From 79f0aae55516a9085342cc4d5b99521453a393d3 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Haian=20Huang=28=E6=B7=B1=E5=BA=A6=E7=9C=B8=29?=
<1286304229@qq.com>
Date: Fri, 3 Feb 2023 14:28:35 +0800
Subject: [PATCH] Beautify the YOLOv5 configuration (#501)
* refactor_config
---
...v5_m-p6-v62_syncbn_fast_8xb16-300e_coco.py | 14 +-
...olov5_m-v61_syncbn_fast_8xb16-300e_coco.py | 14 +-
...v5_s-p6-v62_syncbn_fast_8xb16-300e_coco.py | 33 ++--
...ov5_s-v61_syncbn-detect_8xb16-300e_coco.py | 4 +-
.../yolov5_s-v61_syncbn_8xb16-300e_coco.py | 145 ++++++++++++------
...ov5_s-v61_syncbn_fast_1xb4-300e_balloon.py | 37 +++--
6 files changed, 157 insertions(+), 90 deletions(-)
diff --git a/configs/yolov5/yolov5_m-p6-v62_syncbn_fast_8xb16-300e_coco.py b/configs/yolov5/yolov5_m-p6-v62_syncbn_fast_8xb16-300e_coco.py
index f2ccf787..f593e378 100644
--- a/configs/yolov5/yolov5_m-p6-v62_syncbn_fast_8xb16-300e_coco.py
+++ b/configs/yolov5/yolov5_m-p6-v62_syncbn_fast_8xb16-300e_coco.py
@@ -1,10 +1,15 @@
_base_ = './yolov5_s-p6-v62_syncbn_fast_8xb16-300e_coco.py'
+# ========================modified parameters======================
deepen_factor = 0.67
widen_factor = 0.75
-lr_factor = 0.1 # lrf=0.1
+lr_factor = 0.1
affine_scale = 0.9
+loss_cls_weight = 0.3
+loss_obj_weight = 0.7
+mixup_prob = 0.1
+# =======================Unmodified in most cases==================
num_classes = _base_.num_classes
num_det_layers = _base_.num_det_layers
img_scale = _base_.img_scale
@@ -20,9 +25,9 @@ model = dict(
),
bbox_head=dict(
head_module=dict(widen_factor=widen_factor),
- loss_cls=dict(loss_weight=0.3 *
+ loss_cls=dict(loss_weight=loss_cls_weight *
(num_classes / 80 * 3 / num_det_layers)),
- loss_obj=dict(loss_weight=0.7 *
+ loss_obj=dict(loss_weight=loss_obj_weight *
((img_scale[0] / 640)**2 * 3 / num_det_layers))))
pre_transform = _base_.pre_transform
@@ -49,7 +54,7 @@ train_pipeline = [
*pre_transform, *mosaic_affine_pipeline,
dict(
type='YOLOv5MixUp',
- prob=0.1,
+ prob=mixup_prob,
pre_transform=[*pre_transform, *mosaic_affine_pipeline]),
dict(
type='mmdet.Albu',
@@ -71,5 +76,4 @@ train_pipeline = [
]
train_dataloader = dict(dataset=dict(pipeline=train_pipeline))
-
default_hooks = dict(param_scheduler=dict(lr_factor=lr_factor))
diff --git a/configs/yolov5/yolov5_m-v61_syncbn_fast_8xb16-300e_coco.py b/configs/yolov5/yolov5_m-v61_syncbn_fast_8xb16-300e_coco.py
index fdce9603..d2ef324e 100644
--- a/configs/yolov5/yolov5_m-v61_syncbn_fast_8xb16-300e_coco.py
+++ b/configs/yolov5/yolov5_m-v61_syncbn_fast_8xb16-300e_coco.py
@@ -1,10 +1,15 @@
_base_ = './yolov5_s-v61_syncbn_fast_8xb16-300e_coco.py'
+# ========================modified parameters======================
deepen_factor = 0.67
widen_factor = 0.75
-lr_factor = 0.1 # lrf=0.1
+lr_factor = 0.1
affine_scale = 0.9
+loss_cls_weight = 0.3
+loss_obj_weight = 0.7
+mixup_prob = 0.1
+# =======================Unmodified in most cases==================
num_classes = _base_.num_classes
num_det_layers = _base_.num_det_layers
img_scale = _base_.img_scale
@@ -20,9 +25,9 @@ model = dict(
),
bbox_head=dict(
head_module=dict(widen_factor=widen_factor),
- loss_cls=dict(loss_weight=0.3 *
+ loss_cls=dict(loss_weight=loss_cls_weight *
(num_classes / 80 * 3 / num_det_layers)),
- loss_obj=dict(loss_weight=0.7 *
+ loss_obj=dict(loss_weight=loss_obj_weight *
