Comments (5)
HikariTJU
commented on August 28, 2024
Can you paste ld_gfl_R18_R101_1x training log here?
And btw what do you mean by "Finetune 'faster-rcnn_r18_fpn_1x.py' config"? We didn't provide code to train LD in Faster-RCNN
from ld.
melika-sce
commented on August 28, 2024
Oh sorry, I made mistake pasting the name of configs, I finetuned gfl_r18_fpn_1x and gfl_r101_fpn_1x
- training log of ld_gfl_R18_R101_1x:
2023/07/13 08:59:35 - mmengine - INFO -
------------------------------------------------------------
System environment:
sys.platform: linux
Python: 3.8.16 (default, Jun 12 2023, 18:09:05) [GCC 11.2.0]
CUDA available: True
numpy_random_seed: 903740675
GPU 0: NVIDIA GeForce RTX 3090
CUDA_HOME: /usr/local/cuda
NVCC: Cuda compilation tools, release 11.3, V11.3.109
GCC: gcc (GCC) 4.4.7 20120313 (Red Hat 4.4.7-18)
PyTorch: 2.0.1
PyTorch compiling details: PyTorch built with:
- GCC 9.3
- C++ Version: 201703
- Intel(R) oneAPI Math Kernel Library Version 2023.1-Product Build 20230303 for Intel(R) 64 architecture applications
- Intel(R) MKL-DNN v2.7.3 (Git Hash 6dbeffbae1f23cbbeae17adb7b5b13f1f37c080e)
- OpenMP 201511 (a.k.a. OpenMP 4.5)
- LAPACK is enabled (usually provided by MKL)
- NNPACK is enabled
- CPU capability usage: AVX2
- CUDA Runtime 11.7
- NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_37,code=compute_37
- CuDNN 8.5
- Magma 2.6.1
- Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.7, CUDNN_VERSION=8.5.0, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -D_GLIBCXX_USE_CXX11_ABI=0 -fabi-version=11 -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOROCTRACER -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wunused-local-typedefs -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Werror=cast-function-type -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_DISABLE_GPU_ASSERTS=ON, TORCH_VERSION=2.0.1, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF,
TorchVision: 0.15.2
OpenCV: 4.7.0
MMEngine: 0.7.4
Runtime environment:
cudnn_benchmark: False
mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0}
dist_cfg: {'backend': 'nccl'}
seed: 903740675
Distributed launcher: none
Distributed training: False
GPU number: 1
------------------------------------------------------------
2023/07/13 08:59:36 - mmengine - INFO - Config:
dataset_type = 'CocoDataset'
data_root = 'dataset/VisDrone/'
classes = ('pedestrian', 'people', 'bicycle', 'car', 'van', 'truck',
'tricycle', 'awning-tricycle', 'bus', 'motor')
METAINFO = dict(
classes=('pedestrian', 'people', 'bicycle', 'car', 'van', 'truck',
'tricycle', 'awning-tricycle', 'bus', 'motor'))
backend_args = None
train_pipeline = [
dict(type='LoadImageFromFile', backend_args=None),
dict(type='LoadAnnotations', with_bbox=True),
dict(type='Resize', scale=(640, 640), keep_ratio=True),
dict(type='RandomFlip', 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='LoadAnnotations', with_bbox=True),
dict(
type='PackDetInputs',
meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
'scale_factor'))
]
train_dataloader = dict(
batch_size=2,
num_workers=2,
persistent_workers=True,
sampler=dict(type='DefaultSampler', shuffle=True),
batch_sampler=dict(type='AspectRatioBatchSampler'),
dataset=dict(
type='CocoDataset',
data_root='dataset/VisDrone/',
ann_file='VisDrone2019-DET-train/annotations_VisDrone_train.json',
data_prefix=dict(img='VisDrone2019-DET-train/images/'),
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='Resize', scale=(640, 640), keep_ratio=True),
dict(type='RandomFlip', prob=0.5),
dict(type='PackDetInputs')
],
backend_args=None))
val_dataloader = dict(
batch_size=1,
num_workers=2,
persistent_workers=True,
drop_last=False,
sampler=dict(type='DefaultSampler', shuffle=False),
dataset=dict(
type='CocoDataset',
data_root=dataset/VisDrone/',
ann_file='VisDrone2019-DET-val/annotations_VisDrone_val.json',
data_prefix=dict(img='VisDrone2019-DET-val/images/'),
test_mode=True,
pipeline=[
dict(type='LoadImageFromFile', backend_args=None),
dict(type='Resize', scale=(640, 640), keep_ratio=True),
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=1,
num_workers=2,
persistent_workers=True,
drop_last=False,
sampler=dict(type='DefaultSampler', shuffle=False),
dataset=dict(
type='CocoDataset',
data_root='dataset/VisDrone/',
ann_file='VisDrone2019-DET-test-dev/annotations_VisDrone_dev.json',
data_prefix=dict(img='VisDrone2019-DET-test-dev/images/'),
test_mode=True,
pipeline=[
dict(type='LoadImageFromFile', backend_args=None),
dict(type='Resize', scale=(640, 640), keep_ratio=True),
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=
'dataset/VisDrone/VisDrone2019-DET-val/annotations_VisDrone_val.json',
metric='bbox',
format_only=False,
backend_args=None)
test_evaluator = dict(
type='CocoMetric',
ann_file=
'dataset/VisDrone/VisDrone2019-DET-test-dev/annotations_VisDrone_dev.json',
metric='bbox',
format_only=True,
backend_args=None,
outfile_prefix='./work_dirs/visdrone_detection/test')
train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=12, val_interval=1)
val_cfg = dict(type='ValLoop')
test_cfg = dict(type='TestLoop')
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
begin=0,
end=12,
by_epoch=True,
milestones=[8, 11],
gamma=0.1)
]
optim_wrapper = dict(
type='OptimWrapper',
optimizer=dict(type='SGD', lr=0.00125, momentum=0.9, weight_decay=0.0001))
auto_scale_lr = dict(enable=False, base_batch_size=16)
default_scope = 'mmdet'
default_hooks = dict(
timer=dict(type='IterTimerHook'),
logger=dict(type='LoggerHook', interval=50),
param_scheduler=dict(type='ParamSchedulerHook'),
checkpoint=dict(type='CheckpointHook', interval=1),
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')],
name='visualizer')
log_processor = dict(type='LogProcessor', window_size=50, by_epoch=True)
log_level = 'INFO'
load_from = None
resume = False
teacher_ckpt = 'mmdetection/work_dirs/gfl_r101_fpn_finetune_vis_1x/epoch_12.pth'
model = dict(
type='KnowledgeDistillationSingleStageDetector',
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
bgr_to_rgb=True,
pad_size_divisor=32),
teacher_config='configs/ld/gfl_r101_fpn_finetune_vis_1x.py',
teacher_ckpt=
'mmdetection/work_dirs/gfl_r101_fpn_finetune_vis_1x/epoch_12.pth',
backbone=dict(
type='ResNet',
depth=18,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init_cfg=dict(
type='Pretrained',
checkpoint=
'mmdetection/work_dirs/gfl_r18_fpn_finetune_vis_1x/epoch_12.pth'
)),
neck=dict(
type='FPN',
in_channels=[64, 128, 256, 512],
out_channels=256,
start_level=1,
add_extra_convs='on_output',
num_outs=5),
bbox_head=dict(
type='LDHead',
num_classes=10,
in_channels=256,
stacked_convs=4,
feat_channels=256,
anchor_generator=dict(
type='AnchorGenerator',
ratios=[1.0],
octave_base_scale=8,
scales_per_octave=1,
strides=[8, 16, 32, 64, 128]),
loss_cls=dict(
type='QualityFocalLoss',
use_sigmoid=True,
beta=2.0,
loss_weight=1.0),
loss_dfl=dict(type='DistributionFocalLoss', loss_weight=0.25),
loss_ld=dict(
type='KnowledgeDistillationKLDivLoss', loss_weight=0.25, T=10),
reg_max=16,
loss_bbox=dict(type='GIoULoss', loss_weight=2.0)),
train_cfg=dict(
assigner=dict(type='ATSSAssigner', topk=9),
allowed_border=-1,
pos_weight=-1,
debug=False),
test_cfg=dict(
nms_pre=1000,
min_bbox_size=0,
score_thr=0.05,
nms=dict(type='nms', iou_threshold=0.6),
max_per_img=100))
launcher = 'none'
work_dir = 'mmdetection/work_dir/ld_gfl_r18_r101_fpn_1x_vis'
2023/07/13 08:59:43 - mmengine - INFO - Distributed training is not used, all SyncBatchNorm (SyncBN) layers in the model will be automatically reverted to BatchNormXd layers if they are used.
