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