((img_scale[0] / 640)**2 * 3 / num_det_layers))))
pre_transform = _base_.pre_transform
@@ -49,7 +54,7 @@ train_pipeline = [
*pre_transform, *mosaic_affine_pipeline,
dict(
type='YOLOv5MixUp',
- prob=0.1,
+ prob=mixup_prob,
pre_transform=[*pre_transform, *mosaic_affine_pipeline]),
dict(
type='mmdet.Albu',
@@ -71,5 +76,4 @@ train_pipeline = [
]
train_dataloader = dict(dataset=dict(pipeline=train_pipeline))
-
default_hooks = dict(param_scheduler=dict(lr_factor=lr_factor))
diff --git a/configs/yolov5/yolov5_s-p6-v62_syncbn_fast_8xb16-300e_coco.py b/configs/yolov5/yolov5_s-p6-v62_syncbn_fast_8xb16-300e_coco.py
index d7cb0925..3d14484f 100644
--- a/configs/yolov5/yolov5_s-p6-v62_syncbn_fast_8xb16-300e_coco.py
+++ b/configs/yolov5/yolov5_s-p6-v62_syncbn_fast_8xb16-300e_coco.py
@@ -1,19 +1,32 @@
_base_ = 'yolov5_s-v61_syncbn_fast_8xb16-300e_coco.py'
+# ========================modified parameters======================
img_scale = (1280, 1280) # width, height
-num_classes = 80
-# only on Val
-batch_shapes_cfg = dict(img_size=img_scale[0], size_divisor=64)
-
+num_classes = 80 # Number of classes for classification
+# Config of batch shapes. Only on val.
+# It means not used if batch_shapes_cfg is None.
+batch_shapes_cfg = dict(
+ img_size=img_scale[0],
+ # The image scale of padding should be divided by pad_size_divisor
+ size_divisor=64)
+# Basic size of multi-scale prior box
anchors = [
[(19, 27), (44, 40), (38, 94)], # P3/8
[(96, 68), (86, 152), (180, 137)], # P4/16
[(140, 301), (303, 264), (238, 542)], # P5/32
[(436, 615), (739, 380), (925, 792)] # P6/64
]
+# Strides of multi-scale prior box
strides = [8, 16, 32, 64]
-num_det_layers = 4
+num_det_layers = 4 # The number of model output scales
+loss_cls_weight = 0.5
+loss_bbox_weight = 0.05
+loss_obj_weight = 1.0
+# The obj loss weights of the three output layers
+obj_level_weights = [4.0, 1.0, 0.25, 0.06]
+affine_scale = 0.5 # YOLOv5RandomAffine scaling ratio
+# =======================Unmodified in most cases==================
model = dict(
backbone=dict(arch='P6', out_indices=(2, 3, 4, 5)),
neck=dict(
@@ -23,12 +36,12 @@ model = dict(
in_channels=[256, 512, 768, 1024], featmap_strides=strides),
prior_generator=dict(base_sizes=anchors, strides=strides),
# scaled based on number of detection layers
- loss_cls=dict(loss_weight=0.5 *
+ loss_cls=dict(loss_weight=loss_cls_weight *
(num_classes / 80 * 3 / num_det_layers)),
- loss_bbox=dict(loss_weight=0.05 * (3 / num_det_layers)),
- loss_obj=dict(loss_weight=1.0 *
+ loss_bbox=dict(loss_weight=loss_bbox_weight * (3 / num_det_layers)),
+ loss_obj=dict(loss_weight=loss_obj_weight *
((img_scale[0] / 640)**2 * 3 / num_det_layers)),
- obj_level_weights=[4.0, 1.0, 0.25, 0.06]))
+ obj_level_weights=obj_level_weights))
pre_transform = _base_.pre_transform
albu_train_transforms = _base_.albu_train_transforms
@@ -44,7 +57,7 @@ train_pipeline = [
type='YOLOv5RandomAffine',
max_rotate_degree=0.0,
max_shear_degree=0.0,
- scaling_ratio_range=(0.5, 1.5),
+ scaling_ratio_range=(1 - affine_scale, 1 + affine_scale),
# img_scale is (width, height)
border=(-img_scale[0] // 2, -img_scale[1] // 2),
border_val=(114, 114, 114)),
diff --git a/configs/yolov5/yolov5_s-v61_syncbn-detect_8xb16-300e_coco.py b/configs/yolov5/yolov5_s-v61_syncbn-detect_8xb16-300e_coco.py
index 2789c959..627f9859 100644
