architecture: YOLOv4 use_gpu: true max_iters: 500200 log_iter: 20 save_dir: output snapshot_iter: 10000 metric: COCO pretrain_weights: https://paddlemodels.bj.bcebos.com/object_detection/yolov4_cspdarknet.pdparams weights: output/yolov4_cspdarknet/model_final num_classes: 80 use_fine_grained_loss: true save_prediction_only: True YOLOv4: backbone: CSPDarkNet yolo_head: YOLOv4Head CSPDarkNet: norm_type: sync_bn norm_decay: 0. depth: 53 YOLOv4Head: anchors: [[12, 16], [19, 36], [40, 28], [36, 75], [76, 55], [72, 146], [142, 110], [192, 243], [459, 401]] anchor_masks: [[0, 1, 2], [3, 4, 5], [6, 7, 8]] nms: background_label: -1 keep_top_k: -1 nms_threshold: 0.45 nms_top_k: -1 normalized: true score_threshold: 0.001 downsample: [8,16,32] scale_x_y: [1.2, 1.1, 1.05] YOLOv3Loss: ignore_thresh: 0.7 label_smooth: true downsample: [8,16,32] scale_x_y: [1.2, 1.1, 1.05] iou_loss: IouLoss match_score: true IouLoss: loss_weight: 0.07 max_height: 608 max_width: 608 ciou_term: true loss_square: false LearningRate: base_lr: 0.0001 schedulers: - !PiecewiseDecay gamma: 0.1 milestones: - 400000 - 450000 - !LinearWarmup start_factor: 0. steps: 1000 OptimizerBuilder: clip_grad_by_norm: 10. optimizer: momentum: 0.949 type: Momentum regularizer: factor: 0.0005 type: L2 _READER_: '../yolov3_reader.yml' EvalReader: inputs_def: fields: ['image', 'im_size', 'im_id'] num_max_boxes: 90 dataset: !COCODataSet image_dir: test2017 anno_path: annotations/image_info_test-dev2017.json dataset_dir: dataset/coco with_background: false sample_transforms: - !DecodeImage to_rgb: True - !ResizeImage target_size: 608 interp: 1 - !NormalizeImage mean: [0., 0., 0.] std: [1., 1., 1.] is_scale: True is_channel_first: false - !Permute to_bgr: false channel_first: True batch_size: 1 TestReader: dataset: !ImageFolder use_default_label: true with_background: false sample_transforms: - !DecodeImage to_rgb: True - !ResizeImage target_size: 608 interp: 1 - !NormalizeImage mean: [0., 0., 0.] std: [1., 1., 1.] is_scale: True is_channel_first: false - !Permute to_bgr: false channel_first: True