yolov3_mobilenet_v1_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: http://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV1_pretrained.tar
  10. weights: output/yolov3_mobilenet_v1_voc/model_final
  11. num_classes: 20
  12. use_fine_grained_loss: false
  13. YOLOv3:
  14. backbone: MobileNet
  15. yolo_head: YOLOv3Head
  16. MobileNet:
  17. norm_type: sync_bn
  18. norm_decay: 0.
  19. conv_group_scale: 1
  20. with_extra_blocks: false
  21. YOLOv3Head:
  22. anchor_masks: [[6, 7, 8], [3, 4, 5], [0, 1, 2]]
  23. anchors: [[10, 13], [16, 30], [33, 23],
  24. [30, 61], [62, 45], [59, 119],
  25. [116, 90], [156, 198], [373, 326]]
  26. norm_decay: 0.
  27. yolo_loss: YOLOv3Loss
  28. nms:
  29. background_label: -1
  30. keep_top_k: 100
  31. nms_threshold: 0.45
  32. nms_top_k: 1000
  33. normalized: false
  34. score_threshold: 0.01
  35. YOLOv3Loss:
  36. ignore_thresh: 0.7
  37. label_smooth: false
  38. LearningRate:
  39. base_lr: 0.001
  40. schedulers:
  41. - !PiecewiseDecay
  42. gamma: 0.1
  43. milestones:
  44. - 55000
  45. - 62000
  46. - !LinearWarmup
  47. start_factor: 0.
  48. steps: 1000
  49. OptimizerBuilder:
  50. optimizer:
  51. momentum: 0.9
  52. type: Momentum
  53. regularizer:
  54. factor: 0.0005
  55. type: L2
  56. _READER_: 'yolov3_reader.yml'
  57. TrainReader:
  58. dataset:
  59. !VOCDataSet
  60. dataset_dir: dataset/voc
  61. anno_path: trainval.txt
  62. use_default_label: true
  63. with_background: false
  64. EvalReader:
  65. inputs_def:
  66. fields: ['image', 'im_size', 'im_id', 'gt_bbox', 'gt_class', 'is_difficult']
  67. num_max_boxes: 50
  68. dataset:
  69. !VOCDataSet
  70. dataset_dir: dataset/voc
  71. anno_path: test.txt
  72. use_default_label: true
  73. with_background: false
  74. TestReader:
  75. dataset:
  76. !ImageFolder
  77. use_default_label: true
  78. with_background: false