architecture: CascadeRCNN max_iters: 90000 snapshot_iter: 10000 use_gpu: true log_iter: 20 save_dir: output pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_cos_pretrained.tar weights: output/cascade_rcnn_r50_fpn_1x/model_final metric: COCO num_classes: 81 CascadeRCNN: backbone: ResNet fpn: FPN rpn_head: FPNRPNHead roi_extractor: FPNRoIAlign bbox_head: CascadeBBoxHead bbox_assigner: CascadeBBoxAssigner ResNet: norm_type: affine_channel depth: 50 feature_maps: [2, 3, 4, 5] freeze_at: 2 variant: b FPN: min_level: 2 max_level: 6 num_chan: 256 spatial_scale: [0.03125, 0.0625, 0.125, 0.25] FPNRPNHead: anchor_generator: anchor_sizes: [32, 64, 128, 256, 512] aspect_ratios: [0.5, 1.0, 2.0] stride: [16.0, 16.0] variance: [1.0, 1.0, 1.0, 1.0] anchor_start_size: 32 min_level: 2 max_level: 6 num_chan: 256 rpn_target_assign: rpn_batch_size_per_im: 256 rpn_fg_fraction: 0.5 rpn_positive_overlap: 0.7 rpn_negative_overlap: 0.3 rpn_straddle_thresh: 0.0 train_proposal: min_size: 0.0 nms_thresh: 0.7 pre_nms_top_n: 2000 post_nms_top_n: 2000 test_proposal: min_size: 0.0 nms_thresh: 0.7 pre_nms_top_n: 1000 post_nms_top_n: 1000 FPNRoIAlign: canconical_level: 4 canonical_size: 224 min_level: 2 max_level: 5 box_resolution: 7 sampling_ratio: 2 CascadeBBoxAssigner: batch_size_per_im: 512 bbox_reg_weights: [10, 20, 30] bg_thresh_lo: [0.0, 0.0, 0.0] bg_thresh_hi: [0.5, 0.6, 0.7] fg_thresh: [0.5, 0.6, 0.7] fg_fraction: 0.25 CascadeBBoxHead: head: CascadeTwoFCHead nms: keep_top_k: 100 nms_threshold: 0.5 score_threshold: 0.05 CascadeTwoFCHead: mlp_dim: 1024 LearningRate: base_lr: 0.02 schedulers: - !PiecewiseDecay gamma: 0.1 milestones: [60000, 80000] - !LinearWarmup start_factor: 0.3333333333333333 steps: 500 OptimizerBuilder: optimizer: momentum: 0.9 type: Momentum regularizer: factor: 0.0001 type: L2 _READER_: 'faster_fpn_reader.yml' TrainReader: batch_size: 2