yolov3_r50vd_dcn.yml 1.2 KB

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  1. architecture: YOLOv3
  2. use_gpu: true
  3. max_iters: 500000
  4. log_iter: 20
  5. save_dir: output
  6. snapshot_iter: 20000
  7. metric: COCO
  8. pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_vd_pretrained.tar
  9. weights: output/yolov3_r50vd_dcn/model_final
  10. num_classes: 80
  11. use_fine_grained_loss: false
  12. YOLOv3:
  13. backbone: ResNet
  14. yolo_head: YOLOv3Head
  15. ResNet:
  16. norm_type: sync_bn
  17. freeze_at: 0
  18. freeze_norm: false
  19. norm_decay: 0.
  20. depth: 50
  21. feature_maps: [3, 4, 5]
  22. variant: d
  23. dcn_v2_stages: [5]
  24. YOLOv3Head:
  25. anchor_masks: [[6, 7, 8], [3, 4, 5], [0, 1, 2]]
  26. anchors: [[10, 13], [16, 30], [33, 23],
  27. [30, 61], [62, 45], [59, 119],
  28. [116, 90], [156, 198], [373, 326]]
  29. norm_decay: 0.
  30. yolo_loss: YOLOv3Loss
  31. nms:
  32. background_label: -1
  33. keep_top_k: 100
  34. nms_threshold: 0.45
  35. nms_top_k: 1000
  36. normalized: false
  37. score_threshold: 0.01
  38. YOLOv3Loss:
  39. ignore_thresh: 0.7
  40. label_smooth: false
  41. LearningRate:
  42. base_lr: 0.001
  43. schedulers:
  44. - !PiecewiseDecay
  45. gamma: 0.1
  46. milestones:
  47. - 400000
  48. - 450000
  49. - !LinearWarmup
  50. start_factor: 0.
  51. steps: 4000
  52. OptimizerBuilder:
  53. optimizer:
  54. momentum: 0.9
  55. type: Momentum
  56. regularizer:
  57. factor: 0.0005
  58. type: L2
  59. _READER_: '../yolov3_reader.yml'