2023/07/13 08:59:43 - mmengine - INFO - Hooks will be executed in the following order:
before_run:
(VERY_HIGH ) RuntimeInfoHook
(BELOW_NORMAL) LoggerHook
--------------------
before_train:
(VERY_HIGH ) RuntimeInfoHook
(NORMAL ) IterTimerHook
(VERY_LOW ) CheckpointHook
--------------------
before_train_epoch:
(VERY_HIGH ) RuntimeInfoHook
(NORMAL ) IterTimerHook
(NORMAL ) DistSamplerSeedHook
--------------------
before_train_iter:
(VERY_HIGH ) RuntimeInfoHook
(NORMAL ) IterTimerHook
--------------------
after_train_iter:
(VERY_HIGH ) RuntimeInfoHook
(NORMAL ) IterTimerHook
(BELOW_NORMAL) LoggerHook
(LOW ) ParamSchedulerHook
(VERY_LOW ) CheckpointHook
--------------------
after_train_epoch:
(NORMAL ) IterTimerHook
(LOW ) ParamSchedulerHook
(VERY_LOW ) CheckpointHook
--------------------
before_val_epoch:
(NORMAL ) IterTimerHook
--------------------
before_val_iter:
(NORMAL ) IterTimerHook
--------------------
after_val_iter:
(NORMAL ) IterTimerHook
(NORMAL ) DetVisualizationHook
(BELOW_NORMAL) LoggerHook
--------------------
after_val_epoch:
(VERY_HIGH ) RuntimeInfoHook
(NORMAL ) IterTimerHook
(BELOW_NORMAL) LoggerHook
(LOW ) ParamSchedulerHook
(VERY_LOW ) CheckpointHook
--------------------
after_train:
(VERY_LOW ) CheckpointHook
--------------------
before_test_epoch:
(NORMAL ) IterTimerHook
--------------------
before_test_iter:
(NORMAL ) IterTimerHook
--------------------
after_test_iter:
(NORMAL ) IterTimerHook
(NORMAL ) DetVisualizationHook
(BELOW_NORMAL) LoggerHook
--------------------
after_test_epoch:
(VERY_HIGH ) RuntimeInfoHook
(NORMAL ) IterTimerHook
(BELOW_NORMAL) LoggerHook
--------------------
after_run:
(BELOW_NORMAL) LoggerHook
--------------------
2023/07/13 08:59:48 - mmengine - INFO - load model from:
mmdetection/work_dirs/gfl_r18_fpn_finetune_vis_1x/epoch_12.pth
2023/07/13 08:59:48 - mmengine - INFO - Loads checkpoint by local backend from path: mmdetection/work_dirs/gfl_r18_fpn_finetune_vis_1x/epoch_12.pth
2023/07/13 08:59:49 - mmengine - WARNING - The model and loaded state dict do not match exactly
unexpected key in source state_dict: backbone.conv1.weight, backbone.bn1.weight, backbone.bn1.bias, backbone.bn1.running_mean, backbone.bn1.running_var, backbone.bn1.num_batches_tracked, backbone.layer1.0.conv1.weight, backbone.layer1.0.bn1.weight, backbone.layer1.0.bn1.bias, backbone.layer1.0.bn1.running_mean, backbone.layer1.0.bn1.running_var, backbone.layer1.0.bn1.num_batches_tracked, backbone.layer1.0.conv2.weight, backbone.layer1.0.bn2.weight, backbone.layer1.0.bn2.bias, backbone.layer1.0.bn2.running_mean, backbone.layer1.0.bn2.running_var, backbone.layer1.0.bn2.num_batches_tracked, backbone.layer1.1.conv1.weight, backbone.layer1.1.bn1.weight, backbone.layer1.1.bn1.bias, backbone.layer1.1.bn1.running_mean, backbone.layer1.1.bn1.running_var, backbone.layer1.1.bn1.num_batches_tracked, backbone.layer1.1.conv2.weight, backbone.layer1.1.bn2.weight, backbone.layer1.1.bn2.bias, backbone.layer1.1.bn2.running_mean, backbone.layer1.1.bn2.running_var, backbone.layer1.1.bn2.num_batches_tracked, backbone.layer2.0.conv1.weight, backbone.layer2.0.bn1.weight, backbone.layer2.0.bn1.bias, backbone.layer2.0.bn1.running_mean, backbone.layer2.0.bn1.running_var, backbone.layer2.0.bn1.num_batches_tracked, backbone.layer2.0.conv2.weight, backbone.layer2.0.bn2.weight, backbone.layer2.0.bn2.bias, backbone.layer2.0.bn2.running_mean, backbone.layer2.0.bn2.running_var, backbone.layer2.0.bn2.num_batches_tracked, backbone.layer2.0.downsample.0.weight, backbone.layer2.0.downsample.1.weight, backbone.layer2.0.downsample.1.bias, backbone.layer2.0.downsample.1.running_mean, backbone.layer2.0.downsample.1.running_var, backbone.layer2.0.downsample.1.num_batches_tracked, backbone.layer2.1.conv1.weight, backbone.layer2.1.bn1.weight, backbone.layer2.1.bn1.bias, backbone.layer2.1.bn1.running_mean, backbone.layer2.1.bn1.running_var, backbone.layer2.1.bn1.num_batches_tracked, backbone.layer2.1.conv2.weight, backbone.layer2.1.bn2.weight, backbone.layer2.1.bn2.bias, backbone.layer2.1.bn2.running_mean, backbone.layer2.1.bn2.running_var, backbone.layer2.1.bn2.num_batches_tracked, backbone.layer3.0.conv1.weight, backbone.layer3.0.bn1.weight, backbone.layer3.0.bn1.bias, backbone.layer3.0.bn1.running_mean, backbone.layer3.0.bn1.running_var, backbone.layer3.0.bn1.num_batches_tracked, backbone.layer3.0.conv2.weight, backbone.layer3.0.bn2.weight, backbone.layer3.0.bn2.bias, backbone.layer3.0.bn2.running_mean, backbone.layer3.0.bn2.running_var, backbone.layer3.0.bn2.num_batches_tracked, backbone.layer3.0.downsample.0.weight, backbone.layer3.0.downsample.1.weight, backbone.layer3.0.downsample.1.bias, backbone.layer3.0.downsample.1.running_mean, backbone.layer3.0.downsample.1.running_var, backbone.layer3.0.downsample.1.num_batches_tracked, backbone.layer3.1.conv1.weight, backbone.layer3.1.bn1.weight, backbone.layer3.1.bn1.bias, backbone.layer3.1.bn1.running_mean, backbone.layer3.1.bn1.running_var, backbone.layer3.1.bn1.num_batches_tracked, backbone.layer3.1.conv2.weight, backbone.layer3.1.bn2.weight, backbone.layer3.1.bn2.bias, backbone.layer3.1.bn2.running_mean, backbone.layer3.1.bn2.running_var, backbone.layer3.1.bn2.num_batches_tracked, backbone.layer4.0.conv1.weight, backbone.layer4.0.bn1.weight, backbone.layer4.0.bn1.bias, backbone.layer4.0.bn1.running_mean, backbone.layer4.0.bn1.running_var, backbone.layer4.0.bn1.num_batches_tracked, backbone.layer4.0.conv2.weight, backbone.layer4.0.bn2.weight, backbone.layer4.0.bn2.bias, backbone.layer4.0.bn2.running_mean, backbone.layer4.0.bn2.running_var, backbone.layer4.0.bn2.num_batches_tracked, backbone.layer4.0.downsample.0.weight, backbone.layer4.0.downsample.1.weight, backbone.layer4.0.downsample.1.bias, backbone.layer4.0.downsample.1.running_mean, backbone.layer4.0.downsample.1.running_var, backbone.layer4.0.downsample.1.num_batches_tracked, backbone.layer4.1.conv1.weight, backbone.layer4.1.bn1.weight, backbone.layer4.1.bn1.bias, backbone.layer4.1.bn1.running_mean, backbone.layer4.1.bn1.running_var, backbone.layer4.1.bn1.num_batches_tracked, backbone.layer4.1.conv2.weight, backbone.layer4.1.bn2.weight, backbone.layer4.1.bn2.bias, backbone.layer4.1.bn2.running_mean, backbone.layer4.1.bn2.running_var, backbone.layer4.1.bn2.num_batches_tracked, neck.lateral_convs.0.conv.weight, neck.lateral_convs.0.conv.bias, neck.lateral_convs.1.conv.weight, neck.lateral_convs.1.conv.bias, neck.lateral_convs.2.conv.weight, neck.lateral_convs.2.conv.bias, neck.fpn_convs.0.conv.weight, neck.fpn_convs.0.conv.bias, neck.fpn_convs.1.conv.weight, neck.fpn_convs.1.conv.bias, neck.fpn_convs.2.conv.weight, neck.fpn_convs.2.conv.bias, neck.fpn_convs.3.conv.weight, neck.fpn_convs.3.conv.bias, neck.fpn_convs.4.conv.weight, neck.fpn_convs.4.conv.bias, bbox_head.cls_convs.0.conv.weight, bbox_head.cls_convs.0.gn.weight, bbox_head.cls_convs.0.gn.bias, bbox_head.cls_convs.1.conv.weight, bbox_head.cls_convs.1.gn.weight, bbox_head.cls_convs.1.gn.bias, bbox_head.cls_convs.2.conv.weight, bbox_head.cls_convs.2.gn.weight, bbox_head.cls_convs.2.gn.bias, bbox_head.cls_convs.3.conv.weight, bbox_head.cls_convs.3.gn.weight, bbox_head.cls_convs.3.gn.bias, bbox_head.reg_convs.0.conv.weight, bbox_head.reg_convs.0.gn.weight, bbox_head.reg_convs.0.gn.bias, bbox_head.reg_convs.1.conv.weight, bbox_head.reg_convs.1.gn.weight, bbox_head.reg_convs.1.gn.bias, bbox_head.reg_convs.2.conv.weight, bbox_head.reg_convs.2.gn.weight, bbox_head.reg_convs.2.gn.bias, bbox_head.reg_convs.3.conv.weight, bbox_head.reg_convs.3.gn.weight, bbox_head.reg_convs.3.gn.bias, bbox_head.gfl_cls.weight, bbox_head.gfl_cls.bias, bbox_head.gfl_reg.weight, bbox_head.gfl_reg.bias, bbox_head.scales.0.scale, bbox_head.scales.1.scale, bbox_head.scales.2.scale, bbox_head.scales.3.scale, bbox_head.scales.4.scale, bbox_head.integral.project
