Log In Sign Up
Models:
Mathew Swanson
Mathew-S/
Kaggle-UWMGIT
0
Downloads: 1
[f1e01c]: / configs / sem_fpn / sem_fpn.yml

Download this file

105 lines (104 with data), 3.1 kB 

  1
  2
  3
  4
  5
  6
  7
  8
  9
 10
 11
 12
 13
 14
 15
 16
 17
 18
 19
 20
 21
 22
 23
 24
 25
 26
 27
 28
 29
 30
 31
 32
 33
 34
 35
 36
 37
 38
 39
 40
 41
 42
 43
 44
 45
 46
 47
 48
 49
 50
 51
 52
 53
 54
 55
 56
 57
 58
 59
 60
 61
 62
 63
 64
 65
 66
 67
 68
 69
 70
 71
 72
 73
 74
 75
 76
 77
 78
 79
 80
 81
 82
 83
 84
 85
 86
 87
 88
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
101
102
103
104
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