architecture: YOLOv3 train_feed: YoloTrainFeed eval_feed: YoloEvalFeed test_feed: YoloTestFeed use_gpu: true max_iters: 20000 log_smooth_window: 20 save_dir: output snapshot_iter: 200 metric: VOC map_type: 11point pretrain_weights: https://paddlemodels.bj.bcebos.com/object_detection/yolov3_mobilenet_v1.tar weights: output/yolov3_mobilenet_v1_fruit/best_model num_classes: 3 finetune_exclude_pretrained_params: ['yolo_output'] 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: true 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: 0.00001 schedulers: - !PiecewiseDecay gamma: 0.1 milestones: - 15000 - 18000 - !LinearWarmup start_factor: 0. steps: 100 OptimizerBuilder: optimizer: momentum: 0.9 type: Momentum regularizer: factor: 0.0005 type: L2 YoloTrainFeed: batch_size: 1 dataset: dataset_dir: dataset/fruit annotation: fruit-detection/train.txt use_default_label: false num_workers: 16 bufsize: 128 use_process: true mixup_epoch: -1 sample_transforms: - !DecodeImage to_rgb: true with_mixup: false - !NormalizeBox {} - !ExpandImage max_ratio: 4.0 mean: [123.675, 116.28, 103.53] prob: 0.5 - !RandomInterpImage max_size: 0 target_size: 608 - !RandomFlipImage is_mask_flip: false is_normalized: true prob: 0.5 - !NormalizeImage is_channel_first: false is_scale: true mean: - 0.485 - 0.456 - 0.406 std: - 0.229 - 0.224 - 0.225 - !Permute channel_first: true to_bgr: false batch_transforms: - !RandomShape sizes: [608] with_background: false YoloEvalFeed: batch_size: 1 image_shape: [3, 608, 608] dataset: dataset_dir: dataset/fruit annotation: fruit-detection/val.txt use_default_label: false YoloTestFeed: batch_size: 1 image_shape: [3, 608, 608] dataset: dataset_dir: dataset/fruit annotation: label_list.txt use_default_label: false