80 lines
1.2 KiB
YAML
80 lines
1.2 KiB
YAML
task: detection
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model: RTDETR
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criterion: RTDETRCriterion
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postprocessor: RTDETRPostProcessor
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use_focal_loss: True
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eval_spatial_size: [640, 640] # h w
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RTDETR:
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backbone: PResNet
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encoder: HybridEncoder
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decoder: RTDETRTransformer
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PResNet:
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depth: 50
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variant: d
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freeze_at: 0
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return_idx: [1, 2, 3]
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num_stages: 4
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freeze_norm: True
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pretrained: True
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HybridEncoder:
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in_channels: [512, 1024, 2048]
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feat_strides: [8, 16, 32]
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# intra
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hidden_dim: 256
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use_encoder_idx: [2]
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num_encoder_layers: 1
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nhead: 8
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dim_feedforward: 1024
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dropout: 0.
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enc_act: 'gelu'
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# cross
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expansion: 1.0
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depth_mult: 1
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act: 'silu'
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version: v1
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RTDETRTransformer:
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feat_channels: [256, 256, 256]
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feat_strides: [8, 16, 32]
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hidden_dim: 256
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num_levels: 3
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num_layers: 6
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num_queries: 300
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num_denoising: 100
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label_noise_ratio: 0.5
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box_noise_scale: 1.0 # 1.0 0.4
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eval_idx: -1
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RTDETRPostProcessor:
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num_top_queries: 300
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RTDETRCriterion:
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weight_dict: {loss_vfl: 1, loss_bbox: 5, loss_giou: 2,}
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losses: ['vfl', 'boxes', ]
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alpha: 0.75
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gamma: 2.0
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matcher:
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type: HungarianMatcher
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weight_dict: {cost_class: 2, cost_bbox: 5, cost_giou: 2}
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alpha: 0.25
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gamma: 2.0
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