mirror of
https://github.com/open-mmlab/mmsegmentation.git
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933e4d3cb6
* [Feature] Support MaskFormer(NeurIPS'2021) in MMSeg 1.x * add mmdet try except logic * refactor config files * add readme * fix config * update models & logs * add MMDET installation and fix info * fix comments * fix * fix config norm optimizer setting * update models & logs & unittest * add docstring of MaskFormerHead * wait for mmdet 3.0.0rc4 * replace seg_mask with seg_logits & add docstring for batch_input_shape * use mmdet3.0.0rc4 * fix readme and modify config comments * add mmdet installation in pr_stage_test.yml * update mmcv version in pr_stage_test.yml * add mmdet in build_cpu of pr_stage_test.yml * modify mmdet& mmcv installation in merge_stage_test.yml * fix typo * update test.yml * update test.yml
102 lines
3.3 KiB
YAML
102 lines
3.3 KiB
YAML
Collections:
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- Name: MaskFormer
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Metadata:
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Training Data:
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- Usage
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- ADE20K
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Paper:
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URL: https://arxiv.org/abs/2107.06278
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Title: 'MaskFormer: Per-Pixel Classification is Not All You Need for Semantic
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Segmentation'
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README: configs/maskformer/README.md
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Code:
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URL: https://github.com/open-mmlab/mmdetection/blob/dev-3.x/mmdet/models/dense_heads/maskformer_head.py#L21
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Version: dev-3.x
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Converted From:
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Code: https://github.com/facebookresearch/MaskFormer/
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Models:
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- Name: maskformer_r50-d32_8xb2-160k_ade20k-512x512
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In Collection: MaskFormer
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Metadata:
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backbone: R-50-D32
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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: 23.7
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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): 3.29
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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: 44.29
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Config: configs/maskformer/maskformer_r50-d32_8xb2-160k_ade20k-512x512.py
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/maskformer/maskformer_r50-d32_8xb2-160k_ade20k-512x512/maskformer_r50-d32_8xb2-160k_ade20k-512x512_20221030_182724-cbd39cc1.pth
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- Name: maskformer_r101-d32_8xb2-160k_ade20k-512x512
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In Collection: MaskFormer
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Metadata:
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backbone: R-101-D32
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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: 28.65
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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): 4.12
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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: 45.11
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Config: configs/maskformer/maskformer_r101-d32_8xb2-160k_ade20k-512x512.py
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/maskformer/maskformer_r101-d32_8xb2-160k_ade20k-512x512/maskformer_r101-d32_8xb2-160k_ade20k-512x512_20221031_223053-c8e0931d.pth
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- Name: maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512
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In Collection: MaskFormer
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Metadata:
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backbone: Swin-T
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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: 24.67
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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): 3.73
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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: 46.69
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Config: configs/maskformer/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512.py
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/maskformer/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512_20221114_232813-03550716.pth
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- Name: maskformer_swin-s_upernet_8xb2-160k_ade20k-512x512
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In Collection: MaskFormer
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Metadata:
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backbone: Swin-S
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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: 37.06
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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): 5.33
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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: 49.36
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Config: configs/maskformer/maskformer_swin-s_upernet_8xb2-160k_ade20k-512x512.py
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/maskformer/maskformer_swin-s_upernet_8xb2-160k_ade20k-512x512/maskformer_swin-s_upernet_8xb2-160k_ade20k-512x512_20221115_114710-5ab67e58.pth
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