forked from PulseFocusPlatform/PulseFocusPlatform
175 lines
3.8 KiB
YAML
175 lines
3.8 KiB
YAML
architecture: YOLOv4
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use_gpu: true
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max_iters: 500200
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log_iter: 20
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save_dir: output
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snapshot_iter: 10000
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metric: COCO
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pretrain_weights: https://paddlemodels.bj.bcebos.com/object_detection/CSPDarkNet53_pretrained.pdparams
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weights: output/yolov4_cspdarknet_coco/model_final
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num_classes: 80
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use_fine_grained_loss: true
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YOLOv4:
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backbone: CSPDarkNet
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yolo_head: YOLOv4Head
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CSPDarkNet:
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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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YOLOv4Head:
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anchors: [[12, 16], [19, 36], [40, 28], [36, 75], [76, 55],
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[72, 146], [142, 110], [192, 243], [459, 401]]
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anchor_masks: [[0, 1, 2], [3, 4, 5], [6, 7, 8]]
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nms:
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background_label: -1
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keep_top_k: -1
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nms_threshold: 0.45
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nms_top_k: -1
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normalized: true
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score_threshold: 0.001
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downsample: [8,16,32]
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scale_x_y: [1.2, 1.1, 1.05]
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YOLOv3Loss:
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ignore_thresh: 0.7
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label_smooth: true
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downsample: [8,16,32]
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scale_x_y: [1.2, 1.1, 1.05]
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iou_loss: IouLoss
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match_score: true
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IouLoss:
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loss_weight: 0.07
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max_height: 608
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max_width: 608
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ciou_term: true
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loss_square: 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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- 400000
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- 450000
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- !LinearWarmup
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start_factor: 0.
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steps: 4000
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OptimizerBuilder:
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clip_grad_by_norm: 10.
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optimizer:
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momentum: 0.949
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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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_READER_: '../yolov3_reader.yml'
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TrainReader:
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inputs_def:
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fields: ['image', 'gt_bbox', 'gt_class', 'gt_score', 'im_id']
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num_max_boxes: 50
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dataset:
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!COCODataSet
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image_dir: train2017
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anno_path: annotations/instances_train2017.json
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dataset_dir: dataset/coco
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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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- !ColorDistort {}
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- !RandomExpand
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fill_value: [123.675, 116.28, 103.53]
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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.,0.,0.]
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std: [1.,1.,1.]
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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: [[0, 1, 2], [3, 4, 5], [6, 7, 8]]
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anchors: [[12, 16], [19, 36], [40, 28],
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[36, 75], [76, 55], [72, 146],
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[142, 110], [192, 243], [459, 401]]
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downsample_ratios: [8, 16, 32]
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batch_size: 8
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shuffle: true
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drop_last: true
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worker_num: 8
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bufsize: 16
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use_process: true
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drop_empty: false
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EvalReader:
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inputs_def:
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fields: ['image', 'im_size', 'im_id']
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num_max_boxes: 90
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dataset:
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!COCODataSet
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image_dir: val2017
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anno_path: annotations/instances_val2017.json
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dataset_dir: dataset/coco
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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: 1
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- !NormalizeImage
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mean: [0., 0., 0.]
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std: [1., 1., 1.]
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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: 90
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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: 8
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bufsize: 16
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TestReader:
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dataset:
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!ImageFolder
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use_default_label: true
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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: 1
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- !NormalizeImage
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mean: [0., 0., 0.]
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std: [1., 1., 1.]
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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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