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default_scope = 'mmdet' | |
dataset_type = 'CocoDataset' | |
data_root = '/home/safouane/Downloads/benchmark_aircraft/data/' | |
backend_args = None | |
batch_size = 64 | |
max_epochs = 300 | |
metainfo = { | |
'classes': ('airplane', ), | |
'palette': [ | |
(0, 128, 255), | |
] | |
} | |
num_classes = 1 | |
default_hooks = dict( | |
timer=dict(type='IterTimerHook'), | |
logger=dict(type='LoggerHook', interval=50), | |
param_scheduler=dict(type='ParamSchedulerHook'), | |
checkpoint=dict(type='CheckpointHook', interval=10, max_keep_ckpts=3), | |
sampler_seed=dict(type='DistSamplerSeedHook'), | |
visualization=dict(type='DetVisualizationHook')) | |
env_cfg = dict( | |
cudnn_benchmark=False, | |
mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0), | |
dist_cfg=dict(backend='nccl')) | |
vis_backends = [ | |
dict(type='LocalVisBackend'), | |
] | |
visualizer = dict( | |
type='DetLocalVisualizer', | |
vis_backends=[ | |
dict(type='LocalVisBackend'), | |
dict(type='TensorboardVisBackend'), | |
], | |
name='visualizer') | |
log_processor = dict(type='LogProcessor', window_size=50, by_epoch=True) | |
log_level = 'INFO' | |
load_from = '/home/safouane/Downloads/benchmark_aircraft/mmdetection/configs/rtmdet/checkpoints/rtmdet_tiny_8xb32-300e_coco_20220902_112414-78e30dcc.pth' | |
resume = False | |
train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=500, val_interval=10) | |
val_cfg = dict(type='ValLoop') | |
test_cfg = dict(type='TestLoop') | |
param_scheduler = [ | |
dict( | |
type='LinearLR', start_factor=1e-05, by_epoch=False, begin=0, | |
end=1000), | |
dict( | |
type='CosineAnnealingLR', | |
eta_min=0.0002, | |
begin=150, | |
end=300, | |
T_max=150, | |
by_epoch=True, | |
convert_to_iter_based=True), | |
] | |
optim_wrapper = dict( | |
type='OptimWrapper', | |
optimizer=dict(type='AdamW', lr=0.004, weight_decay=0.05), | |
paramwise_cfg=dict( | |
norm_decay_mult=0, bias_decay_mult=0, bypass_duplicate=True)) | |
auto_scale_lr = dict(enable=False, base_batch_size=16) | |
train_pipeline = [ | |
dict(type='LoadImageFromFile', backend_args=None), | |
dict(type='LoadAnnotations', with_bbox=True), | |
dict( | |
type='CachedMosaic', | |
img_scale=( | |
640, | |
640, | |
), | |
pad_val=114.0, | |
max_cached_images=20, | |
random_pop=False), | |
dict( | |
type='RandomResize', | |
scale=( | |
1280, | |
1280, | |
), | |
ratio_range=( | |
0.5, | |
2.0, | |
), | |
keep_ratio=True), | |
dict(type='RandomCrop', crop_size=( | |
640, | |
640, | |
)), | |
dict(type='YOLOXHSVRandomAug'), | |
dict(type='RandomFlip', prob=0.5), | |
dict(type='Pad', size=( | |
640, | |
640, | |
), pad_val=dict(img=( | |
114, | |
114, | |
114, | |
))), | |
dict( | |
type='CachedMixUp', | |
img_scale=( | |
640, | |
640, | |
), | |
ratio_range=( | |
1.0, | |
1.0, | |
), | |
max_cached_images=10, | |
random_pop=False, | |
pad_val=( | |
114, | |
114, | |
114, | |
), | |
prob=0.5), | |
