mirror of
https://github.com/open-mmlab/mmsegmentation.git
synced 2025-06-03 22:03:48 +08:00
7ddd2fe2ec
* upload original backbone and configs * ConvNext Refactor * ConvNext Refactor * convnext customization refactor with mmseg style * convnext customization refactor with mmseg style * add ade20k_640x640.py * upload files for training * delete dist_optimizer_hook and remove layer_decay_optimizer_constructor * check max(out_indices) < num_stages * add unittest * fix lint error * use MMClassification backbone * fix bugs in base_1k * add mmcls in requirements/mminstall.txt * add mmcls in requirements/mminstall.txt * fix drop_path_rate and layer_scale_init_value * use logger.info instead of print * add mmcls in runtime.txt * fix f string && delete * add doctring in LearningRateDecayOptimizerConstructor and fix mmcls version in requirements * fix typo in LearningRateDecayOptimizerConstructor * use ConvNext models in unit test for LearningRateDecayOptimizerConstructor * add unit test * fix typo * fix typo * add layer_wise and fix redundant backbone.downsample_norm in it * fix unit test * give a ground truth lr_scale and weight_decay * upload models and readme * delete 'backbone.stem_norm' and 'backbone.downsample_norm' in get_num_layer() * fix unit test and use mmcls url * update md2yml.py and metafile * fix typo
134 lines
4.4 KiB
YAML
134 lines
4.4 KiB
YAML
Models:
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- Name: upernet_convnext_tiny_fp16_512x512_160k_ade20k
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In Collection: UperNet
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Metadata:
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backbone: ConvNeXt-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: 50.25
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hardware: V100
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backend: PyTorch
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batch size: 1
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mode: FP16
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resolution: (512,512)
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Training Memory (GB): 4.23
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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: 46.11
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mIoU(ms+flip): 46.62
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Config: configs/convnext/upernet_convnext_tiny_fp16_512x512_160k_ade20k.py
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_tiny_fp16_512x512_160k_ade20k/upernet_convnext_tiny_fp16_512x512_160k_ade20k_20220227_124553-cad485de.pth
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- Name: upernet_convnext_small_fp16_512x512_160k_ade20k
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In Collection: UperNet
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Metadata:
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backbone: ConvNeXt-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: 65.88
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hardware: V100
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backend: PyTorch
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batch size: 1
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mode: FP16
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resolution: (512,512)
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Training Memory (GB): 5.16
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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: 48.56
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mIoU(ms+flip): 49.02
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Config: configs/convnext/upernet_convnext_small_fp16_512x512_160k_ade20k.py
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_small_fp16_512x512_160k_ade20k/upernet_convnext_small_fp16_512x512_160k_ade20k_20220227_131208-1b1e394f.pth
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- Name: upernet_convnext_base_fp16_512x512_160k_ade20k
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In Collection: UperNet
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Metadata:
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backbone: ConvNeXt-B
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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: 69.4
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hardware: V100
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backend: PyTorch
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batch size: 1
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mode: FP16
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resolution: (512,512)
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Training Memory (GB): 6.33
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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: 48.71
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mIoU(ms+flip): 49.54
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Config: configs/convnext/upernet_convnext_base_fp16_512x512_160k_ade20k.py
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_base_fp16_512x512_160k_ade20k/upernet_convnext_base_fp16_512x512_160k_ade20k_20220227_181227-02a24fc6.pth
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- Name: upernet_convnext_base_fp16_640x640_160k_ade20k
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In Collection: UperNet
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Metadata:
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backbone: ConvNeXt-B
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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: 91.91
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hardware: V100
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backend: PyTorch
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batch size: 1
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mode: FP16
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resolution: (640,640)
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Training Memory (GB): 8.53
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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: 52.13
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mIoU(ms+flip): 52.66
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Config: configs/convnext/upernet_convnext_base_fp16_640x640_160k_ade20k.py
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_base_fp16_640x640_160k_ade20k/upernet_convnext_base_fp16_640x640_160k_ade20k_20220227_182859-9280e39b.pth
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- Name: upernet_convnext_large_fp16_640x640_160k_ade20k
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In Collection: UperNet
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Metadata:
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backbone: ConvNeXt-L
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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: 130.04
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hardware: V100
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backend: PyTorch
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batch size: 1
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mode: FP16
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resolution: (640,640)
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Training Memory (GB): 12.08
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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: 53.16
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mIoU(ms+flip): 53.38
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Config: configs/convnext/upernet_convnext_large_fp16_640x640_160k_ade20k.py
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_large_fp16_640x640_160k_ade20k/upernet_convnext_large_fp16_640x640_160k_ade20k_20220226_040532-e57aa54d.pth
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- Name: upernet_convnext_xlarge_fp16_640x640_160k_ade20k
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In Collection: UperNet
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Metadata:
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backbone: ConvNeXt-XL
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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: 157.98
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hardware: V100
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backend: PyTorch
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batch size: 1
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mode: FP16
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resolution: (640,640)
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Training Memory (GB): 26.16
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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: 53.58
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mIoU(ms+flip): 54.11
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Config: configs/convnext/upernet_convnext_xlarge_fp16_640x640_160k_ade20k.py
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_xlarge_fp16_640x640_160k_ade20k/upernet_convnext_xlarge_fp16_640x640_160k_ade20k_20220226_080344-95fc38c2.pth
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