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auto_scale_lr = dict(base_batch_size=16)
backend_args = None
batch_augments = [
dict(size=(
1024,
1024,
), type='BatchFixedSizePad'),
]
classes = 'license_plate'
custom_imports = dict(
allow_failed_imports=False, imports=[
'projects.CO-DETR.codetr',
])
data_root = '/home/worawit.tepsan/Project_AI/Detection/data'
dataset_type = 'CocoDataset'
default_hooks = dict(
checkpoint=dict(
_scope_='mmdet',
by_epoch=True,
interval=1,
max_keep_ckpts=3,
type='CheckpointHook'),
logger=dict(_scope_='mmdet', interval=50, type='LoggerHook'),
param_scheduler=dict(_scope_='mmdet', type='ParamSchedulerHook'),
sampler_seed=dict(_scope_='mmdet', type='DistSamplerSeedHook'),
timer=dict(_scope_='mmdet', type='IterTimerHook'),
visualization=dict(
_scope_='mmdet',
draw=True,
test_out_dir=
'/home/worawit.tepsan/Project_AI/Detection/data_testing_LPR',
type='DetVisualizationHook'))
default_scope = 'mmdet'
env_cfg = dict(
cudnn_benchmark=False,
dist_cfg=dict(backend='nccl'),
mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0))
image_size = (
1024,
1024,
)
launcher = 'slurm'
load_from = '/home/worawit.tepsan/Project_AI/Detection/object_detection/workdir/epoch_13.pth'
load_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True),
dict(
keep_ratio=True,
ratio_range=(
0.1,
2.0,
),
scale=(
1024,
1024,
),
type='RandomResize'),
dict(
allow_negative_crop=True,
crop_size=(
1024,
1024,
),
crop_type='absolute_range',
recompute_bbox=True,
type='RandomCrop'),
dict(min_gt_bbox_wh=(
0.01,
0.01,
), type='FilterAnnotations'),
dict(prob=0.5, type='RandomFlip'),
dict(pad_val=dict(img=(
114,
114,
114,
)), size=(
1024,
1024,
), type='Pad'),
]
log_level = 'INFO'
log_processor = dict(
_scope_='mmdet', by_epoch=True, type='LogProcessor', window_size=50)
loss_lambda = 2.0
max_epochs = 32
max_iters = 270000
metainfo = dict(classes='license_plate')
model = dict(
backbone=dict(
attn_drop_rate=0.0,
convert_weights=True,
depths=[
2,
2,
18,
2,
],
drop_path_rate=0.3,
drop_rate=0.0,
embed_dims=192,
init_cfg=dict(
checkpoint=
'/home/worawit.tepsan/Project_AI/Detection/pretrained_models/swin_large_patch4_window12_384_22k.pth',
type='Pretrained'),
mlp_ratio=4,
num_heads=[
6,
12,
24,
48,
],
out_indices=(
0,
1,
2,
3,
),
patch_norm=True,
pretrain_img_size=384,
qk_scale=None,
qkv_bias=True,
type='SwinTransformer',
window_size=12,
with_cp=False),
bbox_head=[
dict(
anchor_generator=dict(
octave_base_scale=8,
ratios=[
1.0,
],
scales_per_octave=1,
strides=[
4,
8,
16,
32,
64,
128,
],
type='AnchorGenerator'),
bbox_coder=dict(
target_means=[
0.0,
0.0,
0.0,
0.0,
],
target_stds=[
0.1,
0.1,
0.2,
0.2,
],
type='DeltaXYWHBBoxCoder'),
feat_channels=256,
in_channels=256,
loss_bbox=dict(loss_weight=24.0, type='GIoULoss'),
loss_centerness=dict(
loss_weight=12.0, type='CrossEntropyLoss', use_sigmoid=True),
loss_cls=dict(
alpha=0.25,
gamma=2.0,
loss_weight=12.0,
type='FocalLoss',
use_sigmoid=True),
num_classes=1,
stacked_convs=1,
type='CoATSSHead'),
],
data_preprocessor=dict(
batch_augments=None,
bgr_to_rgb=True,
mean=[
123.675,
116.28,
103.53,
],
pad_mask=False,
std=[
58.395,
57.12,
57.375,
],
type='DetDataPreprocessor'),
eval_module='detr',
neck=dict(
act_cfg=None,
in_channels=[
192,
