnet_r101-d8_512x512_80k_ade20k-20200622_101128.log.json | |
| Paper: | |
| Title: Context Encoding for Semantic Segmentation | |
| URL: https://arxiv.org/abs/1803.08904 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/enc_head.py#L63 | |
| Framework: PyTorch | |
| - Name: encnet_r50-d8_4xb4-160k_ade20k-512x512 | |
| In Collection: EncNet | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: ADE20K | |
| Metrics: | |
| mIoU: 40.1 | |
| mIoU(ms+flip): 41.71 | |
| Config: configs/encnet/encnet_r50-d8_4xb4-160k_ade20k-512x512.py | |
| Metadata: | |
| Training Data: ADE20K | |
| Batch Size: 16 | |
| Architecture: | |
| - R-50-D8 | |
| - EncNet | |
| Training Resources: 4x V100 GPUS | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_512x512_160k_ade20k/encnet_r50-d8_512x512_160k_ade20k_20200622_101059-b2db95e0.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_512x512_160k_ade20k/encnet_r50-d8_512x512_160k_ade20k-20200622_101059.log.json | |
| Paper: | |
| Title: Context Encoding for Semantic Segmentation | |
| URL: https://arxiv.org/abs/1803.08904 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/enc_head.py#L63 | |
| Framework: PyTorch | |
| - Name: encnet_r101-d8_4xb4-160k_ade20k-512x512 | |
| In Collection: EncNet | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: ADE20K | |
| Metrics: | |
| mIoU: 42.61 | |
| mIoU(ms+flip): 44.01 | |
| Config: configs/encnet/encnet_r101-d8_4xb4-160k_ade20k-512x512.py | |
| Metadata: | |
| Training Data: ADE20K | |
| Batch Size: 16 | |
| Architecture: | |
| - R-101-D8 | |
| - EncNet | |
| Training Resources: 4x V100 GPUS | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_512x512_160k_ade20k/encnet_r101-d8_512x512_160k_ade20k_20200622_073348-7989641f.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_512x512_160k_ade20k/encnet_r101-d8_512x512_160k_ade20k-20200622_073348.log.json | |
| Paper: | |
| Title: Context Encoding for Semantic Segmentation | |
| URL: https://arxiv.org/abs/1803.08904 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/enc_head.py#L63 | |
| Framework: PyTorch | |