Collections: - Name: segmenter Metadata: Training Data: - ADE20K Paper: URL: https://arxiv.org/abs/2105.05633 Title: 'Segmenter: Transformer for Semantic Segmentation' README: configs/segmenter/README.md Code: URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.21.0/mmseg/models/decode_heads/segmenter_mask_head.py#L15 Version: v0.21.0 Converted From: Code: https://github.com/rstrudel/segmenter Models: - Name: segmenter_vit-t_mask_8x1_512x512_160k_ade20k In Collection: segmenter Metadata: backbone: ViT-T_16 crop size: (512,512) lr schd: 160000 inference time (ms/im): - value: 35.74 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 resolution: (512,512) Training Memory (GB): 1.21 Results: - Task: Semantic Segmentation Dataset: ADE20K Metrics: mIoU: 39.99 mIoU(ms+flip): 40.83 Config: configs/segmenter/segmenter_vit-t_mask_8x1_512x512_160k_ade20k.py Weights: https://download.openmmlab.com/mmsegmentation/v0.5/segmenter/segmenter_vit-t_mask_8x1_512x512_160k_ade20k/segmenter_vit-t_mask_8x1_512x512_160k_ade20k_20220105_151706-ffcf7509.pth - Name: segmenter_vit-s_linear_8x1_512x512_160k_ade20k In Collection: segmenter Metadata: backbone: ViT-S_16 crop size: (512,512) lr schd: 160000 inference time (ms/im): - value: 35.63 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 resolution: (512,512) Training Memory (GB): 1.78 Results: - Task: Semantic Segmentation Dataset: ADE20K Metrics: mIoU: 45.75 mIoU(ms+flip): 46.82 Config: configs/segmenter/segmenter_vit-s_linear_8x1_512x512_160k_ade20k.py Weights: https://download.openmmlab.com/mmsegmentation/v0.5/segmenter/segmenter_vit-s_linear_8x1_512x512_160k_ade20k/segmenter_vit-s_linear_8x1_512x512_160k_ade20k_20220105_151713-39658c46.pth - Name: segmenter_vit-s_mask_8x1_512x512_160k_ade20k In Collection: segmenter Metadata: backbone: ViT-S_16 crop size: (512,512) lr schd: 160000 inference time (ms/im): - value: 40.32 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 resolution: (512,512) Training Memory (GB): 2.03 Results: - Task: Semantic Segmentation Dataset: ADE20K Metrics: mIoU: 46.19 mIoU(ms+flip): 47.85 Config: configs/segmenter/segmenter_vit-s_mask_8x1_512x512_160k_ade20k.py Weights: https://download.openmmlab.com/mmsegmentation/v0.5/segmenter/segmenter_vit-s_mask_8x1_512x512_160k_ade20k/segmenter_vit-s_mask_8x1_512x512_160k_ade20k_20220105_151706-511bb103.pth - Name: segmenter_vit-b_mask_8x1_512x512_160k_ade20k In Collection: segmenter Metadata: backbone: ViT-B_16 crop size: (512,512) lr schd: 160000 inference time (ms/im): - value: 75.76 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 resolution: (512,512) Training Memory (GB): 4.2 Results: - Task: Semantic Segmentation Dataset: ADE20K Metrics: mIoU: 49.6 mIoU(ms+flip): 51.07 Config: configs/segmenter/segmenter_vit-b_mask_8x1_512x512_160k_ade20k.py Weights: https://download.openmmlab.com/mmsegmentation/v0.5/segmenter/segmenter_vit-b_mask_8x1_512x512_160k_ade20k/segmenter_vit-b_mask_8x1_512x512_160k_ade20k_20220105_151706-bc533b08.pth - Name: segmenter_vit-l_mask_8x1_512x512_160k_ade20k In Collection: segmenter Metadata: backbone: ViT-L_16 crop size: (640,640) lr schd: 160000 inference time (ms/im): - value: 381.68 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 resolution: (640,640) Training Memory (GB): 16.56 Results: - Task: Semantic Segmentation Dataset: ADE20K Metrics: mIoU: 52.16 mIoU(ms+flip): 53.65 Config: configs/segmenter/segmenter_vit-l_mask_8x1_512x512_160k_ade20k.py Weights: https://download.openmmlab.com/mmsegmentation/v0.5/segmenter/segmenter_vit-l_mask_8x1_512x512_160k_ade20k/segmenter_vit-l_mask_8x1_512x512_160k_ade20k_20220105_162750-7ef345be.pth