384,
768,
1536,
],
kernel_size=1,
norm_cfg=dict(num_groups=32, type='GN'),
num_outs=5,
out_channels=256,
type='ChannelMapper'),
query_head=dict(
as_two_stage=True,
dn_cfg=dict(
box_noise_scale=1.0,
group_cfg=dict(dynamic=True, num_dn_queries=100, num_groups=None),
label_noise_scale=0.5),
in_channels=2048,
loss_bbox=dict(loss_weight=5.0, type='L1Loss'),
loss_cls=dict(
beta=2.0,
loss_weight=1.0,
type='QualityFocalLoss',
use_sigmoid=True),
loss_iou=dict(loss_weight=2.0, type='GIoULoss'),
num_classes=1,
num_query=900,
positional_encoding=dict(
normalize=True,
num_feats=128,
temperature=20,
type='SinePositionalEncoding'),
transformer=dict(
decoder=dict(
num_layers=6,
return_intermediate=True,
transformerlayers=dict(
attn_cfgs=[
dict(
dropout=0.0,
embed_dims=256,
num_heads=8,
type='MultiheadAttention'),
dict(
dropout=0.0,
embed_dims=256,
num_levels=5,
type='MultiScaleDeformableAttention'),
],
feedforward_channels=2048,
ffn_dropout=0.0,
operation_order=(
'self_attn',
'norm',
'cross_attn',
'norm',
'ffn',
'norm',
),
type='DetrTransformerDecoderLayer'),
type='DinoTransformerDecoder'),
encoder=dict(
num_layers=6,
transformerlayers=dict(
attn_cfgs=dict(
dropout=0.0,
embed_dims=256,
num_levels=5,
type='MultiScaleDeformableAttention'),
feedforward_channels=2048,
ffn_dropout=0.0,
operation_order=(
'self_attn',
'norm',
'ffn',
'norm',
),
type='BaseTransformerLayer'),
type='DetrTransformerEncoder',
with_cp=6),
num_co_heads=2,
num_feature_levels=5,
type='CoDinoTransformer',
with_coord_feat=False),
type='CoDINOHead'),
roi_head=[
dict(
bbox_head=dict(
bbox_coder=dict(
target_means=[
0.0,
0.0,
0.0,
0.0,
],
target_stds=[
0.1,
0.1,
0.2,
0.2,
],
type='DeltaXYWHBBoxCoder'),
fc_out_channels=1024,
in_channels=256,
loss_bbox=dict(loss_weight=120.0, type='GIoULoss'),
loss_cls=dict(
loss_weight=12.0,
type='CrossEntropyLoss',
use_sigmoid=False),
num_classes=1,
reg_class_agnostic=False,
reg_decoded_bbox=True,
roi_feat_size=7,
type='Shared2FCBBoxHead'),
bbox_roi_extractor=dict(
featmap_strides=[
4,
8,
16,
32,
64,
],
finest_scale=56,
out_channels=256,
roi_layer=dict(
output_size=7, sampling_ratio=0, type='RoIAlign'),
type='SingleRoIExtractor'),
type='CoStandardRoIHead'),
],
rpn_head=dict(
anchor_generator=dict(
octave_base_scale=4,
ratios=[
0.5,
1.0,
2.0,
],
scales_per_octave=3,
strides=[
4,
8,
16,
32,
64,
128,
],
type='AnchorGenerator'),
bbox_coder=dict(
target_means=[
0.0,
0.0,
0.0,
0.0,
],
target_stds=[
1.0,
1.0,
1.0,
1.0,
],
type='DeltaXYWHBBoxCoder'),
feat_channels=256,
in_channels=256,
loss_bbox=dict(loss_weight=12.0, type='L1Loss'),
loss_cls=dict(
loss_weight=12.0, type='CrossEntropyLoss', use_sigmoid=True),
type='RPNHead'),
test_cfg=[
dict(max_per_img=300, nms=dict(iou_threshold=0.8, type='soft_nms')),
dict(
rcnn=dict(
max_per_img=100,
nms=dict(iou_threshold=0.5, type='nms'),
score_thr=0.0),
rpn=dict(
max_per_img=1000,
min_bbox_size=0,
nms=dict(iou_threshold=0.7, type='nms'),
nms_pre=1000)),
dict(
max_per_img=100,
min_bbox_size=0,
nms=dict(iou_threshold=0.6, type='nms'),
nms_pre=1000,
score_thr=0.0),
],
train_cfg=[
dict(
assigner=dict(
match_costs=[
dict(type='FocalLossCost', weight=2.0),
dict(box_format='xywh', type='BBoxL1Cost', weight=5.0),
dict(iou_mode='giou', type='IoUCost', weight=2.0),
