PulseFocusPlatform/static/configs/yolov4/yolov4_cspdarknet_coco.yml

175 lines
3.8 KiB
YAML

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/CSPDarkNet53_pretrained.pdparams
weights: output/yolov4_cspdarknet_coco/model_final
num_classes: 80
use_fine_grained_loss: 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: true
LearningRate:
base_lr: 0.0001
schedulers:
- !PiecewiseDecay
gamma: 0.1
milestones:
- 400000
- 450000
- !LinearWarmup
start_factor: 0.
steps: 4000
OptimizerBuilder:
clip_grad_by_norm: 10.
optimizer:
momentum: 0.949
type: Momentum
regularizer:
factor: 0.0005
type: L2
_READER_: '../yolov3_reader.yml'
TrainReader:
inputs_def:
fields: ['image', 'gt_bbox', 'gt_class', 'gt_score', 'im_id']
num_max_boxes: 50
dataset:
!COCODataSet
image_dir: train2017
anno_path: annotations/instances_train2017.json
dataset_dir: dataset/coco
with_background: false
sample_transforms:
- !DecodeImage
to_rgb: True
- !ColorDistort {}
- !RandomExpand
fill_value: [123.675, 116.28, 103.53]
- !RandomCrop {}
- !RandomFlipImage
is_normalized: false
- !NormalizeBox {}
- !PadBox
num_max_boxes: 50
- !BboxXYXY2XYWH {}
batch_transforms:
- !RandomShape
sizes: [320, 352, 384, 416, 448, 480, 512, 544, 576, 608]
random_inter: True
- !NormalizeImage
mean: [0.,0.,0.]
std: [1.,1.,1.]
is_scale: True
is_channel_first: false
- !Permute
to_bgr: false
channel_first: True
# Gt2YoloTarget is only used when use_fine_grained_loss set as true,
# this operator will be deleted automatically if use_fine_grained_loss
# is set as false
- !Gt2YoloTarget
anchor_masks: [[0, 1, 2], [3, 4, 5], [6, 7, 8]]
anchors: [[12, 16], [19, 36], [40, 28],
[36, 75], [76, 55], [72, 146],
[142, 110], [192, 243], [459, 401]]
downsample_ratios: [8, 16, 32]
batch_size: 8
shuffle: true
drop_last: true
worker_num: 8
bufsize: 16
use_process: true
drop_empty: false
EvalReader:
inputs_def:
fields: ['image', 'im_size', 'im_id']
num_max_boxes: 90
dataset:
!COCODataSet
image_dir: val2017
anno_path: annotations/instances_val2017.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
- !PadBox
num_max_boxes: 90
- !Permute
to_bgr: false
channel_first: True
batch_size: 4
drop_empty: false
worker_num: 8
bufsize: 16
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