--- a/configs/yolov5/yolov5_s-v61_syncbn-detect_8xb16-300e_coco.py
+++ b/configs/yolov5/yolov5_s-v61_syncbn-detect_8xb16-300e_coco.py
@@ -1,9 +1,7 @@
_base_ = 'yolov5_s-v61_syncbn_8xb16-300e_coco.py'
test_pipeline = [
- dict(
- type='LoadImageFromFile',
- file_client_args={{_base_.file_client_args}}),
+ dict(type='LoadImageFromFile', file_client_args=_base_.file_client_args),
dict(
type='LetterResize',
scale=_base_.img_scale,
diff --git a/configs/yolov5/yolov5_s-v61_syncbn_8xb16-300e_coco.py b/configs/yolov5/yolov5_s-v61_syncbn_8xb16-300e_coco.py
index d06f75c4..b90f6785 100644
--- a/configs/yolov5/yolov5_s-v61_syncbn_8xb16-300e_coco.py
+++ b/configs/yolov5/yolov5_s-v61_syncbn_8xb16-300e_coco.py
@@ -1,47 +1,95 @@
_base_ = '../_base_/default_runtime.py'
-# dataset settings
-data_root = 'data/coco/'
-dataset_type = 'YOLOv5CocoDataset'
+# ========================Frequently modified parameters======================
+# -----data related-----
+data_root = 'data/coco/' # Root path of data
+# Path of train annotation file
+train_ann_file = 'annotations/instances_train2017.json'
+train_data_prefix = 'train2017/' # Prefix of train image path
+# Path of val annotation file
+val_ann_file = 'annotations/instances_val2017.json'
+val_data_prefix = 'val2017/' # Prefix of val image path
-# parameters that often need to be modified
-num_classes = 80
-img_scale = (640, 640) # width, height
-deepen_factor = 0.33
-widen_factor = 0.5
-max_epochs = 300
-save_epoch_intervals = 10
+num_classes = 80 # Number of classes for classification
+# Batch size of a single GPU during training
train_batch_size_per_gpu = 16
+# Worker to pre-fetch data for each single GPU during training
train_num_workers = 8
-val_batch_size_per_gpu = 1
-val_num_workers = 2
-
-# persistent_workers must be False if num_workers is 0.
+# persistent_workers must be False if num_workers is 0
persistent_workers = True
-# Base learning rate for optim_wrapper
-base_lr = 0.01
-
-# only on Val
-batch_shapes_cfg = dict(
- type='BatchShapePolicy',
- batch_size=val_batch_size_per_gpu,
- img_size=img_scale[0],
- size_divisor=32,
- extra_pad_ratio=0.5)
-
+# -----model related-----
+# Basic size of multi-scale prior box
anchors = [
[(10, 13), (16, 30), (33, 23)], # P3/8
[(30, 61), (62, 45), (59, 119)], # P4/16
[(116, 90), (156, 198), (373, 326)] # P5/32
]
-strides = [8, 16, 32]
-num_det_layers = 3
-# single-scale training is recommended to
+# -----train val related-----
+# Base learning rate for optim_wrapper. Corresponding to 8xb16=64 bs
+base_lr = 0.01
+max_epochs = 300 # Maximum training epochs
+
+model_test_cfg = dict(
+ # The config of multi-label for multi-class prediction.
+ multi_label=True,
+ # The number of boxes before NMS
+ nms_pre=30000,
+ score_thr=0.001, # Threshold to filter out boxes.
+ nms=dict(type='nms', iou_threshold=0.65), # NMS type and threshold
+ max_per_img=300) # Max number of detections of each image
+
+# ========================Possible modified parameters========================
+# -----data related-----
+img_scale = (640, 640) # width, height
+# Dataset type, this will be used to define the dataset
+dataset_type = 'YOLOv5CocoDataset'
+# Batch size of a single GPU during validation
+val_batch_size_per_gpu = 1
+# Worker to pre-fetch data for each single GPU during validation
+val_num_workers = 2
+
+# Config of batch shapes. Only on val.