],
type='HungarianAssigner')),
dict(
rcnn=dict(
assigner=dict(
ignore_iof_thr=-1,
match_low_quality=False,
min_pos_iou=0.5,
neg_iou_thr=0.5,
pos_iou_thr=0.5,
type='MaxIoUAssigner'),
debug=False,
pos_weight=-1,
sampler=dict(
add_gt_as_proposals=True,
neg_pos_ub=-1,
num=512,
pos_fraction=0.25,
type='RandomSampler')),
rpn=dict(
allowed_border=-1,
assigner=dict(
ignore_iof_thr=-1,
match_low_quality=True,
min_pos_iou=0.3,
neg_iou_thr=0.3,
pos_iou_thr=0.7,
type='MaxIoUAssigner'),
debug=False,
pos_weight=-1,
sampler=dict(
add_gt_as_proposals=False,
neg_pos_ub=-1,
num=256,
pos_fraction=0.5,
type='RandomSampler')),
rpn_proposal=dict(
max_per_img=1000,
min_bbox_size=0,
nms=dict(iou_threshold=0.7, type='nms'),
nms_pre=4000)),
dict(
allowed_border=-1,
assigner=dict(topk=9, type='ATSSAssigner'),
debug=False,
pos_weight=-1),
],
type='CoDETR',
use_lsj=False)
num_classes = 1
num_dec_layer = 6
optim_wrapper = dict(
clip_grad=dict(max_norm=0.1, norm_type=2),
optimizer=dict(lr=0.0002, type='AdamW', weight_decay=0.0001),
paramwise_cfg=dict(custom_keys=dict(backbone=dict(lr_mult=0.1))),
type='OptimWrapper')
param_scheduler = [
dict(
begin=0,
by_epoch=True,
end=12,
gamma=0.1,
milestones=[
11,
],
type='MultiStepLR'),
]
pretrained = '/home/worawit.tepsan/Project_AI/Detection/pretrained_models/swin_large_patch4_window12_384_22k.pth'
resume = False
test_cfg = dict(_scope_='mmdet', type='TestLoop')
test_dataloader = dict(
batch_size=2,
dataset=dict(
_scope_='mmdet',
ann_file='annotations/instances_test.json',
data_prefix=dict(img='test/'),
data_root='/home/worawit.tepsan/Project_AI/Detection/data',
metainfo=dict(classes='license_plate'),
pipeline=[
dict(backend_args=None, type='LoadImageFromFile'),
dict(keep_ratio=True, scale=(
1333,
800,
), type='Resize'),
dict(
meta_keys=(
'img_id',
'img_path',
'ori_shape',
'img_shape',
'scale_factor',
),
type='PackDetInputs'),
],
test_mode=True,
type='CocoDataset'),
drop_last=False,
num_workers=8,
persistent_workers=True,
sampler=dict(_scope_='mmdet', shuffle=False, type='DefaultSampler'))
test_evaluator = dict(
_scope_='mmdet',
ann_file='annotations/instances_test.json',
format_only=False,
metric='bbox',
outfile_prefix=
'/home/worawit.tepsan/Project_AI/Detection/object_detection/workdir/coco_detection/test',
type='CocoMetric')
test_pipeline = [
dict(backend_args=None, type='LoadImageFromFile'),
dict(keep_ratio=True, scale=(
1333,
800,
), type='Resize'),
dict(
meta_keys=(
'img_id',
'img_path',
'ori_shape',
'img_shape',
'scale_factor',
),
type='PackDetInputs'),
]
train_cfg = dict(max_epochs=32, type='EpochBasedTrainLoop', val_interval=1)
train_dataloader = dict(
batch_size=2,
dataset=dict(
ann_file='annotations/instances_train.json',
backend_args=None,
data_prefix=dict(img='train/'),
data_root='/home/worawit.tepsan/Project_AI/Detection/data',
filter_cfg=dict(filter_empty_gt=False, min_size=32),
metainfo=dict(classes='license_plate'),
pipeline=[
dict(backend_args=None, type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True),
dict(prob=0.5, type='RandomFlip'),
dict(
transforms=[
[
dict(
keep_ratio=True,
scales=[
(
480,
1333,
),
(
512,
1333,
),
(
544,
1333,
),
(
576,
1333,
),
(
608,
1333,
),
(
640,
1333,
),
(
672,
1333,
),
(
704,
1333,