missing keys in source state_dict: conv1.weight, bn1.weight, bn1.bias, bn1.running_mean, bn1.running_var, layer1.0.conv1.weight, layer1.0.bn1.weight, layer1.0.bn1.bias, layer1.0.bn1.running_mean, layer1.0.bn1.running_var, layer1.0.conv2.weight, layer1.0.bn2.weight, layer1.0.bn2.bias, layer1.0.bn2.running_mean, layer1.0.bn2.running_var, layer1.1.conv1.weight, layer1.1.bn1.weight, layer1.1.bn1.bias, layer1.1.bn1.running_mean, layer1.1.bn1.running_var, layer1.1.conv2.weight, layer1.1.bn2.weight, layer1.1.bn2.bias, layer1.1.bn2.running_mean, layer1.1.bn2.running_var, layer2.0.conv1.weight, layer2.0.bn1.weight, layer2.0.bn1.bias, layer2.0.bn1.running_mean, layer2.0.bn1.running_var, layer2.0.conv2.weight, layer2.0.bn2.weight, layer2.0.bn2.bias, layer2.0.bn2.running_mean, layer2.0.bn2.running_var, layer2.0.downsample.0.weight, layer2.0.downsample.1.weight, layer2.0.downsample.1.bias, layer2.0.downsample.1.running_mean, layer2.0.downsample.1.running_var, layer2.1.conv1.weight, layer2.1.bn1.weight, layer2.1.bn1.bias, layer2.1.bn1.running_mean, layer2.1.bn1.running_var, layer2.1.conv2.weight, layer2.1.bn2.weight, layer2.1.bn2.bias, layer2.1.bn2.running_mean, layer2.1.bn2.running_var, layer3.0.conv1.weight, layer3.0.bn1.weight, layer3.0.bn1.bias, layer3.0.bn1.running_mean, layer3.0.bn1.running_var, layer3.0.conv2.weight, layer3.0.bn2.weight, layer3.0.bn2.bias, layer3.0.bn2.running_mean, layer3.0.bn2.running_var, layer3.0.downsample.0.weight, layer3.0.downsample.1.weight, layer3.0.downsample.1.bias, layer3.0.downsample.1.running_mean, layer3.0.downsample.1.running_var, layer3.1.conv1.weight, layer3.1.bn1.weight, layer3.1.bn1.bias, layer3.1.bn1.running_mean, layer3.1.bn1.running_var, layer3.1.conv2.weight, layer3.1.bn2.weight, layer3.1.bn2.bias, layer3.1.bn2.running_mean, layer3.1.bn2.running_var, layer4.0.conv1.weight, layer4.0.bn1.weight, layer4.0.bn1.bias, layer4.0.bn1.running_mean, layer4.0.bn1.running_var, layer4.0.conv2.weight, layer4.0.bn2.weight, layer4.0.bn2.bias, layer4.0.bn2.running_mean, layer4.0.bn2.running_var, layer4.0.downsample.0.weight, layer4.0.downsample.1.weight, layer4.0.downsample.1.bias, layer4.0.downsample.1.running_mean, layer4.0.downsample.1.running_var, layer4.1.conv1.weight, layer4.1.bn1.weight, layer4.1.bn1.bias, layer4.1.bn1.running_mean, layer4.1.bn1.running_var, layer4.1.conv2.weight, layer4.1.bn2.weight, layer4.1.bn2.bias, layer4.1.bn2.running_mean, layer4.1.bn2.running_var
Name of parameter - Initialization information
backbone.conv1.weight - torch.Size([64, 3, 7, 7]):
The value is the same before and after calling `init_weights` of KnowledgeDistillationSingleStageDetector
backbone.bn1.weight - torch.Size([64]):
The value is the same before and after calling `init_weights` of KnowledgeDistillationSingleStageDetector
backbone.bn1.bias - torch.Size([64]):
The value is the same before and after calling `init_weights` of KnowledgeDistillationSingleStageDetector
backbone.layer1.0.conv1.weight - torch.Size([64, 64, 3, 3]):
The value is the same before and after calling `init_weights` of KnowledgeDistillationSingleStageDetector
backbone.layer1.0.bn1.weight - torch.Size([64]):
The value is the same before and after calling `init_weights` of KnowledgeDistillationSingleStageDetector
backbone.layer1.0.bn1.bias - torch.Size([64]):
The value is the same before and after calling `init_weights` of KnowledgeDistillationSingleStageDetector
backbone.layer1.0.conv2.weight - torch.Size([64, 64, 3, 3]):
The value is the same before and after calling `init_weights` of KnowledgeDistillationSingleStageDetector
backbone.layer1.0.bn2.weight - torch.Size([64]):
The value is the same before and after calling `init_weights` of KnowledgeDistillationSingleStageDetector
backbone.layer1.0.bn2.bias - torch.Size([64]):
The value is the same before and after calling `init_weights` of KnowledgeDistillationSingleStageDetector
backbone.layer1.1.conv1.weight - torch.Size([64, 64, 3, 3]):
The value is the same before and after calling `init_weights` of KnowledgeDistillationSingleStageDetector
backbone.layer1.1.bn1.weight - torch.Size([64]):
The value is the same before and after calling `init_weights` of KnowledgeDistillationSingleStageDetector
backbone.layer1.1.bn1.bias - torch.Size([64]):
The value is the same before and after calling `init_weights` of KnowledgeDistillationSingleStageDetector
backbone.layer1.1.conv2.weight - torch.Size([64, 64, 3, 3]):
The value is the same before and after calling `init_weights` of KnowledgeDistillationSingleStageDetector
backbone.layer1.1.bn2.weight - torch.Size([64]):
The value is the same before and after calling `init_weights` of KnowledgeDistillationSingleStageDetector
backbone.layer1.1.bn2.bias - torch.Size([64]):
The value is the same before and after calling `init_weights` of KnowledgeDistillationSingleStageDetector
backbone.layer2.0.conv1.weight - torch.Size([128, 64, 3, 3]):
The value is the same before and after calling `init_weights` of KnowledgeDistillationSingleStageDetector
backbone.layer2.0.bn1.weight - torch.Size([128]):
The value is the same before and after calling `init_weights` of KnowledgeDistillationSingleStageDetector
backbone.layer2.0.bn1.bias - torch.Size([128]):
The value is the same before and after calling `init_weights` of KnowledgeDistillationSingleStageDetector
backbone.layer2.0.conv2.weight - torch.Size([128, 128, 3, 3]):
The value is the same before and after calling `init_weights` of KnowledgeDistillationSingleStageDetector
backbone.layer2.0.bn2.weight - torch.Size([128]):
The value is the same before and after calling `init_weights` of KnowledgeDistillationSingleStageDetector
backbone.layer2.0.bn2.bias - torch.Size([128]):
The value is the same before and after calling `init_weights` of KnowledgeDistillationSingleStageDetector
backbone.layer2.0.downsample.0.weight - torch.Size([128, 64, 1, 1]):
The value is the same before and after calling `init_weights` of KnowledgeDistillationSingleStageDetector
backbone.layer2.0.downsample.1.weight - torch.Size([128]):
The value is the same before and after calling `init_weights` of KnowledgeDistillationSingleStageDetector
backbone.layer2.0.downsample.1.bias - torch.Size([128]):
The value is the same before and after calling `init_weights` of KnowledgeDistillationSingleStageDetector
backbone.layer2.1.conv1.weight - torch.Size([128, 128, 3, 3]):
The value is the same before and after calling `init_weights` of KnowledgeDistillationSingleStageDetector
backbone.layer2.1.bn1.weight - torch.Size([128]):
The value is the same before and after calling `init_weights` of KnowledgeDistillationSingleStageDetector
backbone.layer2.1.bn1.bias - torch.Size([128]):
The value is the same before and after calling `init_weights` of KnowledgeDistillationSingleStageDetector
backbone.layer2.1.conv2.weight - torch.Size([128, 128, 3, 3]):
The value is the same before and after calling `init_weights` of KnowledgeDistillationSingleStageDetector
backbone.layer2.1.bn2.weight - torch.Size([128]):
The value is the same before and after calling `init_weights` of KnowledgeDistillationSingleStageDetector
backbone.layer2.1.bn2.bias - torch.Size([128]):
The value is the same before and after calling `init_weights` of KnowledgeDistillationSingleStageDetector
backbone.layer3.0.conv1.weight - torch.Size([256, 128, 3, 3]):
The value is the same before and after calling `init_weights` of KnowledgeDistillationSingleStageDetector
backbone.layer3.0.bn1.weight - torch.Size([256]):
The value is the same before and after calling `init_weights` of KnowledgeDistillationSingleStageDetector
backbone.layer3.0.bn1.bias - torch.Size([256]):
The value is the same before and after calling `init_weights` of KnowledgeDistillationSingleStageDetector
backbone.layer3.0.conv2.weight - torch.Size([256, 256, 3, 3]):
The value is the same before and after calling `init_weights` of KnowledgeDistillationSingleStageDetector
backbone.layer3.0.bn2.weight - torch.Size([256]):
The value is the same before and after calling `init_weights` of KnowledgeDistillationSingleStageDetector
backbone.layer3.0.bn2.bias - torch.Size([256]):
The value is the same before and after calling `init_weights` of KnowledgeDistillationSingleStageDetector
backbone.layer3.0.downsample.0.weight - torch.Size([256, 128, 1, 1]):
The value is the same before and after calling `init_weights` of KnowledgeDistillationSingleStageDetector
backbone.layer3.0.downsample.1.weight - torch.Size([256]):
The value is the same before and after calling `init_weights` of KnowledgeDistillationSingleStageDetector
backbone.layer3.0.downsample.1.bias - torch.Size([256]):
The value is the same before and after calling `init_weights` of KnowledgeDistillationSingleStageDetector
backbone.layer3.1.conv1.weight - torch.Size([256, 256, 3, 3]):
The value is the same before and after calling `init_weights` of KnowledgeDistillationSingleStageDetector
backbone.layer3.1.bn1.weight - torch.Size([256]):
The value is the same before and after calling `init_weights` of KnowledgeDistillationSingleStageDetector
backbone.layer3.1.bn1.bias - torch.Size([256]):
The value is the same before and after calling `init_weights` of KnowledgeDistillationSingleStageDetector
backbone.layer3.1.conv2.weight - torch.Size([256, 256, 3, 3]):
The value is the same before and after calling `init_weights` of KnowledgeDistillationSingleStageDetector
backbone.layer3.1.bn2.weight - torch.Size([256]):
The value is the same before and after calling `init_weights` of KnowledgeDistillationSingleStageDetector
backbone.layer3.1.bn2.bias - torch.Size([256]):
The value is the same before and after calling `init_weights` of KnowledgeDistillationSingleStageDetector
backbone.layer4.0.conv1.weight - torch.Size([512, 256, 3, 3]):
The value is the same before and after calling `init_weights` of KnowledgeDistillationSingleStageDetector
backbone.layer4.0.bn1.weight - torch.Size([512]):
The value is the same before and after calling `init_weights` of KnowledgeDistillationSingleStageDetector
backbone.layer4.0.bn1.bias - torch.Size([512]):
The value is the same before and after calling `init_weights` of KnowledgeDistillationSingleStageDetector
