forked from PulseFocusPlatform/PulseFocusPlatform
173 lines
3.8 KiB
YAML
173 lines
3.8 KiB
YAML
architecture: YOLOv3
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use_gpu: false
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max_iters: 1200
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log_iter: 1
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save_dir: output
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snapshot_iter: 200
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metric: VOC
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map_type: integral
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pretrain_weights: https://paddlemodels.bj.bcebos.com/object_detection/yolov3_darknet.tar
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weights: output/yolov3_darknet_roadsign/model_final
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num_classes: 4
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finetune_exclude_pretrained_params: ['yolo_output']
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use_fine_grained_loss: false
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YOLOv3:
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backbone: DarkNet
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yolo_head: YOLOv3Head
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DarkNet:
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norm_type: sync_bn
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norm_decay: 0.
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depth: 53
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YOLOv3Head:
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anchor_masks: [[6, 7, 8], [3, 4, 5], [0, 1, 2]]
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anchors: [[10, 13], [16, 30], [33, 23],
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[30, 61], [62, 45], [59, 119],
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[116, 90], [156, 198], [373, 326]]
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norm_decay: 0.
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yolo_loss: YOLOv3Loss
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nms:
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background_label: -1
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keep_top_k: 100
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nms_threshold: 0.45
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nms_top_k: 1000
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normalized: false
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score_threshold: 0.01
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YOLOv3Loss:
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ignore_thresh: 0.7
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label_smooth: true
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LearningRate:
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base_lr: 0.0001
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schedulers:
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- !PiecewiseDecay
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gamma: 0.1
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milestones:
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- 800
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- 1100
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- !LinearWarmup
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start_factor: 0.
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steps: 100
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OptimizerBuilder:
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optimizer:
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momentum: 0.9
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type: Momentum
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regularizer:
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factor: 0.0005
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type: L2
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TrainReader:
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inputs_def:
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fields: ['image', 'gt_bbox', 'gt_class', 'gt_score']
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num_max_boxes: 50
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dataset:
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!VOCDataSet
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dataset_dir: dataset/roadsign_voc
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anno_path: train.txt
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with_background: false
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sample_transforms:
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- !DecodeImage
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to_rgb: True
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with_mixup: True
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- !MixupImage
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alpha: 1.5
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beta: 1.5
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- !ColorDistort {}
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- !RandomExpand
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fill_value: [123.675, 116.28, 103.53]
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ratio: 1.5
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- !RandomCrop {}
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- !RandomFlipImage
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is_normalized: false
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- !NormalizeBox {}
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- !PadBox
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num_max_boxes: 50
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- !BboxXYXY2XYWH {}
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batch_transforms:
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- !RandomShape
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sizes: [320, 352, 384, 416, 448, 480, 512, 544, 576, 608]
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random_inter: True
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- !NormalizeImage
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mean: [0.485, 0.456, 0.406]
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std: [0.229, 0.224, 0.225]
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is_scale: True
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is_channel_first: false
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- !Permute
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to_bgr: false
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channel_first: True
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# Gt2YoloTarget is only used when use_fine_grained_loss set as true,
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# this operator will be deleted automatically if use_fine_grained_loss
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# is set as false
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- !Gt2YoloTarget
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anchor_masks: [[6, 7, 8], [3, 4, 5], [0, 1, 2]]
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anchors: [[10, 13], [16, 30], [33, 23],
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[30, 61], [62, 45], [59, 119],
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[116, 90], [156, 198], [373, 326]]
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downsample_ratios: [32, 16, 8]
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batch_size: 4
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shuffle: true
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mixup_epoch: 250
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drop_last: true
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worker_num: 2
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bufsize: 2
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use_process: false #true
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EvalReader:
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inputs_def:
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fields: ['image', 'im_size', 'im_id', 'gt_bbox', 'gt_class', 'is_difficult']
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num_max_boxes: 50
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dataset:
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!VOCDataSet
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dataset_dir: dataset/roadsign_voc
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anno_path: valid.txt
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with_background: false
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sample_transforms:
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- !DecodeImage
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to_rgb: True
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- !ResizeImage
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target_size: 608
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interp: 2
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- !NormalizeImage
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mean: [0.485, 0.456, 0.406]
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std: [0.229, 0.224, 0.225]
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is_scale: True
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is_channel_first: false
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- !PadBox
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num_max_boxes: 50
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- !Permute
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to_bgr: false
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channel_first: True
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batch_size: 4
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drop_empty: false
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worker_num: 4
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bufsize: 2
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TestReader:
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inputs_def:
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image_shape: [3, 608, 608]
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fields: ['image', 'im_size', 'im_id']
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dataset:
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!ImageFolder
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anno_path: dataset/roadsign_voc/label_list.txt
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with_background: false
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sample_transforms:
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- !DecodeImage
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to_rgb: True
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- !ResizeImage
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target_size: 608
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interp: 2
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- !NormalizeImage
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mean: [0.485, 0.456, 0.406]
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std: [0.229, 0.224, 0.225]
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is_scale: True
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is_channel_first: false
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- !Permute
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to_bgr: false
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channel_first: True
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batch_size: 1
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