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