dict(type='PackDetInputs'), | |
] | |
test_pipeline = [ | |
dict(type='LoadImageFromFile', backend_args=None), | |
dict(type='Resize', scale=( | |
640, | |
640, | |
), keep_ratio=True), | |
dict(type='Pad', size=( | |
640, | |
640, | |
), pad_val=dict(img=( | |
114, | |
114, | |
114, | |
))), | |
dict(type='LoadAnnotations', with_bbox=True), | |
dict( | |
type='PackDetInputs', | |
meta_keys=( | |
'img_id', | |
'img_path', | |
'ori_shape', | |
'img_shape', | |
'scale_factor', | |
)), | |
] | |
train_dataloader = dict( | |
batch_size=64, | |
num_workers=2, | |
persistent_workers=True, | |
sampler=dict(type='DefaultSampler', shuffle=True), | |
batch_sampler=None, | |
dataset=dict( | |
type='CocoDataset', | |
metainfo=dict(classes=('airplane', ), palette=[ | |
( | |
220, | |
20, | |
60, | |
), | |
]), | |
data_root='/home/safouane/Downloads/benchmark_aircraft/data/', | |
ann_file='train/__coco.json', | |
data_prefix=dict(img='train/'), | |
filter_cfg=dict(filter_empty_gt=True, min_size=32), | |
pipeline=[ | |
dict(type='LoadImageFromFile', backend_args=None), | |
dict(type='LoadAnnotations', with_bbox=True), | |
dict( | |
type='CachedMosaic', | |
img_scale=( | |
640, | |
640, | |
), | |
pad_val=114.0, | |
max_cached_images=20, | |
random_pop=False), | |
dict( | |
type='RandomResize', | |
scale=( | |
1280, | |
1280, | |
), | |
ratio_range=( | |
0.5, | |
2.0, | |
), | |
keep_ratio=True), | |
dict(type='RandomCrop', crop_size=( | |
640, | |
640, | |
)), | |
dict(type='YOLOXHSVRandomAug'), | |
dict(type='RandomFlip', prob=0.5), | |
dict( | |
type='Pad', | |
size=( | |
640, | |
640, | |
), | |
pad_val=dict(img=( | |
114, | |
114, | |
114, | |
))), | |
dict( | |
type='CachedMixUp', | |
img_scale=( | |
640, | |
640, | |
), | |
ratio_range=( | |
1.0, | |
1.0, | |
), | |
max_cached_images=10, | |
random_pop=False, | |
pad_val=( | |
114, | |
114, | |
114, | |
), | |
prob=0.5), | |
dict(type='PackDetInputs'), | |
], | |
backend_args=None), | |
pin_memory=True) | |
val_dataloader = dict( | |
batch_size=64, | |
num_workers=2, | |
persistent_workers=True, | |
drop_last=False, | |
sampler=dict(type='DefaultSampler', shuffle=False), | |
dataset=dict( | |
type='CocoDataset', | |
metainfo=dict(classes=('airplane', ), palette=[ | |
( | |
220, | |
20, | |
60, | |
), | |
]), | |
data_root='/home/safouane/Downloads/benchmark_aircraft/data/', | |
ann_file='val/__coco.json', | |
data_prefix=dict(img='val/'), | |
test_mode=True, | |
pipeline=[ | |
dict(type='LoadImageFromFile', backend_args=None), | |
dict(type='Resize', scale=( | |
640, | |
640, | |
), keep_ratio=True), | |
dict( | |
type='Pad', | |
size=( | |
640, | |
640, | |
), | |
pad_val=dict(img=( | |
114, | |
114, | |
114, | |
))), | |
dict(type='LoadAnnotations', with_bbox=True), | |
dict( | |
type='PackDetInputs', | |
meta_keys=( | |
'img_id', | |
'img_path', | |
'ori_shape', | |
'img_shape', | |
'scale_factor', | |
)), | |
], | |
backend_args=None)) | |