backbone.layer4.0.conv2.weight - torch.Size([512, 512, 3, 3]):
The value is the same before and after calling `init_weights` of KnowledgeDistillationSingleStageDetector
backbone.layer4.0.bn2.weight - torch.Size([512]):
The value is the same before and after calling `init_weights` of KnowledgeDistillationSingleStageDetector
backbone.layer4.0.bn2.bias - torch.Size([512]):
The value is the same before and after calling `init_weights` of KnowledgeDistillationSingleStageDetector
backbone.layer4.0.downsample.0.weight - torch.Size([512, 256, 1, 1]):
The value is the same before and after calling `init_weights` of KnowledgeDistillationSingleStageDetector
backbone.layer4.0.downsample.1.weight - torch.Size([512]):
The value is the same before and after calling `init_weights` of KnowledgeDistillationSingleStageDetector
backbone.layer4.0.downsample.1.bias - torch.Size([512]):
The value is the same before and after calling `init_weights` of KnowledgeDistillationSingleStageDetector
backbone.layer4.1.conv1.weight - torch.Size([512, 512, 3, 3]):
The value is the same before and after calling `init_weights` of KnowledgeDistillationSingleStageDetector
backbone.layer4.1.bn1.weight - torch.Size([512]):
The value is the same before and after calling `init_weights` of KnowledgeDistillationSingleStageDetector
backbone.layer4.1.bn1.bias - torch.Size([512]):
The value is the same before and after calling `init_weights` of KnowledgeDistillationSingleStageDetector
backbone.layer4.1.conv2.weight - torch.Size([512, 512, 3, 3]):
The value is the same before and after calling `init_weights` of KnowledgeDistillationSingleStageDetector
backbone.layer4.1.bn2.weight - torch.Size([512]):
The value is the same before and after calling `init_weights` of KnowledgeDistillationSingleStageDetector
backbone.layer4.1.bn2.bias - torch.Size([512]):
The value is the same before and after calling `init_weights` of KnowledgeDistillationSingleStageDetector
neck.lateral_convs.0.conv.weight - torch.Size([256, 128, 1, 1]):
XavierInit: gain=1, distribution=uniform, bias=0
neck.lateral_convs.0.conv.bias - torch.Size([256]):
The value is the same before and after calling `init_weights` of KnowledgeDistillationSingleStageDetector
neck.lateral_convs.1.conv.weight - torch.Size([256, 256, 1, 1]):
XavierInit: gain=1, distribution=uniform, bias=0
neck.lateral_convs.1.conv.bias - torch.Size([256]):
The value is the same before and after calling `init_weights` of KnowledgeDistillationSingleStageDetector
neck.lateral_convs.2.conv.weight - torch.Size([256, 512, 1, 1]):
XavierInit: gain=1, distribution=uniform, bias=0
neck.lateral_convs.2.conv.bias - torch.Size([256]):
The value is the same before and after calling `init_weights` of KnowledgeDistillationSingleStageDetector
neck.fpn_convs.0.conv.weight - torch.Size([256, 256, 3, 3]):
XavierInit: gain=1, distribution=uniform, bias=0
neck.fpn_convs.0.conv.bias - torch.Size([256]):
The value is the same before and after calling `init_weights` of KnowledgeDistillationSingleStageDetector
neck.fpn_convs.1.conv.weight - torch.Size([256, 256, 3, 3]):
XavierInit: gain=1, distribution=uniform, bias=0
neck.fpn_convs.1.conv.bias - torch.Size([256]):
The value is the same before and after calling `init_weights` of KnowledgeDistillationSingleStageDetector
neck.fpn_convs.2.conv.weight - torch.Size([256, 256, 3, 3]):
XavierInit: gain=1, distribution=uniform, bias=0
neck.fpn_convs.2.conv.bias - torch.Size([256]):
The value is the same before and after calling `init_weights` of KnowledgeDistillationSingleStageDetector
neck.fpn_convs.3.conv.weight - torch.Size([256, 256, 3, 3]):
XavierInit: gain=1, distribution=uniform, bias=0
neck.fpn_convs.3.conv.bias - torch.Size([256]):
The value is the same before and after calling `init_weights` of KnowledgeDistillationSingleStageDetector
neck.fpn_convs.4.conv.weight - torch.Size([256, 256, 3, 3]):
XavierInit: gain=1, distribution=uniform, bias=0
neck.fpn_convs.4.conv.bias - torch.Size([256]):
The value is the same before and after calling `init_weights` of KnowledgeDistillationSingleStageDetector
bbox_head.cls_convs.0.conv.weight - torch.Size([256, 256, 3, 3]):
NormalInit: mean=0, std=0.01, bias=0
bbox_head.cls_convs.0.gn.weight - torch.Size([256]):
The value is the same before and after calling `init_weights` of KnowledgeDistillationSingleStageDetector
bbox_head.cls_convs.0.gn.bias - torch.Size([256]):
The value is the same before and after calling `init_weights` of KnowledgeDistillationSingleStageDetector
bbox_head.cls_convs.1.conv.weight - torch.Size([256, 256, 3, 3]):
NormalInit: mean=0, std=0.01, bias=0
bbox_head.cls_convs.1.gn.weight - torch.Size([256]):
The value is the same before and after calling `init_weights` of KnowledgeDistillationSingleStageDetector
bbox_head.cls_convs.1.gn.bias - torch.Size([256]):
The value is the same before and after calling `init_weights` of KnowledgeDistillationSingleStageDetector
bbox_head.cls_convs.2.conv.weight - torch.Size([256, 256, 3, 3]):
NormalInit: mean=0, std=0.01, bias=0
bbox_head.cls_convs.2.gn.weight - torch.Size([256]):
The value is the same before and after calling `init_weights` of KnowledgeDistillationSingleStageDetector
bbox_head.cls_convs.2.gn.bias - torch.Size([256]):
The value is the same before and after calling `init_weights` of KnowledgeDistillationSingleStageDetector
bbox_head.cls_convs.3.conv.weight - torch.Size([256, 256, 3, 3]):
NormalInit: mean=0, std=0.01, bias=0
bbox_head.cls_convs.3.gn.weight - torch.Size([256]):
The value is the same before and after calling `init_weights` of KnowledgeDistillationSingleStageDetector
bbox_head.cls_convs.3.gn.bias - torch.Size([256]):
The value is the same before and after calling `init_weights` of KnowledgeDistillationSingleStageDetector
bbox_head.reg_convs.0.conv.weight - torch.Size([256, 256, 3, 3]):
NormalInit: mean=0, std=0.01, bias=0
bbox_head.reg_convs.0.gn.weight - torch.Size([256]):
The value is the same before and after calling `init_weights` of KnowledgeDistillationSingleStageDetector
bbox_head.reg_convs.0.gn.bias - torch.Size([256]):
The value is the same before and after calling `init_weights` of KnowledgeDistillationSingleStageDetector
bbox_head.reg_convs.1.conv.weight - torch.Size([256, 256, 3, 3]):
NormalInit: mean=0, std=0.01, bias=0
bbox_head.reg_convs.1.gn.weight - torch.Size([256]):
The value is the same before and after calling `init_weights` of KnowledgeDistillationSingleStageDetector
bbox_head.reg_convs.1.gn.bias - torch.Size([256]):
The value is the same before and after calling `init_weights` of KnowledgeDistillationSingleStageDetector
bbox_head.reg_convs.2.conv.weight - torch.Size([256, 256, 3, 3]):
NormalInit: mean=0, std=0.01, bias=0
bbox_head.reg_convs.2.gn.weight - torch.Size([256]):
The value is the same before and after calling `init_weights` of KnowledgeDistillationSingleStageDetector
bbox_head.reg_convs.2.gn.bias - torch.Size([256]):
The value is the same before and after calling `init_weights` of KnowledgeDistillationSingleStageDetector
bbox_head.reg_convs.3.conv.weight - torch.Size([256, 256, 3, 3]):
NormalInit: mean=0, std=0.01, bias=0
bbox_head.reg_convs.3.gn.weight - torch.Size([256]):
The value is the same before and after calling `init_weights` of KnowledgeDistillationSingleStageDetector
bbox_head.reg_convs.3.gn.bias - torch.Size([256]):
The value is the same before and after calling `init_weights` of KnowledgeDistillationSingleStageDetector
bbox_head.gfl_cls.weight - torch.Size([10, 256, 3, 3]):
NormalInit: mean=0, std=0.01, bias=-4.59511985013459
bbox_head.gfl_cls.bias - torch.Size([10]):
NormalInit: mean=0, std=0.01, bias=-4.59511985013459
bbox_head.gfl_reg.weight - torch.Size([68, 256, 3, 3]):
NormalInit: mean=0, std=0.01, bias=0
bbox_head.gfl_reg.bias - torch.Size([68]):
NormalInit: mean=0, std=0.01, bias=0
bbox_head.scales.0.scale - torch.Size([]):
The value is the same before and after calling `init_weights` of KnowledgeDistillationSingleStageDetector
bbox_head.scales.1.scale - torch.Size([]):
The value is the same before and after calling `init_weights` of KnowledgeDistillationSingleStageDetector
bbox_head.scales.2.scale - torch.Size([]):
The value is the same before and after calling `init_weights` of KnowledgeDistillationSingleStageDetector
bbox_head.scales.3.scale - torch.Size([]):
The value is the same before and after calling `init_weights` of KnowledgeDistillationSingleStageDetector
bbox_head.scales.4.scale - torch.Size([]):
The value is the same before and after calling `init_weights` of KnowledgeDistillationSingleStageDetector
2023/07/13 08:59:49 - mmengine - WARNING - "FileClient" will be deprecated in future. Please use io functions in https://mmengine.readthedocs.io/en/latest/api/fileio.html#file-io
2023/07/13 08:59:49 - mmengine - WARNING - "HardDiskBackend" is the alias of "LocalBackend" and the former will be deprecated in future.
2023/07/13 08:59:49 - mmengine - INFO - Checkpoints will be saved to mmdetection/work_dir/ld_gfl_r18_r101_fpn_1x_vis.
