134 lines
4.4 KiB
YAML
134 lines
4.4 KiB
YAML
Models:
|
|
- Name: convnext-tiny_upernet_8xb2-amp-160k_ade20k-512x512
|
|
In Collection: UPerNet
|
|
Metadata:
|
|
backbone: ConvNeXt-T
|
|
crop size: (512,512)
|
|
lr schd: 160000
|
|
inference time (ms/im):
|
|
- value: 50.25
|
|
hardware: V100
|
|
backend: PyTorch
|
|
batch size: 1
|
|
mode: AMP
|
|
resolution: (512,512)
|
|
Training Memory (GB): 4.23
|
|
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
|
|
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
|
|
- Name: convnext-small_upernet_8xb2-amp-160k_ade20k-512x512
|
|
In Collection: UPerNet
|
|
Metadata:
|
|
backbone: ConvNeXt-S
|
|
crop size: (512,512)
|
|
lr schd: 160000
|
|
inference time (ms/im):
|
|
- value: 65.88
|
|
hardware: V100
|
|
backend: PyTorch
|
|
batch size: 1
|
|
mode: AMP
|
|
resolution: (512,512)
|
|
Training Memory (GB): 5.16
|
|
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
|
|
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
|
|
- Name: convnext-base_upernet_8xb2-amp-160k_ade20k-512x512
|
|
In Collection: UPerNet
|
|
Metadata:
|
|
backbone: ConvNeXt-B
|
|
crop size: (512,512)
|
|
lr schd: 160000
|
|
inference time (ms/im):
|
|
- value: 69.4
|
|
hardware: V100
|
|
backend: PyTorch
|
|
batch size: 1
|
|
mode: AMP
|
|
resolution: (512,512)
|
|
Training Memory (GB): 6.33
|
|
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
|
|
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
|
|
- Name: convnext-base_upernet_8xb2-amp-160k_ade20k-640x640
|
|
In Collection: UPerNet
|
|
Metadata:
|
|
backbone: ConvNeXt-B
|
|
crop size: (640,640)
|
|
lr schd: 160000
|
|
inference time (ms/im):
|
|
- value: 91.91
|
|
hardware: V100
|
|
backend: PyTorch
|
|
batch size: 1
|
|
mode: AMP
|
|
resolution: (640,640)
|
|
Training Memory (GB): 8.53
|
|
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
|
|
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
|
|
- Name: convnext-large_upernet_8xb2-amp-160k_ade20k-640x640
|
|
In Collection: UPerNet
|
|
Metadata:
|
|
backbone: ConvNeXt-L
|
|
crop size: (640,640)
|
|
lr schd: 160000
|
|
inference time (ms/im):
|
|
- value: 130.04
|
|
hardware: V100
|
|
backend: PyTorch
|
|
batch size: 1
|
|
mode: AMP
|
|
resolution: (640,640)
|
|
Training Memory (GB): 12.08
|
|
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
|
|
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
|
|
- Name: convnext-xlarge_upernet_8xb2-amp-160k_ade20k-640x640
|
|
In Collection: UPerNet
|
|
Metadata:
|
|
backbone: ConvNeXt-XL
|
|
crop size: (640,640)
|
|
lr schd: 160000
|
|
inference time (ms/im):
|
|
- value: 157.98
|
|
hardware: V100
|
|
backend: PyTorch
|
|
batch size: 1
|
|
mode: AMP
|
|
resolution: (640,640)
|
|
Training Memory (GB): 26.16
|
|
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
|
|
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
|