architecture: CascadeRCNN pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_cos_pretrained.tar weights: output/cascade_rcnn_r50_fpn_1x_coco/model_final load_static_weights: True roi_stages: 3 # Model Achitecture CascadeRCNN: # model anchor info flow anchor: Anchor proposal: Proposal # model feat info flow backbone: ResNet neck: FPN rpn_head: RPNHead bbox_head: BBoxHead # post process bbox_post_process: BBoxPostProcess ResNet: # index 0 stands for res2 depth: 50 norm_type: bn freeze_at: 0 return_idx: [0,1,2,3] num_stages: 4 FPN: in_channels: [256, 512, 1024, 2048] out_channel: 256 min_level: 0 max_level: 4 spatial_scale: [0.25, 0.125, 0.0625, 0.03125] RPNHead: rpn_feat: name: RPNFeat feat_in: 256 feat_out: 256 anchor_per_position: 3 rpn_channel: 256 Anchor: anchor_generator: name: AnchorGeneratorRPN aspect_ratios: [0.5, 1.0, 2.0] anchor_start_size: 32 stride: [4., 4.] anchor_target_generator: name: AnchorTargetGeneratorRPN batch_size_per_im: 256 fg_fraction: 0.5 negative_overlap: 0.3 positive_overlap: 0.7 straddle_thresh: 0.0 Proposal: proposal_generator: name: ProposalGenerator min_size: 0.0 nms_thresh: 0.7 train_pre_nms_top_n: 2000 train_post_nms_top_n: 2000 infer_pre_nms_top_n: 1000 infer_post_nms_top_n: 1000 proposal_target_generator: name: ProposalTargetGenerator batch_size_per_im: 512 bbox_reg_weights: [0.1, 0.1, 0.2, 0.2] bg_thresh_hi: [0.5, 0.6, 0.7] bg_thresh_lo: [0.0, 0.0, 0.0] fg_thresh: [0.5, 0.6, 0.7] fg_fraction: 0.25 is_cls_agnostic: true BBoxHead: bbox_feat: name: BBoxFeat roi_extractor: name: RoIAlign resolution: 7 sampling_ratio: 2 head_feat: name: TwoFCHead in_dim: 256 mlp_dim: 1024 in_feat: 1024 cls_agnostic: true BBoxPostProcess: decode: name: RCNNBox num_classes: 81 batch_size: 1 var_weight: 3. nms: name: MultiClassNMS keep_top_k: 100 score_threshold: 0.05 nms_threshold: 0.5