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bdd4bc8a
编写于
8月 26, 2019
作者:
K
Kaipeng Deng
提交者:
GitHub
8月 26, 2019
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add config comment in yolov3_darknet.yml (#3126)
* add yolov3_darknet cinfig comment * fix commment * add log_iter
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# Architecture of detection, which is also the prefix of data feed module
architecture
:
YOLOv3
# Data feed module.
train_feed
:
YoloTrainFeed
eval_feed
:
YoloEvalFeed
test_feed
:
YoloTestFeed
# Use GPU or CPU, true by default.
use_gpu
:
true
# Maximum number of iteration.
# In YOLOv3 model, default iteration number is to train for 270 epoches.
max_iters
:
500200
# Smooth the log output in specified iterations, 20 by default.
log_smooth_window
:
20
# The number of iteration interval to display in training log.
log_iter
:
20
# The directory to save models.
save_dir
:
output
# Snapshot period. If training and test at same time, evaluate model at each snapshot_iter. 2000 by default.
snapshot_iter
:
2000
# Evalution method, COCO and VOC are available.
metric
:
COCO
# The path of oretrained wegiths. If url is provided, it will be downloaded and decompressed automatically.
pretrain_weights
:
https://paddle-imagenet-models-name.bj.bcebos.com/DarkNet53_pretrained.tar
# The path of final model for evaluation and test.
weights
:
output/yolov3_darknet/model_final
# Number of classes, 80 for COCO and 20 for VOC.
num_classes
:
80
# YOLOv3 architecture, see https://arxiv.org/abs/1804.02767
YOLOv3
:
backbone
:
DarkNet
yolo_head
:
YOLOv3Head
# Backbone module
DarkNet
:
# Batch normalization type in training, sync_bn for synchronized batch normalization
norm_type
:
sync_bn
# L2 weight decay factor of batch normalization layer
norm_decay
:
0.
# Darknet convolution layer number, only support 53 currently
depth
:
53
# YOLOv3 head module
# Generate bbox output in evaluation and calculate loss in training
# fluid.layers.yolov3_loss / fluid.layers.yolo_box
YOLOv3Head
:
# anchor mask of 3 yolo_loss/yolo_box layers, each yolo_loss/yolo_box layer has 3 anchors
anchor_masks
:
[[
6
,
7
,
8
],
[
3
,
4
,
5
],
[
0
,
1
,
2
]]
# 9 anchors for 3 yolo_loss/yolo_box layer, generated by perform kmeans on COCO gtboxes
anchors
:
[[
10
,
13
],
[
16
,
30
],
[
33
,
23
],
[
30
,
61
],
[
62
,
45
],
[
59
,
119
],
[
116
,
90
],
[
156
,
198
],
[
373
,
326
]]
# L2 weight decay factor of batch normalization layer
norm_decay
:
0.
# Ignore threshold for yolo_loss layer, 0.7 by default.
# Objectness loss will be ignored if a predcition bbox overlap a gtbox over ignore_thresh.
ignore_thresh
:
0.7
# Whether use label smooth in yolo_loss layer
# It is recommended to set as true when only num_classes is very big
label_smooth
:
true
# fluid.layers.multiclass_nms
# Non-max suppress for output prediction boxes, see multiclass_nms for following parameters.
# 1. Select detection bounding boxes with high scores larger than score_threshold.
# 2. Select detection bounding boxes with the largest nms_top_k scores.
# 3. Suppress detection bounding boxes which have high IoU overlap witch already selected boxes.
# 4. Keep the top keep_top_k detection bounding boxes as output.
nms
:
# Which label is regard as backgroud and will be ignored, -1 for no backgroud label.
background_label
:
-1
# Number of total bboxes to be kept per image after NMS step.
keep_top_k
:
100
# IoU threshold for NMS, bbox with IoU over nms_threshold will be suppressed.
nms_threshold
:
0.45
# Maximum number of detections to be kept according to the confidences after the filtering detections based on score_threshold.