2023/07/13 09:00:12 - mmengine - INFO - Epoch(train) [1][ 50/3139] lr: 1.2387e-04 eta: 4:51:04 time: 0.4643 data_time: 0.0102 memory: 726 loss: 2.8593 loss_cls: 0.0497 loss_bbox: 1.7562 loss_dfl: 0.6355 loss_ld: 0.4180
2023/07/13 09:00:28 - mmengine - INFO - Epoch(train) [1][ 100/3139] lr: 2.4900e-04 eta: 4:06:07 time: 0.3219 data_time: 0.0032 memory: 735 loss: 2.8364 loss_cls: 0.1487 loss_bbox: 1.4559 loss_dfl: 0.4698 loss_ld: 0.7620
2023/07/13 09:00:45 - mmengine - INFO - Epoch(train) [1][ 150/3139] lr: 3.7412e-04 eta: 3:51:05 time: 0.3225 data_time: 0.0032 memory: 728 loss: 2.8286 loss_cls: 0.1581 loss_bbox: 1.3511 loss_dfl: 0.4469 loss_ld: 0.8726
2023/07/13 09:01:01 - mmengine - INFO - Epoch(train) [1][ 200/3139] lr: 4.9925e-04 eta: 3:43:30 time: 0.3230 data_time: 0.0033 memory: 726 loss: 2.8752 loss_cls: 0.1716 loss_bbox: 1.3117 loss_dfl: 0.4379 loss_ld: 0.9539
2023/07/13 09:01:17 - mmengine - INFO - Epoch(train) [1][ 250/3139] lr: 6.2437e-04 eta: 3:38:54 time: 0.3234 data_time: 0.0034 memory: 734 loss: 2.7505 loss_cls: 0.2777 loss_bbox: 1.2401 loss_dfl: 0.4148 loss_ld: 0.8179
2023/07/13 09:01:33 - mmengine - INFO - Epoch(train) [1][ 300/3139] lr: 7.4950e-04 eta: 3:35:54 time: 0.3249 data_time: 0.0043 memory: 762 loss: 2.6779 loss_cls: 0.2078 loss_bbox: 1.2246 loss_dfl: 0.3988 loss_ld: 0.8467
2023/07/13 09:01:49 - mmengine - INFO - Epoch(train) [1][ 350/3139] lr: 8.7462e-04 eta: 3:33:27 time: 0.3224 data_time: 0.0035 memory: 727 loss: 2.7521 loss_cls: 0.2863 loss_bbox: 1.1349 loss_dfl: 0.4004 loss_ld: 0.9305
2023/07/13 09:02:05 - mmengine - INFO - Epoch(train) [1][ 400/3139] lr: 9.9975e-04 eta: 3:31:12 time: 0.3180 data_time: 0.0032 memory: 718 loss: 2.5842 loss_cls: 0.2757 loss_bbox: 1.1461 loss_dfl: 0.4002 loss_ld: 0.7622
2023/07/13 09:02:21 - mmengine - INFO - Epoch(train) [1][ 450/3139] lr: 1.1249e-03 eta: 3:29:55 time: 0.3255 data_time: 0.0044 memory: 715 loss: 2.7192 loss_cls: 0.2496 loss_bbox: 1.0995 loss_dfl: 0.3936 loss_ld: 0.9766
2023/07/13 09:02:37 - mmengine - INFO - Epoch(train) [1][ 500/3139] lr: 1.2500e-03 eta: 3:28:31 time: 0.3203 data_time: 0.0032 memory: 725 loss: 2.6099 loss_cls: 0.2229 loss_bbox: 1.1500 loss_dfl: 0.3945 loss_ld: 0.8425
2023/07/13 09:02:54 - mmengine - INFO - Epoch(train) [1][ 550/3139] lr: 1.2500e-03 eta: 3:27:30 time: 0.3237 data_time: 0.0036 memory: 731 loss: 2.4420 loss_cls: 0.4721 loss_bbox: 1.1231 loss_dfl: 0.3913 loss_ld: 0.4554
2023/07/13 09:03:10 - mmengine - INFO - Epoch(train) [1][ 600/3139] lr: 1.2500e-03 eta: 3:26:43 time: 0.3257 data_time: 0.0037 memory: 752 loss: 2.5645 loss_cls: 0.3175 loss_bbox: 1.1156 loss_dfl: 0.3883 loss_ld: 0.7430
2023/07/13 09:03:26 - mmengine - INFO - Epoch(train) [1][ 650/3139] lr: 1.2500e-03 eta: 3:25:55 time: 0.3236 data_time: 0.0040 memory: 717 loss: 2.4554 loss_cls: 0.3810 loss_bbox: 1.0707 loss_dfl: 0.3833 loss_ld: 0.6203
2023/07/13 09:03:42 - mmengine - INFO - Epoch(train) [1][ 700/3139] lr: 1.2500e-03 eta: 3:25:15 time: 0.3247 data_time: 0.0036 memory: 726 loss: 2.5690 loss_cls: 0.2448 loss_bbox: 1.0416 loss_dfl: 0.3644 loss_ld: 0.9182
2023/07/13 09:03:58 - mmengine - INFO - Epoch(train) [1][ 750/3139] lr: 1.2500e-03 eta: 3:24:32 time: 0.3224 data_time: 0.0038 memory: 725 loss: 2.3903 loss_cls: 0.2681 loss_bbox: 1.0477 loss_dfl: 0.3556 loss_ld: 0.7188
2023/07/13 09:04:15 - mmengine - INFO - Epoch(train) [1][ 800/3139] lr: 1.2500e-03 eta: 3:23:53 time: 0.3230 data_time: 0.0036 memory: 728 loss: 2.6587 loss_cls: 0.2732 loss_bbox: 1.0669 loss_dfl: 0.3701 loss_ld: 0.9483
2023/07/13 09:04:31 - mmengine - INFO - Epoch(train) [1][ 850/3139] lr: 1.2500e-03 eta: 3:23:25 time: 0.3265 data_time: 0.0047 memory: 746 loss: 2.5394 loss_cls: 0.2578 loss_bbox: 1.0237 loss_dfl: 0.3494 loss_ld: 0.9085
2023/07/13 09:04:47 - mmengine - INFO - Epoch(train) [1][ 900/3139] lr: 1.2500e-03 eta: 3:22:56 time: 0.3253 data_time: 0.0037 memory: 728 loss: 2.4551 loss_cls: 0.2998 loss_bbox: 1.0223 loss_dfl: 0.3577 loss_ld: 0.7753
2023/07/13 09:05:03 - mmengine - INFO - Epoch(train) [1][ 950/3139] lr: 1.2500e-03 eta: 3:22:27 time: 0.3247 data_time: 0.0037 memory: 721 loss: 2.5495 loss_cls: 0.2648 loss_bbox: 1.0234 loss_dfl: 0.3582 loss_ld: 0.9032
2023/07/13 09:05:19 - mmengine - INFO - Exp name: ld_gfl_r18_r101_fpn_1x_vis_20230713_085933
2023/07/13 09:05:19 - mmengine - INFO - Epoch(train) [1][1000/3139] lr: 1.2500e-03 eta: 3:21:47 time: 0.3180 data_time: 0.0035 memory: 736 loss: 2.4022 loss_cls: 0.2666 loss_bbox: 1.0429 loss_dfl: 0.3513 loss_ld: 0.7414
2023/07/13 09:05:35 - mmengine - INFO - Epoch(train) [1][1050/3139] lr: 1.2500e-03 eta: 3:21:16 time: 0.3220 data_time: 0.0043 memory: 723 loss: 2.2707 loss_cls: 0.5327 loss_bbox: 0.9849 loss_dfl: 0.3297 loss_ld: 0.4234
2023/07/13 09:05:52 - mmengine - INFO - Epoch(train) [1][1100/3139] lr: 1.2500e-03 eta: 3:20:49 time: 0.3235 data_time: 0.0038 memory: 716 loss: 2.3549 loss_cls: 0.2943 loss_bbox: 1.0201 loss_dfl: 0.3505 loss_ld: 0.6900
2023/07/13 09:06:08 - mmengine - INFO - Epoch(train) [1][1150/3139] lr: 1.2500e-03 eta: 3:20:22 time: 0.3228 data_time: 0.0039 memory: 718 loss: 2.2110 loss_cls: 0.3172 loss_bbox: 0.9607 loss_dfl: 0.3248 loss_ld: 0.6083
2023/07/13 09:06:24 - mmengine - INFO - Epoch(train) [1][1200/3139] lr: 1.2500e-03 eta: 3:19:52 time: 0.3199 data_time: 0.0033 memory: 719 loss: 2.5678 loss_cls: 0.2996 loss_bbox: 0.9581 loss_dfl: 0.3483 loss_ld: 0.9618
2023/07/13 09:06:40 - mmengine - INFO - Epoch(train) [1][1250/3139] lr: 1.2500e-03 eta: 3:19:27 time: 0.3232 data_time: 0.0036 memory: 726 loss: 2.2851 loss_cls: 0.2567 loss_bbox: 1.0031 loss_dfl: 0.3344 loss_ld: 0.6910
2023/07/13 09:06:56 - mmengine - INFO - Epoch(train) [1][1300/3139] lr: 1.2500e-03 eta: 3:19:02 time: 0.3226 data_time: 0.0035 memory: 714 loss: 2.4766 loss_cls: 0.2856 loss_bbox: 0.9674 loss_dfl: 0.3455 loss_ld: 0.8780
2023/07/13 09:07:12 - mmengine - INFO - Epoch(train) [1][1350/3139] lr: 1.2500e-03 eta: 3:18:40 time: 0.3239 data_time: 0.0039 memory: 728 loss: 2.5335 loss_cls: 0.3209 loss_bbox: 0.9661 loss_dfl: 0.3481 loss_ld: 0.8984
2023/07/13 09:07:28 - mmengine - INFO - Epoch(train) [1][1400/3139] lr: 1.2500e-03 eta: 3:18:16 time: 0.3224 data_time: 0.0035 memory: 734 loss: 2.3318 loss_cls: 0.3507 loss_bbox: 0.9512 loss_dfl: 0.3247 loss_ld: 0.7052