test_dataloader = dict( | |
batch_size=64, | |
num_workers=2, | |
persistent_workers=True, | |
drop_last=False, | |
sampler=dict(type='DefaultSampler', shuffle=False), | |
dataset=dict( | |
type='CocoDataset', | |
metainfo=dict(classes=('airplane', ), palette=[ | |
( | |
220, | |
20, | |
60, | |
), | |
]), | |
data_root='/home/safouane/Downloads/benchmark_aircraft/data/', | |
ann_file='test/__coco.json', | |
data_prefix=dict(img='test/'), | |
test_mode=True, | |
pipeline=[ | |
dict(type='LoadImageFromFile', backend_args=None), | |
dict(type='Resize', scale=( | |
640, | |
640, | |
), keep_ratio=True), | |
dict( | |
type='Pad', | |
size=( | |
640, | |
640, | |
), | |
pad_val=dict(img=( | |
114, | |
114, | |
114, | |
))), | |
dict(type='LoadAnnotations', with_bbox=True), | |
dict( | |
type='PackDetInputs', | |
meta_keys=( | |
'img_id', | |
'img_path', | |
'ori_shape', | |
'img_shape', | |
'scale_factor', | |
)), | |
], | |
backend_args=None)) | |
val_evaluator = dict( | |
type='CocoMetric', | |
ann_file='/home/safouane/Downloads/benchmark_aircraft/data/val/__coco.json', | |
metric='bbox', | |
format_only=False, | |
backend_args=None) | |
test_evaluator = dict( | |
type='CocoMetric', | |
ann_file= | |
'/home/safouane/Downloads/benchmark_aircraft/data/test/__coco.json', | |
metric='bbox', | |
format_only=False, | |
backend_args=None) | |
tta_model = dict( | |
type='DetTTAModel', | |
tta_cfg=dict(nms=dict(type='nms', iou_threshold=0.6), max_per_img=100)) | |
img_scales = [ | |
( | |
640, | |
640, | |
), | |
( | |
320, | |
320, | |
), | |
( | |
960, | |
960, | |
), | |
] | |
tta_pipeline = [ | |
dict(type='LoadImageFromFile', backend_args=None), | |
dict( | |
type='TestTimeAug', | |
transforms=[ | |
[ | |
dict(type='Resize', scale=( | |
640, | |
640, | |
), keep_ratio=True), | |
dict(type='Resize', scale=( | |
320, | |
320, | |
), keep_ratio=True), | |
dict(type='Resize', scale=( | |
960, | |
960, | |
), keep_ratio=True), | |
], | |
[ | |
dict(type='RandomFlip', prob=1.0), | |
dict(type='RandomFlip', prob=0.0), | |
], | |
[ | |
dict( | |
type='Pad', | |
size=( | |
960, | |
960, | |
), | |
pad_val=dict(img=( | |
114, | |
114, | |
114, | |
))), | |
], | |
[ | |
dict(type='LoadAnnotations', with_bbox=True), | |
], | |
[ | |
dict( | |
type='PackDetInputs', | |
meta_keys=( | |
'img_id', | |
'img_path', | |
'ori_shape', | |
'img_shape', | |
'scale_factor', | |
'flip', | |
'flip_direction', | |
)), | |
], | |
]), | |
] | |
model = dict( | |
type='RTMDet', | |
data_preprocessor=dict( | |
type='DetDataPreprocessor', | |
mean=[ | |
103.53, | |
116.28, | |
123.675, | |
], | |
std=[ | |
57.375, | |
57.12, | |
58.395, | |
], | |
bgr_to_rgb=False, | |
batch_augments=None), | |
backbone=dict( | |
type='CSPNeXt', | |
arch='P5', | |
expand_ratio=0.5, | |
deepen_factor=0.167, | |
widen_factor=0.375, | |
channel_attention=True, | |
norm_cfg=dict(type='SyncBN'), | |
act_cfg=dict(type='SiLU', inplace=True), | |
init_cfg=dict( | |
