architecture: YOLOv3 use_gpu: true max_iters: 500200 log_smooth_window: 20 save_dir: output snapshot_iter: 2000 metric: COCO pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNet34_pretrained.tar weights: output/yolov3_r34/model_final num_classes: 80 use_fine_grained_loss: false YOLOv3: backbone: ResNet yolo_head: YOLOv3Head ResNet: norm_type: sync_bn freeze_at: 0 freeze_norm: false norm_decay: 0. depth: 34 feature_maps: [3, 4, 5] YOLOv3Head: anchor_masks: [[6, 7, 8], [3, 4, 5], [0, 1, 2]] anchors: [[10, 13], [16, 30], [33, 23], [30, 61], [62, 45], [59, 119], [116, 90], [156, 198], [373, 326]] norm_decay: 0. yolo_loss: YOLOv3Loss nms: background_label: -1 keep_top_k: 100 nms_threshold: 0.45 nms_top_k: 1000 normalized: false score_threshold: 0.01 YOLOv3Loss: batch_size: 8 ignore_thresh: 0.7 label_smooth: true LearningRate: base_lr: 0.001 schedulers: - !PiecewiseDecay gamma: 0.1 milestones: - 400000 - 450000 - !LinearWarmup start_factor: 0. steps: 4000 OptimizerBuilder: optimizer: momentum: 0.9 type: Momentum regularizer: factor: 0.0005 type: L2 _READER_: 'yolov3_reader.yml'