yolov3_r34_voc.yml 1.7 KB

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  1. architecture: YOLOv3
  2. use_gpu: true
  3. max_iters: 70000
  4. log_iter: 20
  5. save_dir: output
  6. snapshot_iter: 2000
  7. metric: VOC
  8. map_type: 11point
  9. pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNet34_pretrained.tar
  10. weights: output/yolov3_r34_voc/model_final
  11. num_classes: 20
  12. use_fine_grained_loss: false
  13. YOLOv3:
  14. backbone: ResNet
  15. yolo_head: YOLOv3Head
  16. ResNet:
  17. norm_type: sync_bn
  18. freeze_at: 0
  19. freeze_norm: false
  20. norm_decay: 0.
  21. depth: 34
  22. feature_maps: [3, 4, 5]
  23. YOLOv3Head:
  24. anchor_masks: [[6, 7, 8], [3, 4, 5], [0, 1, 2]]
  25. anchors: [[10, 13], [16, 30], [33, 23],
  26. [30, 61], [62, 45], [59, 119],
  27. [116, 90], [156, 198], [373, 326]]
  28. norm_decay: 0.
  29. yolo_loss: YOLOv3Loss
  30. nms:
  31. background_label: -1
  32. keep_top_k: 100
  33. nms_threshold: 0.45
  34. nms_top_k: 1000
  35. normalized: false
  36. score_threshold: 0.01
  37. YOLOv3Loss:
  38. ignore_thresh: 0.7
  39. label_smooth: false
  40. LearningRate:
  41. base_lr: 0.001
  42. schedulers:
  43. - !PiecewiseDecay
  44. gamma: 0.1
  45. milestones:
  46. - 55000
  47. - 62000
  48. - !LinearWarmup
  49. start_factor: 0.
  50. steps: 1000
  51. OptimizerBuilder:
  52. optimizer:
  53. momentum: 0.9
  54. type: Momentum
  55. regularizer:
  56. factor: 0.0005
  57. type: L2
  58. _READER_: 'yolov3_reader.yml'
  59. TrainReader:
  60. dataset:
  61. !VOCDataSet
  62. dataset_dir: dataset/voc
  63. anno_path: trainval.txt
  64. use_default_label: true
  65. with_background: false
  66. EvalReader:
  67. inputs_def:
  68. fields: ['image', 'im_size', 'im_id', 'gt_bbox', 'gt_class', 'is_difficult']
  69. num_max_boxes: 50
  70. dataset:
  71. !VOCDataSet
  72. dataset_dir: dataset/voc
  73. anno_path: test.txt
  74. use_default_label: true
  75. with_background: false
  76. TestReader:
  77. dataset:
  78. !ImageFolder
  79. use_default_label: true
  80. with_background: false