_BASE_ datasetsdog_detectionyml runtimeyml _base_optimizer_80eyml _base_ppyoloe_plus_crnyml _base_ppyoloe_plus_readerymllog_iter 100snapshot_epoch 5weights outputppyoloe_plus_crn_l_80e_cocomodel

lr_config: policy: CosineAnnealingDecay learning_rate: 0.01 warmup_steps: 500 min_lr_ratio: 0.001

total_epochs: 80 batch_size: 4 num_workers: 4

dataset: train: dataset_root: ../datasets/dog_detection/train/ ann_file: ../datasets/dog_detection/train/annotations/train.json img_prefix: ../datasets/dog_detection/train/images/ val: dataset_root: ../datasets/dog_detection/val/ ann_file: ../datasets/dog_detection/val/annotations/val.json img_prefix: ../datasets/dog_detection/val/images/ test: dataset_root: ../datasets/dog_detection/test/ ann_file: ../datasets/dog_detection/test/annotations/test.json img_prefix: ../datasets/dog_detection/test/images/

model: architecture: YOLOv3 num_classes: 1 backbone: type: YOLOv3DarkNet53 depth_mult: ${depth_mult} neck: type: YOLOv3FPN in_channels: [1024, 512, 256] out_channels: 256 head: type: YOLOv3Head num_classes: ${model.num_classes} in_channels: 256 anchors: [[10, 13], [16, 30], [33, 23], [30, 61], [62, 45], [59, 119], [116, 90], [156, 198], [373, 326]] anchor_masks: [[6, 7, 8], [3, 4, 5], [0, 1, 2]] featmap_strides: [8, 16, 32]

optimizer: type: SGD momentum: 0.9 weight_decay: 4.0e-5

post_process: type: YOLOv3PostProcess conf_thresh: 0.01 nms_thresh: 0.45 keep_top_k: 100

reader: type: DefaultReader use_prefetch: True buf_size: 1024

eval_metric:

• type: CocoDetectionMetric class_num: ${model.num_classes} jsonfile_prefix: dog_detection iou_threshold: 0.5 evaluate_direction: asc metric_order: [0]

test_cfg: nms_thresh: 0.45 score_thresh: 0.01 postprocess: type: YOLOv3PostProcess conf_thresh: 0.01 nms_thresh: 0.45 keep_top_k: 100

work_dir: output/ppyoloe_plus_crn_l_80e_coco load_from: null resume_from: nul