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- architecture: YOLOv3
- use_gpu: true
- max_iters: 120000
- log_iter: 20
- save_dir: output
- snapshot_iter: 2000
- metric: COCO
- pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/DarkNet53_pretrained.tar
- weights: https://paddlemodels.bj.bcebos.com/object_detection/vehicle_yolov3_darknet.tar
- num_classes: 6
- YOLOv3:
- backbone: DarkNet
- yolo_head: YOLOv3Head
- DarkNet:
- norm_type: sync_bn
- norm_decay: 0.
- depth: 53
- YOLOv3Head:
- anchor_masks: [[6, 7, 8], [3, 4, 5], [0, 1, 2]]
- anchors: [[8, 9], [10, 23], [19, 15],
- [23, 33], [40, 25], [54, 50],
- [101, 80], [139, 145], [253, 224]]
- norm_decay: 0.
- yolo_loss: YOLOv3Loss
- nms:
- background_label: -1
- keep_top_k: 100
- nms_threshold: 0.45
- nms_top_k: 400
- normalized: false
- score_threshold: 0.005
- YOLOv3Loss:
- batch_size: 8
- ignore_thresh: 0.7
- label_smooth: false
- LearningRate:
- base_lr: 0.001
- schedulers:
- - !PiecewiseDecay
- gamma: 0.1
- milestones:
- - 60000
- - 80000
- - !LinearWarmup
- start_factor: 0.
- steps: 4000
- OptimizerBuilder:
- optimizer:
- momentum: 0.9
- type: Momentum
- regularizer:
- factor: 0.0005
- type: L2
- _READER_: '../../configs/yolov3_reader.yml'
- TrainReader:
- batch_size: 8
- dataset:
- !COCODataSet
- dataset_dir: dataset/vehicle
- anno_path: annotations/instances_train2017.json
- image_dir: train2017
- with_background: false
- EvalReader:
- batch_size: 8
- dataset:
- !COCODataSet
- dataset_dir: dataset/vehicle
- anno_path: annotations/instances_val2017.json
- image_dir: val2017
- with_background: false
- TestReader:
- batch_size: 1
- dataset:
- !ImageFolder
- anno_path: contrib/VehicleDetection/vehicle.json
- with_background: false
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