yolov3_r34.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: 10000
  7. metric: COCO
  8. pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNet34_pretrained.tar
  9. weights: output/yolov3_r34/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: 34
  21. feature_maps: [3, 4, 5]
  22. YOLOv3Head:
  23. anchor_masks: [[6, 7, 8], [3, 4, 5], [0, 1, 2]]
  24. anchors: [[10, 13], [16, 30], [33, 23],
  25. [30, 61], [62, 45], [59, 119],
  26. [116, 90], [156, 198], [373, 326]]
  27. norm_decay: 0.
  28. yolo_loss: YOLOv3Loss
  29. nms:
  30. background_label: -1
  31. keep_top_k: 100
  32. nms_threshold: 0.45
  33. nms_top_k: 1000
  34. normalized: false
  35. score_threshold: 0.01
  36. YOLOv3Loss:
  37. ignore_thresh: 0.7
  38. label_smooth: true
  39. LearningRate:
  40. base_lr: 0.001
  41. schedulers:
  42. - !PiecewiseDecay
  43. gamma: 0.1
  44. milestones:
  45. - 400000
  46. - 450000
  47. - !LinearWarmup
  48. start_factor: 0.
  49. steps: 4000
  50. OptimizerBuilder:
  51. optimizer:
  52. momentum: 0.9
  53. type: Momentum
  54. regularizer:
  55. factor: 0.0005
  56. type: L2
  57. _READER_: 'yolov3_reader.yml'