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- _BASE_: [
- '../datasets/coco_detection.yml',
- '../runtime.yml',
- '_base_/optimizer_1x.yml',
- '_base_/gfl_reader.yml',
- ]
- weights: output/gfl_r18vd_1x_coco/model_final
- find_unused_parameters: True
- architecture: GFL
- pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/ResNet18_vd_pretrained.pdparams
- GFL:
- backbone: ResNet
- neck: FPN
- head: LDGFLHead
- ResNet:
- depth: 18
- variant: d
- norm_type: bn
- freeze_at: 0
- return_idx: [1,2,3]
- num_stages: 4
- FPN:
- out_channel: 256
- spatial_scales: [0.125, 0.0625, 0.03125]
- extra_stage: 2
- has_extra_convs: true
- use_c5: false
- LDGFLHead: # new head
- conv_feat:
- name: FCOSFeat
- feat_in: 256
- feat_out: 256
- num_convs: 4
- norm_type: "gn"
- use_dcn: false
- fpn_stride: [8, 16, 32, 64, 128]
- prior_prob: 0.01
- reg_max: 16
- loss_class:
- name: QualityFocalLoss
- use_sigmoid: True
- beta: 2.0
- loss_weight: 1.0
- loss_dfl:
- name: DistributionFocalLoss
- loss_weight: 0.25
- loss_bbox:
- name: GIoULoss
- loss_weight: 2.0
- loss_ld:
- name: KnowledgeDistillationKLDivLoss
- loss_weight: 0.25
- T: 10
- loss_ld_vlr:
- name: KnowledgeDistillationKLDivLoss
- loss_weight: 0.25
- T: 10
- loss_kd:
- name: KnowledgeDistillationKLDivLoss
- loss_weight: 10
- T: 2
- nms:
- name: MultiClassNMS
- nms_top_k: 1000
- keep_top_k: 100
- score_threshold: 0.025
- nms_threshold: 0.6
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