vehicle_yolov3_darknet.yml 1.7 KB

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  1. architecture: YOLOv3
  2. use_gpu: true
  3. max_iters: 120000
  4. log_iter: 20
  5. save_dir: output
  6. snapshot_iter: 2000
  7. metric: COCO
  8. pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/DarkNet53_pretrained.tar
  9. weights: https://paddlemodels.bj.bcebos.com/object_detection/vehicle_yolov3_darknet.tar
  10. num_classes: 6
  11. YOLOv3:
  12. backbone: DarkNet
  13. yolo_head: YOLOv3Head
  14. DarkNet:
  15. norm_type: sync_bn
  16. norm_decay: 0.
  17. depth: 53
  18. YOLOv3Head:
  19. anchor_masks: [[6, 7, 8], [3, 4, 5], [0, 1, 2]]
  20. anchors: [[8, 9], [10, 23], [19, 15],
  21. [23, 33], [40, 25], [54, 50],
  22. [101, 80], [139, 145], [253, 224]]
  23. norm_decay: 0.
  24. yolo_loss: YOLOv3Loss
  25. nms:
  26. background_label: -1
  27. keep_top_k: 100
  28. nms_threshold: 0.45
  29. nms_top_k: 400
  30. normalized: false
  31. score_threshold: 0.005
  32. YOLOv3Loss:
  33. batch_size: 8
  34. ignore_thresh: 0.7
  35. label_smooth: false
  36. LearningRate:
  37. base_lr: 0.001
  38. schedulers:
  39. - !PiecewiseDecay
  40. gamma: 0.1
  41. milestones:
  42. - 60000
  43. - 80000
  44. - !LinearWarmup
  45. start_factor: 0.
  46. steps: 4000
  47. OptimizerBuilder:
  48. optimizer:
  49. momentum: 0.9
  50. type: Momentum
  51. regularizer:
  52. factor: 0.0005
  53. type: L2
  54. _READER_: '../../configs/yolov3_reader.yml'
  55. TrainReader:
  56. batch_size: 8
  57. dataset:
  58. !COCODataSet
  59. dataset_dir: dataset/vehicle
  60. anno_path: annotations/instances_train2017.json
  61. image_dir: train2017
  62. with_background: false
  63. EvalReader:
  64. batch_size: 8
  65. dataset:
  66. !COCODataSet
  67. dataset_dir: dataset/vehicle
  68. anno_path: annotations/instances_val2017.json
  69. image_dir: val2017
  70. with_background: false
  71. TestReader:
  72. batch_size: 1
  73. dataset:
  74. !ImageFolder
  75. anno_path: contrib/VehicleDetection/vehicle.json
  76. with_background: false