architecture: YOLOv3 use_gpu: true max_iters: 70000 log_smooth_window: 20 save_dir: output snapshot_iter: 2000 metric: VOC map_type: 11point pretrain_weights: http://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV1_pretrained.tar weights: output/yolov3_mobilenet_v1_voc/model_final num_classes: 20 YOLOv3: backbone: MobileNet yolo_head: YOLOv3Head MobileNet: norm_type: sync_bn norm_decay: 0. conv_group_scale: 1 with_extra_blocks: false 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. ignore_thresh: 0.7 label_smooth: false nms: background_label: -1 keep_top_k: 100 nms_threshold: 0.45 nms_top_k: 1000 normalized: false score_threshold: 0.01 LearningRate: base_lr: 0.001 schedulers: - !PiecewiseDecay gamma: 0.1 milestones: - 55000 - 62000 - !LinearWarmup start_factor: 0. steps: 1000 OptimizerBuilder: optimizer: momentum: 0.9 type: Momentum regularizer: factor: 0.0005 type: L2 _READER_: 'yolov3_reader.yml' TrainReader: dataset: !VOCDataSet dataset_dir: dataset/voc anno_path: trainval.txt use_default_label: true with_background: false EvalReader: inputs_def: image_shape: [3, 608, 608] fields: ['image', 'im_size', 'im_id', 'gt_bbox', 'gt_class', 'is_difficult'] num_max_boxes: 50 dataset: !VOCDataSet dataset_dir: dataset/voc anno_path: test.txt use_default_label: true with_background: false TestReader: dataset: !ImageFolder use_default_label: true with_background: false