Collections:
- Name: sem_fpn
  Metadata:
    Training Data:
    - Cityscapes
    - ADE20K
  Paper:
    URL: https://arxiv.org/abs/1901.02446
    Title: Panoptic Feature Pyramid Networks
  README: configs/sem_fpn/README.md
  Code:
    URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fpn_head.py#L12
    Version: v0.17.0
  Converted From:
    Code: https://github.com/facebookresearch/detectron2
Models:
- Name: fpn_r50_512x1024_80k_cityscapes
  In Collection: sem_fpn
  Metadata:
    backbone: R-50
    crop size: (512,1024)
    lr schd: 80000
    inference time (ms/im):
    - value: 73.86
      hardware: V100
      backend: PyTorch
      batch size: 1
      mode: FP32
      resolution: (512,1024)
    Training Memory (GB): 2.8
  Results:
  - Task: Semantic Segmentation
    Dataset: Cityscapes
    Metrics:
      mIoU: 74.52
      mIoU(ms+flip): 76.08
  Config: configs/sem_fpn/fpn_r50_512x1024_80k_cityscapes.py
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/sem_fpn/fpn_r50_512x1024_80k_cityscapes/fpn_r50_512x1024_80k_cityscapes_20200717_021437-94018a0d.pth
- Name: fpn_r101_512x1024_80k_cityscapes
  In Collection: sem_fpn
  Metadata:
    backbone: R-101
    crop size: (512,1024)
    lr schd: 80000
    inference time (ms/im):
    - value: 97.18
      hardware: V100
      backend: PyTorch
      batch size: 1
      mode: FP32
      resolution: (512,1024)
    Training Memory (GB): 3.9
  Results:
  - Task: Semantic Segmentation
    Dataset: Cityscapes
    Metrics:
      mIoU: 75.8
      mIoU(ms+flip): 77.4
  Config: configs/sem_fpn/fpn_r101_512x1024_80k_cityscapes.py
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/sem_fpn/fpn_r101_512x1024_80k_cityscapes/fpn_r101_512x1024_80k_cityscapes_20200717_012416-c5800d4c.pth
- Name: fpn_r50_512x512_160k_ade20k
  In Collection: sem_fpn
  Metadata:
    backbone: R-50
    crop size: (512,512)
    lr schd: 160000
    inference time (ms/im):
    - value: 17.93
      hardware: V100
      backend: PyTorch
      batch size: 1
      mode: FP32
      resolution: (512,512)
    Training Memory (GB): 4.9
  Results:
  - Task: Semantic Segmentation
    Dataset: ADE20K
    Metrics:
      mIoU: 37.49
      mIoU(ms+flip): 39.09
  Config: configs/sem_fpn/fpn_r50_512x512_160k_ade20k.py
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/sem_fpn/fpn_r50_512x512_160k_ade20k/fpn_r50_512x512_160k_ade20k_20200718_131734-5b5a6ab9.pth
- Name: fpn_r101_512x512_160k_ade20k
  In Collection: sem_fpn
  Metadata:
    backbone: R-101
    crop size: (512,512)
    lr schd: 160000
    inference time (ms/im):
    - value: 24.64
      hardware: V100
      backend: PyTorch
      batch size: 1
      mode: FP32
      resolution: (512,512)
    Training Memory (GB): 5.9
  Results:
  - Task: Semantic Segmentation
    Dataset: ADE20K
    Metrics:
      mIoU: 39.35
      mIoU(ms+flip): 40.72
  Config: configs/sem_fpn/fpn_r101_512x512_160k_ade20k.py
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/sem_fpn/fpn_r101_512x512_160k_ade20k/fpn_r101_512x512_160k_ade20k_20200718_131734-306b5004.pth