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* segmenter: add model * update * readme: update * config: update * segmenter: update readme * segmenter: update * segmenter: update * segmenter: update * configs: set checkpoint path to pretrain folder * segmenter: modify vit-s/lin, remove data config * rreadme: update * configs: transfer from _base_ to segmenter * configs: add 8x1 suffix * configs: remove redundant lines * configs: cleanup * first attempt * swipe CI error * Update mmseg/models/decode_heads/__init__.py Co-authored-by: Junjun2016 <hejunjun@sjtu.edu.cn> * segmenter_linear: use fcn backbone * segmenter_mask: update * models: add segmenter vit * decoders: yapf+remove unused imports * apply precommit * segmenter/linear_head: fix * segmenter/linear_header: fix * segmenter: fix mask transformer * fix error * segmenter/mask_head: use trunc_normal init * refactor segmenter head * Fetch upstream (#1) * [Feature] Change options to cfg-option (#1129) * [Feature] Change option to cfg-option * add expire date and fix the docs * modify docstring * [Fix] Add <!-- [ABSTRACT] --> in metafile #1127 * [Fix] Fix correct num_classes of HRNet in LoveDA dataset #1136 * Bump to v0.20.1 (#1138) * bump version 0.20.1 * bump version 0.20.1 * [Fix] revise --option to --options #1140 Co-authored-by: Rockey <41846794+RockeyCoss@users.noreply.github.com> Co-authored-by: MengzhangLI <mcmong@pku.edu.cn> * decode_head: switch from linear to fcn * fix init list formatting * configs: remove variants, keep only vit-s on ade * align inference metric of vit-s-mask * configs: add vit t/b/l * Update mmseg/models/decode_heads/segmenter_mask_head.py Co-authored-by: Miao Zheng <76149310+MeowZheng@users.noreply.github.com> * Update mmseg/models/decode_heads/segmenter_mask_head.py Co-authored-by: Miao Zheng <76149310+MeowZheng@users.noreply.github.com> * Update mmseg/models/decode_heads/segmenter_mask_head.py Co-authored-by: Miao Zheng <76149310+MeowZheng@users.noreply.github.com> * Update mmseg/models/decode_heads/segmenter_mask_head.py Co-authored-by: Miao Zheng <76149310+MeowZheng@users.noreply.github.com> * Update mmseg/models/decode_heads/segmenter_mask_head.py Co-authored-by: Miao Zheng <76149310+MeowZheng@users.noreply.github.com> * model_converters: use torch instead of einops * setup: remove einops * segmenter_mask: fix missing imports * add necessary imported init funtion * segmenter/seg-l: set resolution to 640 * segmenter/seg-l: fix test size * fix vitjax2mmseg * add README and unittest * fix unittest * add docstring * refactor config and add pretrained link * fix typo * add paper name in readme * change segmenter config names * fix typo in readme * fix typos in readme * fix segmenter typo * fix segmenter typo * delete redundant comma in config files * delete redundant comma in config files * fix convert script * update lateset master version Co-authored-by: MengzhangLI <mcmong@pku.edu.cn> Co-authored-by: Junjun2016 <hejunjun@sjtu.edu.cn> Co-authored-by: Rockey <41846794+RockeyCoss@users.noreply.github.com> Co-authored-by: Miao Zheng <76149310+MeowZheng@users.noreply.github.com>
126 lines
4.0 KiB
YAML
126 lines
4.0 KiB
YAML
Collections:
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- Name: segmenter
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Metadata:
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Training Data:
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- ADE20K
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Paper:
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URL: https://arxiv.org/abs/2105.05633
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Title: 'Segmenter: Transformer for Semantic Segmentation'
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README: configs/segmenter/README.md
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Code:
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URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.21.0/mmseg/models/decode_heads/segmenter_mask_head.py#L15
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Version: v0.21.0
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Converted From:
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Code: https://github.com/rstrudel/segmenter
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Models:
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- Name: segmenter_vit-t_mask_8x1_512x512_160k_ade20k
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In Collection: segmenter
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Metadata:
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backbone: ViT-T_16
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crop size: (512,512)
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lr schd: 160000
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inference time (ms/im):
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- value: 35.74
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hardware: V100
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backend: PyTorch
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batch size: 1
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mode: FP32
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resolution: (512,512)
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Training Memory (GB): 1.21
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Results:
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- Task: Semantic Segmentation
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Dataset: ADE20K
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Metrics:
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mIoU: 39.99
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mIoU(ms+flip): 40.83
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Config: configs/segmenter/segmenter_vit-t_mask_8x1_512x512_160k_ade20k.py
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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
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- Name: segmenter_vit-s_linear_8x1_512x512_160k_ade20k
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In Collection: segmenter
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Metadata:
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backbone: ViT-S_16
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crop size: (512,512)
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lr schd: 160000
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inference time (ms/im):
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- value: 35.63
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hardware: V100
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backend: PyTorch
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batch size: 1
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mode: FP32
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resolution: (512,512)
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Training Memory (GB): 1.78
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Results:
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- Task: Semantic Segmentation
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Dataset: ADE20K
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Metrics:
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mIoU: 45.75
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mIoU(ms+flip): 46.82
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Config: configs/segmenter/segmenter_vit-s_linear_8x1_512x512_160k_ade20k.py
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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
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- Name: segmenter_vit-s_mask_8x1_512x512_160k_ade20k
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In Collection: segmenter
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Metadata:
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backbone: ViT-S_16
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crop size: (512,512)
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lr schd: 160000
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inference time (ms/im):
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- value: 40.32
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hardware: V100
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backend: PyTorch
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batch size: 1
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mode: FP32
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resolution: (512,512)
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Training Memory (GB): 2.03
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Results:
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- Task: Semantic Segmentation
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Dataset: ADE20K
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Metrics:
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mIoU: 46.19
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mIoU(ms+flip): 47.85
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Config: configs/segmenter/segmenter_vit-s_mask_8x1_512x512_160k_ade20k.py
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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
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- Name: segmenter_vit-b_mask_8x1_512x512_160k_ade20k
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In Collection: segmenter
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Metadata:
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backbone: ViT-B_16
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crop size: (512,512)
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lr schd: 160000
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inference time (ms/im):
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- value: 75.76
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hardware: V100
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backend: PyTorch
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batch size: 1
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mode: FP32
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resolution: (512,512)
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Training Memory (GB): 4.2
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Results:
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- Task: Semantic Segmentation
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Dataset: ADE20K
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Metrics:
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mIoU: 49.6
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mIoU(ms+flip): 51.07
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Config: configs/segmenter/segmenter_vit-b_mask_8x1_512x512_160k_ade20k.py
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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
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- Name: segmenter_vit-l_mask_8x1_512x512_160k_ade20k
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In Collection: segmenter
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Metadata:
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backbone: ViT-L_16
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crop size: (640,640)
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lr schd: 160000
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inference time (ms/im):
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- value: 381.68
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hardware: V100
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backend: PyTorch
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batch size: 1
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mode: FP32
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resolution: (640,640)
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Training Memory (GB): 16.56
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Results:
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- Task: Semantic Segmentation
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Dataset: ADE20K
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Metrics:
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mIoU: 52.16
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mIoU(ms+flip): 53.65
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Config: configs/segmenter/segmenter_vit-l_mask_8x1_512x512_160k_ade20k.py
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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
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