architecture: SSD train_feed: SSDTrainFeed eval_feed: SSDEvalFeed test_feed: SSDTestFeed pretrain_weights: https://paddlemodels.bj.bcebos.com/object_detection/ssd_mobilenet_v1_coco_pretrained.tar use_gpu: true max_iters: 28000 snapshot_iter: 2000 log_smooth_window: 1 metric: VOC map_type: 11point save_dir: output weights: output/ssd_mobilenet_v1_voc/model_final/ # 20(label_class) + 1(background) num_classes: 21 SSD: backbone: MobileNet multi_box_head: MultiBoxHead output_decoder: background_label: 0 keep_top_k: 200 nms_eta: 1.0 nms_threshold: 0.45 nms_top_k: 400 score_threshold: 0.01 MobileNet: norm_decay: 0. conv_group_scale: 1 conv_learning_rate: 0.1 extra_block_filters: [[256, 512], [128, 256], [128, 256], [64, 128]] with_extra_blocks: true MultiBoxHead: aspect_ratios: [[2.], [2., 3.], [2., 3.], [2., 3.], [2., 3.], [2., 3.]] base_size: 300 flip: true max_ratio: 90 max_sizes: [[], 150.0, 195.0, 240.0, 285.0, 300.0] min_ratio: 20 min_sizes: [60.0, 105.0, 150.0, 195.0, 240.0, 285.0] offset: 0.5 LearningRate: schedulers: - !PiecewiseDecay milestones: [10000, 15000, 20000, 25000] values: [0.001, 0.0005, 0.00025, 0.0001, 0.00001] OptimizerBuilder: optimizer: momentum: 0.0 type: RMSPropOptimizer regularizer: factor: 0.00005 type: L2 SSDTrainFeed: batch_size: 32 use_process: true dataset: dataset_dir: dataset/voc annotation: VOCdevkit/VOC_all/ImageSets/Main/train.txt image_dir: VOCdevkit/VOC_all/JPEGImages use_default_label: true SSDEvalFeed: batch_size: 64 use_process: true dataset: dataset_dir: dataset/voc annotation: VOCdevkit/VOC_all/ImageSets/Main/val.txt image_dir: VOCdevkit/VOC_all/JPEGImages use_default_label: true drop_last: false SSDTestFeed: batch_size: 1 dataset: use_default_label: true drop_last: false