2023/07/13 09:07:44 - mmengine - INFO - Epoch(train) [1][1450/3139] lr: 1.2500e-03 eta: 3:17:53 time: 0.3227 data_time: 0.0041 memory: 740 loss: 2.4525 loss_cls: 0.2690 loss_bbox: 0.9553 loss_dfl: 0.3351 loss_ld: 0.8930
2023/07/13 09:08:01 - mmengine - INFO - Epoch(train) [1][1500/3139] lr: 1.2500e-03 eta: 3:17:30 time: 0.3224 data_time: 0.0043 memory: 731 loss: 2.3288 loss_cls: 0.2671 loss_bbox: 0.9753 loss_dfl: 0.3258 loss_ld: 0.7606
2023/07/13 09:08:17 - mmengine - INFO - Epoch(train) [1][1550/3139] lr: 1.2500e-03 eta: 3:17:12 time: 0.3267 data_time: 0.0040 memory: 730 loss: 2.3856 loss_cls: 0.2628 loss_bbox: 0.8593 loss_dfl: 0.3091 loss_ld: 0.9545
2023/07/13 09:08:33 - mmengine - INFO - Epoch(train) [1][1600/3139] lr: 1.2500e-03 eta: 3:16:53 time: 0.3248 data_time: 0.0042 memory: 731 loss: 2.2865 loss_cls: 0.3131 loss_bbox: 0.8571 loss_dfl: 0.3039 loss_ld: 0.8124
2023/07/13 09:08:49 - mmengine - INFO - Epoch(train) [1][1650/3139] lr: 1.2500e-03 eta: 3:16:32 time: 0.3233 data_time: 0.0039 memory: 727 loss: 2.2641 loss_cls: 0.3056 loss_bbox: 0.9180 loss_dfl: 0.3223 loss_ld: 0.7182
2023/07/13 09:09:05 - mmengine - INFO - Epoch(train) [1][1700/3139] lr: 1.2500e-03 eta: 3:16:10 time: 0.3223 data_time: 0.0035 memory: 721 loss: 2.3271 loss_cls: 0.2757 loss_bbox: 0.9004 loss_dfl: 0.3115 loss_ld: 0.8394
2023/07/13 09:09:22 - mmengine - INFO - Epoch(train) [1][1750/3139] lr: 1.2500e-03 eta: 3:15:49 time: 0.3225 data_time: 0.0037 memory: 722 loss: 2.1586 loss_cls: 0.3351 loss_bbox: 0.9035 loss_dfl: 0.3016 loss_ld: 0.6184
2023/07/13 09:09:38 - mmengine - INFO - Epoch(train) [1][1800/3139] lr: 1.2500e-03 eta: 3:15:32 time: 0.3271 data_time: 0.0040 memory: 730 loss: 2.2740 loss_cls: 0.2776 loss_bbox: 0.9105 loss_dfl: 0.3151 loss_ld: 0.7707
2023/07/13 09:09:54 - mmengine - INFO - Epoch(train) [1][1850/3139] lr: 1.2500e-03 eta: 3:15:11 time: 0.3215 data_time: 0.0037 memory: 749 loss: 2.2827 loss_cls: 0.2938 loss_bbox: 0.8489 loss_dfl: 0.3241 loss_ld: 0.8158
2023/07/13 09:10:10 - mmengine - INFO - Epoch(train) [1][1900/3139] lr: 1.2500e-03 eta: 3:14:51 time: 0.3233 data_time: 0.0040 memory: 721 loss: 2.2303 loss_cls: 0.2871 loss_bbox: 0.9360 loss_dfl: 0.3187 loss_ld: 0.6886
2023/07/13 09:10:26 - mmengine - INFO - Epoch(train) [1][1950/3139] lr: 1.2500e-03 eta: 3:14:28 time: 0.3201 data_time: 0.0035 memory: 722 loss: 2.1642 loss_cls: 0.2894 loss_bbox: 0.8575 loss_dfl: 0.2940 loss_ld: 0.7232
2023/07/13 09:10:42 - mmengine - INFO - Exp name: ld_gfl_r18_r101_fpn_1x_vis_20230713_085933
2023/07/13 09:10:42 - mmengine - INFO - Epoch(train) [1][2000/3139] lr: 1.2500e-03 eta: 3:14:11 time: 0.3252 data_time: 0.0033 memory: 723 loss: 2.2237 loss_cls: 0.3377 loss_bbox: 0.8221 loss_dfl: 0.3112 loss_ld: 0.7527
2023/07/13 09:10:59 - mmengine - INFO - Epoch(train) [1][2050/3139] lr: 1.2500e-03 eta: 3:13:52 time: 0.3242 data_time: 0.0040 memory: 716 loss: 2.1239 loss_cls: 0.2976 loss_bbox: 0.8956 loss_dfl: 0.3111 loss_ld: 0.6196
2023/07/13 09:11:15 - mmengine - INFO - Epoch(train) [1][2100/3139] lr: 1.2500e-03 eta: 3:13:33 time: 0.3232 data_time: 0.0038 memory: 713 loss: 2.2035 loss_cls: 0.2751 loss_bbox: 0.8657 loss_dfl: 0.3090 loss_ld: 0.7537
2023/07/13 09:11:31 - mmengine - INFO - Epoch(train) [1][2150/3139] lr: 1.2500e-03 eta: 3:13:14 time: 0.3235 data_time: 0.0035 memory: 732 loss: 2.3374 loss_cls: 0.2631 loss_bbox: 0.8443 loss_dfl: 0.3060 loss_ld: 0.9240
2023/07/13 09:11:47 - mmengine - INFO - Epoch(train) [1][2200/3139] lr: 1.2500e-03 eta: 3:12:55 time: 0.3224 data_time: 0.0036 memory: 722 loss: 2.1256 loss_cls: 0.2977 loss_bbox: 0.8422 loss_dfl: 0.2959 loss_ld: 0.6898
2023/07/13 09:12:03 - mmengine - INFO - Epoch(train) [1][2250/3139] lr: 1.2500e-03 eta: 3:12:34 time: 0.3216 data_time: 0.0037 memory: 718 loss: 2.1131 loss_cls: 0.2979 loss_bbox: 0.8392 loss_dfl: 0.2979 loss_ld: 0.6782
2023/07/13 09:12:19 - mmengine - INFO - Epoch(train) [1][2300/3139] lr: 1.2500e-03 eta: 3:12:15 time: 0.3218 data_time: 0.0037 memory: 723 loss: 2.2634 loss_cls: 0.2936 loss_bbox: 0.8086 loss_dfl: 0.3019 loss_ld: 0.8593
2023/07/13 09:12:35 - mmengine - INFO - Epoch(train) [1][2350/3139] lr: 1.2500e-03 eta: 3:11:55 time: 0.3214 data_time: 0.0037 memory: 720 loss: 2.1510 loss_cls: 0.3258 loss_bbox: 0.8311 loss_dfl: 0.2962 loss_ld: 0.6979
2023/07/13 09:12:51 - mmengine - INFO - Epoch(train) [1][2400/3139] lr: 1.2500e-03 eta: 3:11:35 time: 0.3206 data_time: 0.0042 memory: 730 loss: 2.3122 loss_cls: 0.7657 loss_bbox: 0.8856 loss_dfl: 0.3006 loss_ld: 0.3602
2023/07/13 09:13:08 - mmengine - INFO - Epoch(train) [1][2450/3139] lr: 1.2500e-03 eta: 3:11:16 time: 0.3226 data_time: 0.0038 memory: 735 loss: 2.1597 loss_cls: 0.3952 loss_bbox: 0.8340 loss_dfl: 0.3073 loss_ld: 0.6231
2023/07/13 09:13:24 - mmengine - INFO - Epoch(train) [1][2500/3139] lr: 1.2500e-03 eta: 3:10:56 time: 0.3206 data_time: 0.0035 memory: 722 loss: 2.1330 loss_cls: 0.3067 loss_bbox: 0.8367 loss_dfl: 0.2961 loss_ld: 0.6935
2023/07/13 09:13:40 - mmengine - INFO - Epoch(train) [1][2550/3139] lr: 1.2500e-03 eta: 3:10:36 time: 0.3202 data_time: 0.0037 memory: 723 loss: 2.0319 loss_cls: 0.4225 loss_bbox: 0.8425 loss_dfl: 0.2964 loss_ld: 0.4705
2023/07/13 09:13:56 - mmengine - INFO - Epoch(train) [1][2600/3139] lr: 1.2500e-03 eta: 3:10:17 time: 0.3223 data_time: 0.0045 memory: 738 loss: 2.1100 loss_cls: 0.3306 loss_bbox: 0.8441 loss_dfl: 0.2945 loss_ld: 0.6407
2023/07/13 09:14:12 - mmengine - INFO - Epoch(train) [1][2650/3139] lr: 1.2500e-03 eta: 3:09:59 time: 0.3222 data_time: 0.0033 memory: 728 loss: 2.0686 loss_cls: 0.3202 loss_bbox: 0.8319 loss_dfl: 0.2910 loss_ld: 0.6254
2023/07/13 09:14:28 - mmengine - INFO - Epoch(train) [1][2700/3139] lr: 1.2500e-03 eta: 3:09:41 time: 0.3241 data_time: 0.0034 memory: 737 loss: 2.1032 loss_cls: 0.4505 loss_bbox: 0.8201 loss_dfl: 0.2842 loss_ld: 0.5483
2023/07/13 09:14:44 - mmengine - INFO - Epoch(train) [1][2750/3139] lr: 1.2500e-03 eta: 3:09:24 time: 0.3232 data_time: 0.0039 memory: 719 loss: 2.0519 loss_cls: 0.3435 loss_bbox: 0.8369 loss_dfl: 0.2891 loss_ld: 0.5824
2023/07/13 09:15:00 - mmengine - INFO - Epoch(train) [1][2800/3139] lr: 1.2500e-03 eta: 3:09:06 time: 0.3233 data_time: 0.0042 memory: 738 loss: 1.9789 loss_cls: 0.3233 loss_bbox: 0.7799 loss_dfl: 0.2734 loss_ld: 0.6024
2023/07/13 09:15:17 - mmengine - INFO - Epoch(train) [1][2850/3139] lr: 1.2500e-03 eta: 3:08:49 time: 0.3249 data_time: 0.0038 memory: 731 loss: 2.0823 loss_cls: 0.3069 loss_bbox: 0.8366 loss_dfl: 0.2891 loss_ld: 0.6497