+# It means not used if batch_shapes_cfg is None.
+batch_shapes_cfg = dict(
+ type='BatchShapePolicy',
+ batch_size=val_batch_size_per_gpu,
+ img_size=img_scale[0],
+ # The image scale of padding should be divided by pad_size_divisor
+ size_divisor=32,
+ # Additional paddings for pixel scale
+ extra_pad_ratio=0.5)
+
+# -----model related-----
+# The scaling factor that controls the depth of the network structure
+deepen_factor = 0.33
+# The scaling factor that controls the width of the network structure
+widen_factor = 0.5
+# Strides of multi-scale prior box
+strides = [8, 16, 32]
+num_det_layers = 3 # The number of model output scales
+norm_cfg = dict(type='BN', momentum=0.03, eps=0.001)
+
+# -----train val related-----
+affine_scale = 0.5 # YOLOv5RandomAffine scaling ratio
+loss_cls_weight = 0.5
+loss_bbox_weight = 0.05
+loss_obj_weight = 1.0
+prior_match_thr = 4. # Priori box matching threshold
+obj_level_weights = [4., 1.,
+ 0.4] # The obj loss weights of the three output layers
+lr_factor = 0.01 # Learning rate scaling factor
+weight_decay = 0.0005
+# Save model checkpoint and validation intervals
+save_epoch_intervals = 10
+# The maximum checkpoints to keep.
+max_keep_ckpts = 3
+# Single-scale training is recommended to
# be turned on, which can speed up training.
env_cfg = dict(cudnn_benchmark=True)
+# ===============================Unmodified in most cases====================
model = dict(
type='YOLODetector',
data_preprocessor=dict(
@@ -53,7 +101,7 @@ model = dict(
type='YOLOv5CSPDarknet',
deepen_factor=deepen_factor,
widen_factor=widen_factor,
- norm_cfg=dict(type='BN', momentum=0.03, eps=0.001),
+ norm_cfg=norm_cfg,
act_cfg=dict(type='SiLU', inplace=True)),
neck=dict(
type='YOLOv5PAFPN',
@@ -62,7 +110,7 @@ model = dict(
in_channels=[256, 512, 1024],
out_channels=[256, 512, 1024],
num_csp_blocks=3,
- norm_cfg=dict(type='BN', momentum=0.03, eps=0.001),
+ norm_cfg=norm_cfg,
act_cfg=dict(type='SiLU', inplace=True)),
bbox_head=dict(
type='YOLOv5Head',
@@ -82,28 +130,25 @@ model = dict(
type='mmdet.CrossEntropyLoss',
use_sigmoid=True,
reduction='mean',
- loss_weight=0.5 * (num_classes / 80 * 3 / num_det_layers)),
+ loss_weight=loss_cls_weight *
+ (num_classes / 80 * 3 / num_det_layers)),
loss_bbox=dict(
type='IoULoss',
iou_mode='ciou',
bbox_format='xywh',
eps=1e-7,
reduction='mean',
- loss_weight=0.05 * (3 / num_det_layers),
+ loss_weight=loss_bbox_weight * (3 / num_det_layers),
return_iou=True),
loss_obj=dict(
type='mmdet.CrossEntropyLoss',
use_sigmoid=True,
reduction='mean',
- loss_weight=1.0 * ((img_scale[0] / 640)**2 * 3 / num_det_layers)),
- prior_match_thr=4.,
- obj_level_weights=[4., 1., 0.4]),
- test_cfg=dict(
- multi_label=True,
- nms_pre=30000,
- score_thr=0.001,
- nms=dict(type='nms', iou_threshold=0.65),
- max_per_img=300))
+ loss_weight=loss_obj_weight *
+ ((img_scale[0] / 640)**2 * 3 / num_det_layers)),
+ prior_match_thr=prior_match_thr,
+ obj_level_weights=obj_level_weights),
+ test_cfg=model_test_cfg)
albu_train_transforms = [
dict(type='Blur', p=0.01),
@@ -128,7 +173,7 @@ train_pipeline = [
type='YOLOv5RandomAffine',
max_rotate_degree=0.0,
max_shear_degree=0.0,
- scaling_ratio_range=(0.5, 1.5),
+ scaling_ratio_range=(1 - affine_scale, 1 + affine_scale),
# img_scale is (width, height)
border=(-img_scale[0] // 2, -img_scale[1] // 2),
border_val=(114, 114, 114)),
@@ -160,8 +205,8 @@ train_dataloader = dict(
