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yolov3_r34_voc.yml
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yolov3_r34_voc.yml
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architecture: YOLOv3
use_gpu: true
max_iters: 70000
log_iter: 20
save_dir: output
snapshot_iter: 2000
metric: VOC
map_type: 11point
pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNet34_pretrained.tar
weights: output/yolov3_r34_voc/model_final
num_classes: 20
use_fine_grained_loss: false
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.
yolo_loss: YOLOv3Loss
nms:
background_label: -1
keep_top_k: 100
nms_threshold: 0.45
nms_top_k: 1000
normalized: false
score_threshold: 0.01
YOLOv3Loss:
ignore_thresh: 0.7
label_smooth: false
LearningRate:
base_lr: 0.001
schedulers:
- !PiecewiseDecay
gamma: 0.1
milestones:
- 55000
- 62000
- !LinearWarmup
start_factor: 0.
steps: 1000
OptimizerBuilder:
optimizer:
momentum: 0.9
type: Momentum
regularizer:
factor: 0.0005
type: L2
_READER_: 'yolov3_reader.yml'
TrainReader:
dataset:
!VOCDataSet
dataset_dir: dataset/voc
anno_path: trainval.txt
use_default_label: true
with_background: false
EvalReader:
inputs_def:
fields: ['image', 'im_size', 'im_id', 'gt_bbox', 'gt_class', 'is_difficult']
num_max_boxes: 50
dataset:
!VOCDataSet
dataset_dir: dataset/voc
anno_path: test.txt
use_default_label: true
with_background: false
TestReader:
dataset:
!ImageFolder
use_default_label: true
with_background: false