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陈赣
2026-06-03 12:42:47 +08:00
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task: detection
model: RTDETR
criterion: RTDETRCriterionv2
postprocessor: RTDETRPostProcessor
use_focal_loss: True
eval_spatial_size: [640, 640] # h w
RTDETR:
backbone: PResNet
encoder: HybridEncoder
decoder: RTDETRTransformerv2
PResNet:
depth: 50
variant: d
freeze_at: 0
return_idx: [1, 2, 3]
num_stages: 4
freeze_norm: True
pretrained: True
HybridEncoder:
in_channels: [512, 1024, 2048]
feat_strides: [8, 16, 32]
# intra
hidden_dim: 256
use_encoder_idx: [2]
num_encoder_layers: 1
nhead: 8
dim_feedforward: 1024
dropout: 0.
enc_act: 'gelu'
# cross
expansion: 1.0
depth_mult: 1
act: 'silu'
RTDETRTransformerv2:
feat_channels: [256, 256, 256]
feat_strides: [8, 16, 32]
hidden_dim: 256
num_levels: 3
num_layers: 6
num_queries: 300
num_denoising: 100
label_noise_ratio: 0.5
box_noise_scale: 1.0 # 1.0 0.4
eval_idx: -1
# NEW
num_points: [4, 4, 4] # [3,3,3] [2,2,2]
cross_attn_method: default # default, discrete
query_select_method: default # default, agnostic
RTDETRPostProcessor:
num_top_queries: 300
RTDETRCriterionv2:
weight_dict: {loss_vfl: 1, loss_bbox: 5, loss_giou: 2,}
losses: ['vfl', 'boxes', ]
alpha: 0.75
gamma: 2.0
matcher:
type: HungarianMatcher
weight_dict: {cost_class: 2, cost_bbox: 5, cost_giou: 2}
alpha: 0.25
gamma: 2.0