PP-LiteSeg-B.yaml 1.3 KB

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  1. batch_size: 4
  2. iters: 160000
  3. train_dataset:
  4. type: Dataset
  5. dataset_root: datasets/Cityscapes
  6. train_path: datasets/Cityscapes/train.txt
  7. num_classes: 19
  8. transforms:
  9. - type: ResizeStepScaling
  10. min_scale_factor: 0.125
  11. max_scale_factor: 1.5
  12. scale_step_size: 0.125
  13. - type: RandomPaddingCrop
  14. crop_size: [1024, 512]
  15. - type: RandomHorizontalFlip
  16. - type: RandomDistort
  17. brightness_range: 0.5
  18. contrast_range: 0.5
  19. saturation_range: 0.5
  20. - type: Normalize
  21. mode: train
  22. val_dataset:
  23. type: Dataset
  24. dataset_root: datasets/Cityscapes
  25. val_path: datasets/Cityscapes/val.txt
  26. num_classes: 19
  27. transforms:
  28. - type: Normalize
  29. mode: val
  30. model:
  31. type: PPLiteSeg
  32. backbone:
  33. type: STDC2
  34. pretrained: https://bj.bcebos.com/paddleseg/dygraph/PP_STDCNet2.tar.gz
  35. optimizer:
  36. type: SGD
  37. momentum: 0.9
  38. weight_decay: 5.0e-4
  39. lr_scheduler:
  40. type: PolynomialDecay
  41. learning_rate: 0.005
  42. end_lr: 0
  43. power: 0.9
  44. warmup_iters: 1000
  45. warmup_start_lr: 1.0e-5
  46. loss:
  47. types:
  48. - type: OhemCrossEntropyLoss
  49. min_kept: 130000 # batch_size * 1024 * 512 // 16
  50. - type: OhemCrossEntropyLoss
  51. min_kept: 130000
  52. - type: OhemCrossEntropyLoss
  53. min_kept: 130000
  54. coef: [1, 1, 1]
  55. test_config:
  56. aug_eval: True
  57. scales: 0.5