architecture: YOLOv3 train_feed: YoloTrainFeed eval_feed: YoloEvalFeed test_feed: YoloTestFeed 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 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. 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: 0.001 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 YoloTrainFeed: batch_size: 8 dataset: dataset_dir: dataset/coco annotation: annotations/instances_train2017.json image_dir: train2017 num_workers: 8 bufsize: 128 use_process: true YoloEvalFeed: batch_size: 8 dataset: dataset_dir: dataset/coco annotation: annotations/instances_val2017.json image_dir: val2017 YoloTestFeed: batch_size: 1 dataset: annotation: dataset/coco/annotations/instances_val2017.json