dataset=dict(
type=dataset_type,
data_root=data_root,
- ann_file='annotations/instances_train2017.json',
- data_prefix=dict(img='train2017/'),
+ ann_file=train_ann_file,
+ data_prefix=dict(img=train_data_prefix),
filter_cfg=dict(filter_empty_gt=False, min_size=32),
pipeline=train_pipeline))
@@ -191,8 +236,8 @@ val_dataloader = dict(
type=dataset_type,
data_root=data_root,
test_mode=True,
- data_prefix=dict(img='val2017/'),
- ann_file='annotations/instances_val2017.json',
+ data_prefix=dict(img=val_data_prefix),
+ ann_file=val_ann_file,
pipeline=test_pipeline,
batch_shapes_cfg=batch_shapes_cfg))
@@ -205,7 +250,7 @@ optim_wrapper = dict(
type='SGD',
lr=base_lr,
momentum=0.937,
- weight_decay=0.0005,
+ weight_decay=weight_decay,
nesterov=True,
batch_size_per_gpu=train_batch_size_per_gpu),
constructor='YOLOv5OptimizerConstructor')
@@ -214,13 +259,13 @@ default_hooks = dict(
param_scheduler=dict(
type='YOLOv5ParamSchedulerHook',
scheduler_type='linear',
- lr_factor=0.01,
+ lr_factor=lr_factor,
max_epochs=max_epochs),
checkpoint=dict(
type='CheckpointHook',
interval=save_epoch_intervals,
save_best='auto',
- max_keep_ckpts=3))
+ max_keep_ckpts=max_keep_ckpts))
custom_hooks = [
dict(
@@ -235,7 +280,7 @@ custom_hooks = [
val_evaluator = dict(
type='mmdet.CocoMetric',
proposal_nums=(100, 1, 10),
- ann_file=data_root + 'annotations/instances_val2017.json',
+ ann_file=data_root + val_ann_file,
metric='bbox')
test_evaluator = val_evaluator
diff --git a/configs/yolov5/yolov5_s-v61_syncbn_fast_1xb4-300e_balloon.py b/configs/yolov5/yolov5_s-v61_syncbn_fast_1xb4-300e_balloon.py
index 529702e2..2c585ceb 100644
--- a/configs/yolov5/yolov5_s-v61_syncbn_fast_1xb4-300e_balloon.py
+++ b/configs/yolov5/yolov5_s-v61_syncbn_fast_1xb4-300e_balloon.py
@@ -1,39 +1,42 @@
_base_ = './yolov5_s-v61_syncbn_fast_8xb16-300e_coco.py'
+# ========================modified parameters======================
data_root = 'data/balloon/'
-
-train_batch_size_per_gpu = 4
-train_num_workers = 2
-
+# Path of train annotation file
+train_ann_file = 'train.json'
+train_data_prefix = 'train/' # Prefix of train image path
+# Path of val annotation file
+val_ann_file = 'val.json'
+val_data_prefix = 'val/' # Prefix of val image path
metainfo = {
'classes': ('balloon', ),
'palette': [
(220, 20, 60),
]
}
+num_classes = 1
+train_batch_size_per_gpu = 4
+train_num_workers = 2
+log_interval = 1
+
+# =======================Unmodified in most cases==================
train_dataloader = dict(
batch_size=train_batch_size_per_gpu,
num_workers=train_num_workers,
dataset=dict(
data_root=data_root,
metainfo=metainfo,
- data_prefix=dict(img='train/'),
- ann_file='train.json'))
-
+ data_prefix=dict(img=train_data_prefix),
+ ann_file=train_ann_file))
val_dataloader = dict(
dataset=dict(
data_root=data_root,
metainfo=metainfo,
- data_prefix=dict(img='val/'),
- ann_file='val.json'))
-
+ data_prefix=dict(img=val_data_prefix),
+ ann_file=val_ann_file))
test_dataloader = val_dataloader
-
-val_evaluator = dict(ann_file=data_root + 'val.json')
-
+val_evaluator = dict(ann_file=data_root + val_ann_file)
test_evaluator = val_evaluator
-
-model = dict(bbox_head=dict(head_module=dict(num_classes=1)))
-
-default_hooks = dict(logger=dict(interval=1))
+model = dict(bbox_head=dict(head_module=dict(num_classes=num_classes)))
+default_hooks = dict(logger=dict(interval=log_interval))