gflv2_r50_fpn.yml 1.0 KB

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  1. architecture: GFL
  2. pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/ResNet50_cos_pretrained.pdparams
  3. GFL:
  4. backbone: ResNet
  5. neck: FPN
  6. head: GFLHead
  7. ResNet:
  8. depth: 50
  9. variant: b
  10. norm_type: bn
  11. freeze_at: 0
  12. return_idx: [1,2,3]
  13. num_stages: 4
  14. FPN:
  15. out_channel: 256
  16. spatial_scales: [0.125, 0.0625, 0.03125]
  17. extra_stage: 2
  18. has_extra_convs: true
  19. use_c5: false
  20. GFLHead:
  21. conv_feat:
  22. name: FCOSFeat
  23. feat_in: 256
  24. feat_out: 256
  25. num_convs: 4
  26. norm_type: "gn"
  27. use_dcn: false
  28. fpn_stride: [8, 16, 32, 64, 128]
  29. prior_prob: 0.01
  30. reg_max: 16
  31. dgqp_module:
  32. name: DGQP
  33. reg_topk: 4
  34. reg_channels: 64
  35. add_mean: True
  36. loss_class:
  37. name: QualityFocalLoss
  38. use_sigmoid: False
  39. beta: 2.0
  40. loss_weight: 1.0
  41. loss_dfl:
  42. name: DistributionFocalLoss
  43. loss_weight: 0.25
  44. loss_bbox:
  45. name: GIoULoss
  46. loss_weight: 2.0
  47. nms:
  48. name: MultiClassNMS
  49. nms_top_k: 1000
  50. keep_top_k: 100
  51. score_threshold: 0.025
  52. nms_threshold: 0.6