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mmsegmentation/configs/convnext/metafile.yaml

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Models:
- Name: convnext-tiny_upernet_8xb2-amp-160k_ade20k-512x512
In Collection: UPerNet
Results:
Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 46.11
mIoU(ms+flip): 46.62
Config: configs/convnext/convnext-tiny_upernet_8xb2-amp-160k_ade20k-512x512.py
Metadata:
Training Data: ADE20K
Batch Size: 16
Architecture:
- ConvNeXt-T
- UPerNet
Training Resources: 8x V100 GPUS
Memory (GB): 4.23
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
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_tiny_fp16_512x512_160k_ade20k/upernet_convnext_tiny_fp16_512x512_160k_ade20k_20220227_124553.log.json
Paper:
Title: A ConvNet for the 2020s
URL: https://arxiv.org/abs/2201.03545
Code: https://github.com/open-mmlab/mmclassification/blob/v0.20.1/mmcls/models/backbones/convnext.py#L133
Framework: PyTorch
- Name: convnext-small_upernet_8xb2-amp-160k_ade20k-512x512
In Collection: UPerNet
Results:
Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 48.56
mIoU(ms+flip): 49.02
Config: configs/convnext/convnext-small_upernet_8xb2-amp-160k_ade20k-512x512.py
Metadata:
Training Data: ADE20K
Batch Size: 16
Architecture:
- ConvNeXt-S
- UPerNet
Training Resources: 8x V100 GPUS
Memory (GB): 5.16
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
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_small_fp16_512x512_160k_ade20k/upernet_convnext_small_fp16_512x512_160k_ade20k_20220227_131208.log.json
Paper:
Title: A ConvNet for the 2020s
URL: https://arxiv.org/abs/2201.03545
Code: https://github.com/open-mmlab/mmclassification/blob/v0.20.1/mmcls/models/backbones/convnext.py#L133
Framework: PyTorch
- Name: convnext-base_upernet_8xb2-amp-160k_ade20k-512x512
In Collection: UPerNet
Results:
Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 48.71
mIoU(ms+flip): 49.54
Config: configs/convnext/convnext-base_upernet_8xb2-amp-160k_ade20k-512x512.py
Metadata:
Training Data: ADE20K
Batch Size: 16
Architecture:
- ConvNeXt-B
- UPerNet
Training Resources: 8x V100 GPUS
Memory (GB): 6.33
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
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_base_fp16_512x512_160k_ade20k/upernet_convnext_base_fp16_512x512_160k_ade20k_20220227_181227.log.json
Paper:
Title: A ConvNet for the 2020s
URL: https://arxiv.org/abs/2201.03545
Code: https://github.com/open-mmlab/mmclassification/blob/v0.20.1/mmcls/models/backbones/convnext.py#L133
Framework: PyTorch
- Name: convnext-base_upernet_8xb2-amp-160k_ade20k-640x640
In Collection: UPerNet
Results:
Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 52.13
mIoU(ms+flip): 52.66
Config: configs/convnext/convnext-base_upernet_8xb2-amp-160k_ade20k-640x640.py
Metadata:
Training Data: ADE20K
Batch Size: 16
Architecture:
- ConvNeXt-B
- UPerNet
Training Resources: 8x V100 GPUS
Memory (GB): 8.53
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
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_base_fp16_640x640_160k_ade20k/upernet_convnext_base_fp16_640x640_160k_ade20k_20220227_182859.log.json
Paper:
Title: A ConvNet for the 2020s
URL: https://arxiv.org/abs/2201.03545
Code: https://github.com/open-mmlab/mmclassification/blob/v0.20.1/mmcls/models/backbones/convnext.py#L133
Framework: PyTorch
- Name: convnext-large_upernet_8xb2-amp-160k_ade20k-640x640
In Collection: UPerNet
Results:
Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 53.16
mIoU(ms+flip): 53.38
Config: configs/convnext/convnext-large_upernet_8xb2-amp-160k_ade20k-640x640.py
Metadata:
Training Data: ADE20K
Batch Size: 16
Architecture:
- ConvNeXt-L
- UPerNet
Training Resources: 8x V100 GPUS
Memory (GB): 12.08
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
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_large_fp16_640x640_160k_ade20k/upernet_convnext_large_fp16_640x640_160k_ade20k_20220226_040532.log.json
Paper:
Title: A ConvNet for the 2020s
URL: https://arxiv.org/abs/2201.03545
Code: https://github.com/open-mmlab/mmclassification/blob/v0.20.1/mmcls/models/backbones/convnext.py#L133
Framework: PyTorch
- Name: convnext-xlarge_upernet_8xb2-amp-160k_ade20k-640x640
In Collection: UPerNet
Results:
Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 53.58
mIoU(ms+flip): 54.11
Config: configs/convnext/convnext-xlarge_upernet_8xb2-amp-160k_ade20k-640x640.py
Metadata:
Training Data: ADE20K
Batch Size: 16
Architecture:
- ConvNeXt-XL
- UPerNet
Training Resources: 8x V100 GPUS
Memory (GB): 26.16
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
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_xlarge_fp16_640x640_160k_ade20k/upernet_convnext_xlarge_fp16_640x640_160k_ade20k_20220226_080344.log.json
Paper:
Title: A ConvNet for the 2020s
URL: https://arxiv.org/abs/2201.03545
Code: https://github.com/open-mmlab/mmclassification/blob/v0.20.1/mmcls/models/backbones/convnext.py#L133
Framework: PyTorch
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