),
(
736,
1333,
),
(
768,
1333,
),
(
800,
1333,
),
],
type='RandomChoiceResize'),
],
[
dict(
keep_ratio=True,
scales=[
(
400,
4200,
),
(
500,
4200,
),
(
600,
4200,
),
],
type='RandomChoiceResize'),
dict(
allow_negative_crop=True,
crop_size=(
384,
600,
),
crop_type='absolute_range',
type='RandomCrop'),
dict(
keep_ratio=True,
scales=[
(
480,
1333,
),
(
512,
1333,
),
(
544,
1333,
),
(
576,
1333,
),
(
608,
1333,
),
(
640,
1333,
),
(
672,
1333,
),
(
704,
1333,
),
(
736,
1333,
),
(
768,
1333,
),
(
800,
1333,
),
],
type='RandomChoiceResize'),
],
],
type='RandomChoice'),
dict(type='PackDetInputs'),
],
type='CocoDataset'),
num_workers=2,
persistent_workers=True,
sampler=dict(_scope_='mmdet', shuffle=True, type='DefaultSampler'))
train_pipeline = [
dict(backend_args=None, type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True),
dict(prob=0.5, type='RandomFlip'),
dict(
transforms=[
[
dict(
keep_ratio=True,
scales=[
(
480,
1333,
),
(
512,
1333,
),
(
544,
1333,
),
(
576,
1333,
),
(
608,
1333,
),
(
640,
1333,
),
(
672,
1333,
),
(
704,
1333,
),
(
736,
1333,
),
(
768,
1333,
),
(
800,
1333,
),
],
type='RandomChoiceResize'),
],
[
dict(
keep_ratio=True,
scales=[
(
400,
4200,
),
(
500,
4200,
),
(
600,
4200,
),
],
type='RandomChoiceResize'),
dict(
allow_negative_crop=True,
crop_size=(
384,
600,
),
crop_type='absolute_range',
type='RandomCrop'),
dict(
keep_ratio=True,
scales=[
(
480,
1333,
),
(
512,
1333,
),
(
544,
1333,
),
(
576,
1333,
),
(
608,
1333,
),
(
640,
1333,
),
(
672,
1333,
),
(
704,
1333,
),
(
736,
1333,
),
(
768,
1333,
),
(
800,
1333,
),
],
type='RandomChoiceResize'),
],
],
type='RandomChoice'),
dict(type='PackDetInputs'),
]
val_cfg = dict(_scope_='mmdet', type='ValLoop')
val_dataloader = dict(
batch_size=2,
dataset=dict(
_scope_='mmdet',
ann_file='annotations/instances_val.json',
backend_args=None,
data_prefix=dict(img='val/'),
data_root='/home/worawit.tepsan/Project_AI/Detection/data',
metainfo=dict(classes='license_plate'),
pipeline=[
dict(backend_args=None, type='LoadImageFromFile'),
dict(keep_ratio=True, scale=(
1333,
800,
), type='Resize'),
dict(type='LoadAnnotations', with_bbox=True),
dict(
meta_keys=(
'img_id',
'img_path',
'ori_shape',
'img_shape',
'scale_factor',
),
type='PackDetInputs'),
],
test_mode=True,
type='CocoDataset'),
drop_last=False,
num_workers=2,
persistent_workers=True,
sampler=dict(_scope_='mmdet', shuffle=False, type='DefaultSampler'))
val_evaluator = dict(
_scope_='mmdet',
ann_file=
'/home/worawit.tepsan/Project_AI/Detection/data/annotations/instances_val.json',
backend_args=None,
format_only=False,
metric='bbox',
type='CocoMetric')
val_pipeline = [
dict(backend_args=None, type='LoadImageFromFile'),
dict(keep_ratio=True, scale=(
1333,
800,
), type='Resize'),
dict(type='LoadAnnotations', with_bbox=True),
dict(
meta_keys=(
'img_id',
'img_path',
'ori_shape',
'img_shape',
'scale_factor',
),
type='PackDetInputs'),
]
vis_backends = [
dict(_scope_='mmdet', type='LocalVisBackend'),
]
visualizer = dict(
_scope_='mmdet',
name='visualizer',
type='DetLocalVisualizer',
vis_backends=[
dict(type='LocalVisBackend'),
])
work_dir = '/home/worawit.tepsan/Project_AI/Detection/object_detection/workdir'
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