nms_top_k
:
1000
# Whether detections are normalized.
normalized
:
false
# Threshold to filter out bounding boxes with low confidence score.
score_threshold
:
0.01
# Learning rate configuration
LearningRate
:
# Base learning rate for training, 1e-3 by default.
base_lr
:
0.001
# Learning rate schedulers, PiecewiseDecay and LinearWarmup by default
schedulers
:
# fluid.layers.piecewise_decay
# each milestone stage decay gamma
-
!PiecewiseDecay
gamma
:
0.1
milestones
:
-
400000
-
450000
# fluid.layers.linear_lr_warmup
# Start learning rate equals to base_lr * start_factor
-
!LinearWarmup
start_factor
:
0.
steps
:
4000
# Optimizer module
OptimizerBuilder
:
# fluid.optimizer
optimizer
:
momentum
:
0.9
type
:
Momentum
# fluid.regularizer
regularizer
:
factor
:
0.0005
type
:
L2
# Data feed module for training
YoloTrainFeed
:
# Batch size per device, 8 by default
batch_size
:
8
# Dataset module
dataset
:
# Dataset directory.
dataset_dir
:
dataset/coco
# Annotation file path.
annotation
:
annotations/instances_train2017.json
# Directory where image files are stored.
image_dir
:
train2017
# List of data fields needed.
fields
:
[
image
,
gt_box
,
gt_label
,
gt_score
]
# List of image dims
image_shape
:
[
3
,
608
,
608
]
# List of sample transformations to use.
sample_transforms
:
# read image data and decode to numpy.
-
!DecodeImage
to_rgb
:
true
# YOLOv3 use image mixup in training.
with_mixup
:
true
# Mixup two images in training, a trick to improve performance.
-
!MixupImage
alpha
:
1.5
# default: 1.5
beta
:
1.5
# default: 1.5
# Normalize gtbox to range [0, 1]
-
!NormalizeBox
{}
# Random color distort: brightness, contrast, hue, saturation.
-
!RandomDistort
brightness_lower
:
0.5
brightness_prob
:
0.5
brightness_upper
:
1.5
contrast_lower
:
0.5
contrast_prob
:
0.5
contrast_upper
:
1.5
count
:
4
hue_lower
:
-18
hue_prob
:
0.5
hue_upper
:
18
is_order
:
false
saturation_lower
:
0.5
saturation_prob
:
0.5
saturation_upper
:
1.5
# Random Expand the image and modify bounding box.
# Operators:
# 1. Scale the image weight and height.
# 2. Construct new images with new height and width.
# 3. Fill the new image with the mean.
# 4. Put original imge into new image.
# 5. Rescale the bounding box.
# 6. Determine if the new bbox is satisfied in the new image.
-
!ExpandImage
# max expand ratio, default 4.0.
max_ratio
:
4.0
mean
:
[
123.675
,
116.28
,
103.53
]
prob
:
0.5
# Random Crop the image and modify bounding box.
# Operators:
# 1. Scale the image weight and height.
# 2. Crop the image according to a radom sample.
# 3. Rescale the bounding box.
# 4. Determine if the new bbox is satisfied in the new image.
-
!CropImage
# Recrop image if there are no bbox in output cropped image.
avoid_no_bbox
:
true
batch_sampler
:
[[
1
,
1
,
1.0
,
1.0
,
1.0
,
1.0
,
0.0
,
0.0
],
[
1
,
50
,
0.3
,
1.0
,
0.5
,
2.0
,
0.1
,
1.0
],
[
1
,
50
,
0.3
,
1.0
,
0.5
,
2.0
,
0.3
,
1.0
],
[
1
,
50
,
0.3
,
1.0
,
0.5
,
2.0
,
0.5
,
1.0
],
[
1
,
50
,
0.3
,
1.0
,
0.5
,
2.0
,
0.7
,
1.0
],
[
1
,
50
,
0.3
,
1.0
,
0.5
,
2.0
,
0.9
,
1.0
],
[
1
,
50
,
0.3
,
1.0
,
0.5
,
2.0
,
0.0
,
1.0
]]
# Whether should all bbox satisfy IoU constrains.
satisfy_all
:
false
# Interpolate image to target_size with random interpolate method:
# cv2.INTER_NEAREST,
# cv2.INTER_LINEAR,
# cv2.INTER_AREA,
# cv2.INTER_CUBIC,
# cv2.INTER_LANCZOS4,
-
!RandomInterpImage
max_size
:
0
target_size
:
608
# Filp the image and bounding box.
# Operators:
# 1. Flip the image numpy.
# 2. Transform the bboxes' x coordinates. (Must judge whether the coordinates are normalized!)
# 3. Transform the segmentations' x coordinates. (Must judge whether the coordinates are normalized!)
-
!RandomFlipImage
is_mask_flip
:
false
is_normalized
:
true
prob
:
0.5
# Normalize the image.
# Operators:
# 1.(optional) Scale the image to [0,1]
# 2. Each pixel minus mean and is divided by std
-
!NormalizeImage
is_channel_first
:
false
is_scale
:
true
mean
:
[
0.485
,
0.456
,
0.406
]
std
:
[
0.229
,
0.224
,
0.225
]
# Change data layout to [C, H, W].
-
!Permute
channel_first
:
true
to_bgr
:
false
# List of batch transformations to use.
batch_transforms
:
# Random reshape images in each mini-batch to different shapes.
-
!RandomShape
sizes
:
[
320
,
352
,
384
,
416
,
448
,
480
,
512
,
544
,
576
,
608
]
# YOLOv3 read gtbox into zero padded tensor with max box number as 50.
num_max_boxes
:
50
# YOLOv3 read gtlabel without regarding backgroud as label 0.
with_background
:
false
# Number of samples, -1 represents all samples. -1 by default.
samples
:
-1
# Whether samples should be shuffled, true by default.
shuffle
:
true
# Whether drop last images which less than a batch.
drop_last
:
true
# Whether use multi-process reader in training.
use_process
:
true
# Use multi-process reader number.
num_workers
:
8
# Buffer size for reader.
bufsize
:
128
# Mixup image epoch number.
mixup_epoch
:
250
# Data feed module for evaluation
YoloEvalFeed
:
batch_size
:
8
dataset
:
dataset_dir
:
dataset/coco
annotation
:
annotations/instances_val2017.json
image_dir
:
val2017
batch_transforms
:
[]
fields
:
[
image
,
im_size
,
im_id
,
gt_box
,
gt_label
,
is_difficult
]
image_shape
:
[
3
,
608
,
608
]
sample_transforms
:
-
!DecodeImage
to_rgb
:
true
with_mixup
:
false
# Rescale image to the specified target size, and capped at max_size if max_size != 0.
# If target_size is list, selected a scale randomly as the specified target size.
-
!ResizeImage
interp
:
2
# 2 for cv2.INTER_CUBIC
max_size
:
0
target_size
:
608
use_cv2
:
true
-
!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
num_max_boxes
:
50
samples
:
-1
shuffle
:
false
drop_last
:
false
# Use multi-thread reader in evaluation mode.
use_process
:
false
# Thread number for multi-thread reader.
num_workers
:
8
with_background
:
false
# Data feed module for test
YoloTestFeed
:
batch_size
:
1
dataset
:
annotation
:
dataset/coco/annotations/instances_val2017.json
batch_transforms
:
[]
fields
:
[
image
,
im_size
,
im_id
]
sample_transforms
:
-
!DecodeImage
to_rgb
:
true
with_mixup
:
false
-
!ResizeImage
interp
:
2
max_size
:
0
target_size
:
608
use_cv2
:
true
-
!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
num_max_boxes
:
50
samples
:
-1
shuffle
:
false
drop_last
:
false
# Use multi-thread reader in test mode.
use_process
:
false
num_workers
:
8
with_background
:
false
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