2023/07/13 09:15:33 - mmengine - INFO - Epoch(train) [1][2900/3139] lr: 1.2500e-03 eta: 3:08:32 time: 0.3230 data_time: 0.0033 memory: 716 loss: 2.1838 loss_cls: 0.3102 loss_bbox: 0.8087 loss_dfl: 0.2888 loss_ld: 0.7760
2023/07/13 09:15:49 - mmengine - INFO - Epoch(train) [1][2950/3139] lr: 1.2500e-03 eta: 3:08:15 time: 0.3251 data_time: 0.0038 memory: 737 loss: 2.1450 loss_cls: 0.3113 loss_bbox: 0.8374 loss_dfl: 0.2961 loss_ld: 0.7001
2023/07/13 09:16:05 - mmengine - INFO - Exp name: ld_gfl_r18_r101_fpn_1x_vis_20230713_085933
2023/07/13 09:16:05 - mmengine - INFO - Epoch(train) [1][3000/3139] lr: 1.2500e-03 eta: 3:07:58 time: 0.3233 data_time: 0.0040 memory: 729 loss: 2.0610 loss_cls: 0.3165 loss_bbox: 0.8524 loss_dfl: 0.2961 loss_ld: 0.5960
2023/07/13 09:16:21 - mmengine - INFO - Epoch(train) [1][3050/3139] lr: 1.2500e-03 eta: 3:07:41 time: 0.3246 data_time: 0.0038 memory: 718 loss: 2.0545 loss_cls: 0.3105 loss_bbox: 0.7869 loss_dfl: 0.2838 loss_ld: 0.6733
2023/07/13 09:16:38 - mmengine - INFO - Epoch(train) [1][3100/3139] lr: 1.2500e-03 eta: 3:07:24 time: 0.3236 data_time: 0.0035 memory: 719 loss: 2.1269 loss_cls: 0.3013 loss_bbox: 0.8114 loss_dfl: 0.2933 loss_ld: 0.7210
2023/07/13 09:16:50 - mmengine - INFO - Exp name: ld_gfl_r18_r101_fpn_1x_vis_20230713_085933
2023/07/13 09:16:50 - mmengine - INFO - Saving checkpoint at 1 epochs
2023/07/13 09:16:57 - mmengine - INFO - Epoch(val) [1][ 50/548] eta: 0:00:43 time: 0.0875 data_time: 0.0058 memory: 725
2023/07/13 09:17:01 - mmengine - INFO - Epoch(val) [1][100/548] eta: 0:00:37 time: 0.0814 data_time: 0.0017 memory: 497
2023/07/13 09:17:05 - mmengine - INFO - Epoch(val) [1][150/548] eta: 0:00:33 time: 0.0811 data_time: 0.0015 memory: 497
2023/07/13 09:17:09 - mmengine - INFO - Epoch(val) [1][200/548] eta: 0:00:28 time: 0.0804 data_time: 0.0015 memory: 497
2023/07/13 09:17:13 - mmengine - INFO - Epoch(val) [1][250/548] eta: 0:00:24 time: 0.0808 data_time: 0.0015 memory: 497
2023/07/13 09:17:17 - mmengine - INFO - Epoch(val) [1][300/548] eta: 0:00:20 time: 0.0804 data_time: 0.0015 memory: 497
2023/07/13 09:17:21 - mmengine - INFO - Epoch(val) [1][350/548] eta: 0:00:16 time: 0.0799 data_time: 0.0015 memory: 497
2023/07/13 09:17:25 - mmengine - INFO - Epoch(val) [1][400/548] eta: 0:00:12 time: 0.0801 data_time: 0.0014 memory: 497
2023/07/13 09:17:29 - mmengine - INFO - Epoch(val) [1][450/548] eta: 0:00:07 time: 0.0776 data_time: 0.0014 memory: 497
2023/07/13 09:17:33 - mmengine - INFO - Epoch(val) [1][500/548] eta: 0:00:03 time: 0.0789 data_time: 0.0015 memory: 497
2023/07/13 09:17:37 - mmengine - INFO - Evaluating bbox...
2023/07/13 09:17:54 - mmengine - INFO - bbox_mAP_copypaste: 0.031 0.067 0.024 0.007 0.048 0.097
2023/07/13 09:17:54 - mmengine - INFO - Epoch(val) [1][548/548] coco/bbox_mAP: 0.0310 coco/bbox_mAP_50: 0.0670 coco/bbox_mAP_75: 0.0240 coco/bbox_mAP_s: 0.0070 coco/bbox_mAP_m: 0.0480 coco/bbox_mAP_l: 0.0970 data_time: 0.0019 time: 0.0802
2023/07/13 09:18:10 - mmengine - INFO - Epoch(train) [2][ 50/3139] lr: 1.2500e-03 eta: 3:06:55 time: 0.3250 data_time: 0.0054 memory: 718 loss: 2.0558 loss_cls: 0.3033 loss_bbox: 0.8179 loss_dfl: 0.2912 loss_ld: 0.6434
2023/07/13 09:18:26 - mmengine - INFO - Epoch(train) [2][ 100/3139] lr: 1.2500e-03 eta: 3:06:38 time: 0.3239 data_time: 0.0039 memory: 726 loss: 2.0286 loss_cls: 0.3108 loss_bbox: 0.7554 loss_dfl: 0.2671 loss_ld: 0.6952
2023/07/13 09:18:42 - mmengine - INFO - Epoch(train) [2][ 150/3139] lr: 1.2500e-03 eta: 3:06:21 time: 0.3244 data_time: 0.0035 memory: 761 loss: 1.9695 loss_cls: 0.3787 loss_bbox: 0.8066 loss_dfl: 0.2699 loss_ld: 0.5144
2023/07/13 09:18:59 - mmengine - INFO - Epoch(train) [2][ 200/3139] lr: 1.2500e-03 eta: 3:06:04 time: 0.3236 data_time: 0.0033 memory: 725 loss: 2.0286 loss_cls: 0.3670 loss_bbox: 0.8487 loss_dfl: 0.2991 loss_ld: 0.5138
2023/07/13 09:19:15 - mmengine - INFO - Epoch(train) [2][ 250/3139] lr: 1.2500e-03 eta: 3:05:48 time: 0.3242 data_time: 0.0043 memory: 722 loss: 2.0063 loss_cls: 0.3011 loss_bbox: 0.7753 loss_dfl: 0.2722 loss_ld: 0.6577
2023/07/13 09:19:31 - mmengine - INFO - Epoch(train) [2][ 300/3139] lr: 1.2500e-03 eta: 3:05:29 time: 0.3202 data_time: 0.0034 memory: 729 loss: 2.0693 loss_cls: 0.3063 loss_bbox: 0.7949 loss_dfl: 0.2754 loss_ld: 0.6926
2023/07/13 09:19:47 - mmengine - INFO - Epoch(train) [2][ 350/3139] lr: 1.2500e-03 eta: 3:05:12 time: 0.3246 data_time: 0.0038 memory: 716 loss: 1.9857 loss_cls: 0.3404 loss_bbox: 0.7884 loss_dfl: 0.2801 loss_ld: 0.5768
2023/07/13 09:20:03 - mmengine - INFO - Epoch(train) [2][ 400/3139] lr: 1.2500e-03 eta: 3:04:54 time: 0.3203 data_time: 0.0036 memory: 748 loss: 1.9519 loss_cls: 0.3383 loss_bbox: 0.7646 loss_dfl: 0.2705 loss_ld: 0.5785
2023/07/13 09:20:19 - mmengine - INFO - Epoch(train) [2][ 450/3139] lr: 1.2500e-03 eta: 3:04:37 time: 0.3246 data_time: 0.0038 memory: 728 loss: 2.0275 loss_cls: 0.3325 loss_bbox: 0.7809 loss_dfl: 0.2818 loss_ld: 0.6323
2023/07/13 09:20:36 - mmengine - INFO - Epoch(train) [2][ 500/3139] lr: 1.2500e-03 eta: 3:04:22 time: 0.3274 data_time: 0.0040 memory: 722 loss: 2.1411 loss_cls: 0.3269 loss_bbox: 0.7443 loss_dfl: 0.2810 loss_ld: 0.7889
2023/07/13 09:20:52 - mmengine - INFO - Epoch(train) [2][ 550/3139] lr: 1.2500e-03 eta: 3:04:05 time: 0.3225 data_time: 0.0036 memory: 725 loss: 1.9630 loss_cls: 0.3111 loss_bbox: 0.7642 loss_dfl: 0.2778 loss_ld: 0.6100
2023/07/13 09:21:08 - mmengine - INFO - Epoch(train) [2][ 600/3139] lr: 1.2500e-03 eta: 3:03:47 time: 0.3225 data_time: 0.0034 memory: 730 loss: 2.0902 loss_cls: 0.3224 loss_bbox: 0.7505 loss_dfl: 0.2831 loss_ld: 0.7342
2023/07/13 09:21:24 - mmengine - INFO - Epoch(train) [2][ 650/3139] lr: 1.2500e-03 eta: 3:03:30 time: 0.3233 data_time: 0.0037 memory: 724 loss: 1.8091 loss_cls: 0.3269 loss_bbox: 0.7080 loss_dfl: 0.2486 loss_ld: 0.5256
2023/07/13 09:21:40 - mmengine - INFO - Epoch(train) [2][ 700/3139] lr: 1.2500e-03 eta: 3:03:13 time: 0.3225 data_time: 0.0040 memory: 716 loss: 1.9109 loss_cls: 0.3289 loss_bbox: 0.7713 loss_dfl: 0.2799 loss_ld: 0.5308
2023/07/13 09:21:56 - mmengine - INFO - Epoch(train) [2][ 750/3139] lr: 1.2500e-03 eta: 3:02:56 time: 0.3233 data_time: 0.0043 memory: 728 loss: 2.0026 loss_cls: 0.3085 loss_bbox: 0.7894 loss_dfl: 0.2758 loss_ld: 0.6289