type='Pretrained', | |
prefix='backbone.', | |
checkpoint= | |
'https://download.openmmlab.com/mmdetection/v3.0/rtmdet/cspnext_rsb_pretrain/cspnext-tiny_imagenet_600e.pth' | |
)), | |
neck=dict( | |
type='CSPNeXtPAFPN', | |
in_channels=[ | |
96, | |
192, | |
384, | |
], | |
out_channels=96, | |
num_csp_blocks=1, | |
expand_ratio=0.5, | |
norm_cfg=dict(type='SyncBN'), | |
act_cfg=dict(type='SiLU', inplace=True)), | |
bbox_head=dict( | |
type='RTMDetSepBNHead', | |
num_classes=1, | |
in_channels=96, | |
stacked_convs=2, | |
feat_channels=96, | |
anchor_generator=dict( | |
type='MlvlPointGenerator', offset=0, strides=[ | |
8, | |
16, | |
32, | |
]), | |
bbox_coder=dict(type='DistancePointBBoxCoder'), | |
loss_cls=dict( | |
type='QualityFocalLoss', | |
use_sigmoid=True, | |
beta=2.0, | |
loss_weight=1.0), | |
loss_bbox=dict(type='GIoULoss', loss_weight=2.0), | |
with_objectness=False, | |
exp_on_reg=False, | |
share_conv=True, | |
pred_kernel_size=1, | |
norm_cfg=dict(type='SyncBN'), | |
act_cfg=dict(type='SiLU', inplace=True)), | |
train_cfg=dict( | |
assigner=dict(type='DynamicSoftLabelAssigner', topk=13), | |
allowed_border=-1, | |
pos_weight=-1, | |
debug=False), | |
test_cfg=dict( | |
nms_pre=30000, | |
min_bbox_size=0, | |
score_thr=0.001, | |
nms=dict(type='nms', iou_threshold=0.65), | |
max_per_img=300)) | |
train_pipeline_stage2 = [ | |
dict(type='LoadImageFromFile', backend_args=None), | |
dict(type='LoadAnnotations', with_bbox=True), | |
dict( | |
type='RandomResize', | |
scale=( | |
640, | |
640, | |
), | |
ratio_range=( | |
0.5, | |
2.0, | |
), | |
keep_ratio=True), | |
dict(type='RandomCrop', crop_size=( | |
640, | |
640, | |
)), | |
dict(type='YOLOXHSVRandomAug'), | |
dict(type='RandomFlip', prob=0.5), | |
dict(type='Pad', size=( | |
640, | |
640, | |
), pad_val=dict(img=( | |
114, | |
114, | |
114, | |
))), | |
dict(type='PackDetInputs'), | |
] | |
stage2_num_epochs = 20 | |
base_lr = 0.004 | |
interval = 10 | |
custom_hooks = [ | |
dict( | |
type='EMAHook', | |
ema_type='ExpMomentumEMA', | |
momentum=0.0002, | |
update_buffers=True, | |
priority=49), | |
dict( | |
type='PipelineSwitchHook', | |
switch_epoch=280, | |
switch_pipeline=[ | |
dict(type='LoadImageFromFile', backend_args=None), | |
dict(type='LoadAnnotations', with_bbox=True), | |
dict( | |
type='RandomResize', | |
scale=( | |
640, | |
640, | |
), | |
ratio_range=( | |
0.5, | |
2.0, | |
), | |
keep_ratio=True), | |
dict(type='RandomCrop', crop_size=( | |
640, | |
640, | |
)), | |
dict(type='YOLOXHSVRandomAug'), | |
dict(type='RandomFlip', prob=0.5), | |
dict( | |
type='Pad', | |
size=( | |
640, | |
640, | |
), | |
pad_val=dict(img=( | |
114, | |
114, | |
114, | |
))), | |
dict(type='PackDetInputs'), | |
]), | |
] | |
checkpoint = 'https://download.openmmlab.com/mmdetection/v3.0/rtmdet/cspnext_rsb_pretrain/cspnext-tiny_imagenet_600e.pth' | |
launcher = 'none' | |
work_dir = './work_dirs/rtmdet_tiny_8xb32-300e_coco' | |