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yolov3_mobilenet_v1_fruit.yml
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architecture: YOLOv3
train_feed: YoloTrainFeed
eval_feed: YoloEvalFeed
test_feed: YoloTestFeed
use_gpu: true
max_iters: 20000
log_smooth_window: 20
save_dir: output
snapshot_iter: 200
metric: VOC
map_type: 11point
pretrain_weights: https://paddlemodels.bj.bcebos.com/object_detection/yolov3_mobilenet_v1.tar
weights: output/yolov3_mobilenet_v1_fruit/best_model
num_classes: 3
finetune_exclude_pretrained_params: ['yolo_output']
YOLOv3:
backbone: MobileNet
yolo_head: YOLOv3Head
MobileNet:
norm_type: sync_bn
norm_decay: 0.
conv_group_scale: 1
with_extra_blocks: false
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: 0.00001
schedulers:
- !PiecewiseDecay
gamma: 0.1
milestones:
- 15000
- 18000
- !LinearWarmup
start_factor: 0.
steps: 100
OptimizerBuilder:
optimizer:
momentum: 0.9
type: Momentum
regularizer:
factor: 0.0005
type: L2
YoloTrainFeed:
batch_size: 1
dataset:
dataset_dir: dataset/fruit
annotation: fruit-detection/train.txt
use_default_label: false
num_workers: 16
bufsize: 128
use_process: true
mixup_epoch: -1
sample_transforms:
- !DecodeImage
to_rgb: true
with_mixup: false
- !NormalizeBox {}
- !ExpandImage
max_ratio: 4.0
mean: [123.675, 116.28, 103.53]
prob: 0.5
- !RandomInterpImage
max_size: 0
target_size: 608
- !RandomFlipImage
is_mask_flip: false
is_normalized: true
prob: 0.5
- !NormalizeImage
is_channel_first: false
is_scale: true
mean:
- 0.485
- 0.456
- 0.406
std:
- 0.229
- 0.224
- 0.225
- !Permute
channel_first: true
to_bgr: false
batch_transforms:
- !RandomShape
sizes: [608]
with_background: false
YoloEvalFeed:
batch_size: 1
image_shape: [3, 608, 608]
dataset:
dataset_dir: dataset/fruit
annotation: fruit-detection/val.txt
use_default_label: false
YoloTestFeed:
batch_size: 1
image_shape: [3, 608, 608]
dataset:
dataset_dir: dataset/fruit
use_default_label: false