forked from PulseFocusPlatform/PulseFocusPlatform
80 lines
1.5 KiB
YAML
80 lines
1.5 KiB
YAML
architecture: SOLOv2
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use_gpu: true
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max_iters: 135000
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snapshot_iter: 20000
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log_smooth_window: 20
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save_dir: output
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pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV3_large_x1_0_ssld_pretrained.tar
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metric: COCO
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weights: output/solov2/solov2_mobilenetv3_fpn_448_3x/model_final
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num_classes: 81
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use_ema: true
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ema_decay: 0.9998
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SOLOv2:
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backbone: MobileNetV3RCNN
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fpn: FPN
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bbox_head: SOLOv2Head
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mask_head: SOLOv2MaskHead
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MobileNetV3RCNN:
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norm_type: bn
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freeze_norm: true
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norm_decay: 0.0
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feature_maps: [2, 3, 4, 6]
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conv_decay: 0.00001
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lr_mult_list: [0.25, 0.25, 0.5, 0.5, 0.75]
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scale: 1.0
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model_name: large
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FPN:
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max_level: 6
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min_level: 2
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num_chan: 256
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spatial_scale: [0.03125, 0.0625, 0.125, 0.25]
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reverse_out: True
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SOLOv2Head:
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seg_feat_channels: 256
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stacked_convs: 2
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num_grids: [40, 36, 24, 16, 12]
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kernel_out_channels: 128
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solov2_loss: SOLOv2Loss
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mask_nms: MaskMatrixNMS
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drop_block: True
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SOLOv2MaskHead:
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in_channels: 128
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out_channels: 128
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start_level: 0
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end_level: 3
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SOLOv2Loss:
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ins_loss_weight: 3.0
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focal_loss_gamma: 2.0
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focal_loss_alpha: 0.25
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MaskMatrixNMS:
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pre_nms_top_n: 500
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post_nms_top_n: 100
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LearningRate:
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base_lr: 0.02
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schedulers:
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- !PiecewiseDecay
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gamma: 0.1
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milestones: [90000, 120000]
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- !LinearWarmup
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start_factor: 0.
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steps: 1000
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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: 0.0001
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type: L2
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_READER_: 'solov2_light_448_reader.yml'
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