2023/07/13 09:22:13 - mmengine - INFO - Epoch(train) [2][ 800/3139] lr: 1.2500e-03 eta: 3:02:40 time: 0.3251 data_time: 0.0045 memory: 731 loss: 1.8804 loss_cls: 0.3210 loss_bbox: 0.7396 loss_dfl: 0.2668 loss_ld: 0.5530
2023/07/13 09:22:29 - mmengine - INFO - Epoch(train) [2][ 850/3139] lr: 1.2500e-03 eta: 3:02:21 time: 0.3199 data_time: 0.0033 memory: 723 loss: 1.9802 loss_cls: 0.3337 loss_bbox: 0.7190 loss_dfl: 0.2613 loss_ld: 0.6663
2023/07/13 09:22:32 - mmengine - INFO - Exp name: ld_gfl_r18_r101_fpn_1x_vis_20230713_085933
2023/07/13 09:22:45 - mmengine - INFO - Epoch(train) [2][ 900/3139] lr: 1.2500e-03 eta: 3:02:05 time: 0.3248 data_time: 0.0038 memory: 731 loss: 2.0623 loss_cls: 0.2714 loss_bbox: 0.7810 loss_dfl: 0.2799 loss_ld: 0.7300
2023/07/13 09:23:01 - mmengine - INFO - Epoch(train) [2][ 950/3139] lr: 1.2500e-03 eta: 3:01:48 time: 0.3230 data_time: 0.0034 memory: 746 loss: 1.8590 loss_cls: 0.2915 loss_bbox: 0.7538 loss_dfl: 0.2669 loss_ld: 0.5467
2023/07/13 09:23:17 - mmengine - INFO - Epoch(train) [2][1000/3139] lr: 1.2500e-03 eta: 3:01:31 time: 0.3237 data_time: 0.0039 memory: 720 loss: 1.9172 loss_cls: 0.3102 loss_bbox: 0.7668 loss_dfl: 0.2671 loss_ld: 0.5733
2023/07/13 09:23:33 - mmengine - INFO - Epoch(train) [2][1050/3139] lr: 1.2500e-03 eta: 3:01:13 time: 0.3206 data_time: 0.0034 memory: 720 loss: 1.8737 loss_cls: 0.3256 loss_bbox: 0.7144 loss_dfl: 0.2580 loss_ld: 0.5756
2023/07/13 09:23:49 - mmengine - INFO - Epoch(train) [2][1100/3139] lr: 1.2500e-03 eta: 3:00:57 time: 0.3258 data_time: 0.0038 memory: 739 loss: 1.9443 loss_cls: 0.3277 loss_bbox: 0.7204 loss_dfl: 0.2713 loss_ld: 0.6248
2023/07/13 09:24:06 - mmengine - INFO - Epoch(train) [2][1150/3139] lr: 1.2500e-03 eta: 3:00:41 time: 0.3241 data_time: 0.0040 memory: 738 loss: 1.9872 loss_cls: 0.2868 loss_bbox: 0.7940 loss_dfl: 0.2785 loss_ld: 0.6280
2023/07/13 09:24:22 - mmengine - INFO - Epoch(train) [2][1200/3139] lr: 1.2500e-03 eta: 3:00:24 time: 0.3241 data_time: 0.0036 memory: 735 loss: 1.9264 loss_cls: 0.3261 loss_bbox: 0.7162 loss_dfl: 0.2589 loss_ld: 0.6251
2023/07/13 09:24:38 - mmengine - INFO - Epoch(train) [2][1250/3139] lr: 1.2500e-03 eta: 3:00:07 time: 0.3219 data_time: 0.0034 memory: 734 loss: 1.9784 loss_cls: 0.3095 loss_bbox: 0.7702 loss_dfl: 0.2738 loss_ld: 0.6249
2023/07/13 09:24:54 - mmengine - INFO - Epoch(train) [2][1300/3139] lr: 1.2500e-03 eta: 2:59:50 time: 0.3232 data_time: 0.0036 memory: 724 loss: 2.0347 loss_cls: 0.2970 loss_bbox: 0.7701 loss_dfl: 0.2739 loss_ld: 0.6937
2023/07/13 09:25:10 - mmengine - INFO - Epoch(train) [2][1350/3139] lr: 1.2500e-03 eta: 2:59:33 time: 0.3212 data_time: 0.0035 memory: 726 loss: 2.0080 loss_cls: 0.2750 loss_bbox: 0.7498 loss_dfl: 0.2679 loss_ld: 0.7153
2023/07/13 09:25:26 - mmengine - INFO - Epoch(train) [2][1400/3139] lr: 1.2500e-03 eta: 2:59:15 time: 0.3201 data_time: 0.0037 memory: 719 loss: 1.8600 loss_cls: 0.3123 loss_bbox: 0.7422 loss_dfl: 0.2567 loss_ld: 0.5488
2023/07/13 09:25:42 - mmengine - INFO - Epoch(train) [2][1450/3139] lr: 1.2500e-03 eta: 2:58:58 time: 0.3226 data_time: 0.0036 memory: 725 loss: 1.7912 loss_cls: 0.3119 loss_bbox: 0.7084 loss_dfl: 0.2505 loss_ld: 0.5204
2023/07/13 09:25:58 - mmengine - INFO - Epoch(train) [2][1500/3139] lr: 1.2500e-03 eta: 2:58:40 time: 0.3214 data_time: 0.0038 memory: 718 loss: 1.8293 loss_cls: 0.3279 loss_bbox: 0.7110 loss_dfl: 0.2525 loss_ld: 0.5379
2023/07/13 09:26:15 - mmengine - INFO - Epoch(train) [2][1550/3139] lr: 1.2500e-03 eta: 2:58:24 time: 0.3228 data_time: 0.0037 memory: 718 loss: 1.9348 loss_cls: 0.3098 loss_bbox: 0.7497 loss_dfl: 0.2682 loss_ld: 0.6071
2023/07/13 09:26:31 - mmengine - INFO - Epoch(train) [2][1600/3139] lr: 1.2500e-03 eta: 2:58:07 time: 0.3238 data_time: 0.0046 memory: 720 loss: 1.8630 loss_cls: 0.2764 loss_bbox: 0.7121 loss_dfl: 0.2573 loss_ld: 0.6173
2023/07/13 09:26:47 - mmengine - INFO - Epoch(train) [2][1650/3139] lr: 1.2500e-03 eta: 2:57:50 time: 0.3219 data_time: 0.0037 memory: 717 loss: 1.9354 loss_cls: 0.3247 loss_bbox: 0.7084 loss_dfl: 0.2811 loss_ld: 0.6211
2023/07/13 09:27:03 - mmengine - INFO - Epoch(train) [2][1700/3139] lr: 1.2500e-03 eta: 2:57:34 time: 0.3266 data_time: 0.0048 memory: 734 loss: 1.8319 loss_cls: 0.3123 loss_bbox: 0.7048 loss_dfl: 0.2617 loss_ld: 0.5531
2023/07/13 09:27:19 - mmengine - INFO - Epoch(train) [2][1750/3139] lr: 1.2500e-03 eta: 2:57:18 time: 0.3227 data_time: 0.0039 memory: 727 loss: 1.8714 loss_cls: 0.3081 loss_bbox: 0.6823 loss_dfl: 0.2552 loss_ld: 0.6258
2023/07/13 09:27:36 - mmengine - INFO - Epoch(train) [2][1800/3139] lr: 1.2500e-03 eta: 2:57:02 time: 0.3251 data_time: 0.0041 memory: 720 loss: 1.9765 loss_cls: 0.3413 loss_bbox: 0.6715 loss_dfl: 0.2611 loss_ld: 0.7026
2023/07/13 09:27:52 - mmengine - INFO - Epoch(train) [2][1850/3139] lr: 1.2500e-03 eta: 2:56:45 time: 0.3238 data_time: 0.0037 memory: 728 loss: 1.8822 loss_cls: 0.3238 loss_bbox: 0.7194 loss_dfl: 0.2648 loss_ld: 0.5742
2023/07/13 09:27:55 - mmengine - INFO - Exp name: ld_gfl_r18_r101_fpn_1x_vis_20230713_085933
2023/07/13 09:28:08 - mmengine - INFO - Epoch(train) [2][1900/3139] lr: 1.2500e-03 eta: 2:56:29 time: 0.3258 data_time: 0.0053 memory: 736 loss: 1.9576 loss_cls: 0.3062 loss_bbox: 0.7264 loss_dfl: 0.2701 loss_ld: 0.6549
2023/07/13 09:28:24 - mmengine - INFO - Epoch(train) [2][1950/3139] lr: 1.2500e-03 eta: 2:56:12 time: 0.3211 data_time: 0.0039 memory: 752 loss: 1.9770 loss_cls: 0.3051 loss_bbox: 0.7281 loss_dfl: 0.2630 loss_ld: 0.6809
2023/07/13 09:28:40 - mmengine - INFO - Epoch(train) [2][2000/3139] lr: 1.2500e-03 eta: 2:55:56 time: 0.3241 data_time: 0.0042 memory: 735 loss: 1.9473 loss_cls: 0.3038 loss_bbox: 0.7434 loss_dfl: 0.2653 loss_ld: 0.6347
2023/07/13 09:28:56 - mmengine - INFO - Epoch(train) [2][2050/3139] lr: 1.2500e-03 eta: 2:55:39 time: 0.3234 data_time: 0.0038 memory: 724 loss: 1.8560 loss_cls: 0.2922 loss_bbox: 0.7566 loss_dfl: 0.2661 loss_ld: 0.5411
2023/07/13 09:29:13 - mmengine - INFO - Epoch(train) [2][2100/3139] lr: 1.2500e-03 eta: 2:55:22 time: 0.3233 data_time: 0.0038 memory: 721 loss: 1.9887 loss_cls: 0.2847 loss_bbox: 0.7514 loss_dfl: 0.2719 loss_ld: 0.6808
2023/07/13 09:29:29 - mmengine - INFO - Epoch(train) [2][2150/3139] lr: 1.2500e-03 eta: 2:55:07 time: 0.3263 data_time: 0.0035 memory: 716 loss: 1.8883 loss_cls: 0.2965 loss_bbox: 0.7605 loss_dfl: 0.2633 loss_ld: 0.5680
2023/07/13 09:29:45 - mmengine - INFO - Epoch(train) [2][2200/3139] lr: 1.2500e-03 eta: 2:54:51 time: 0.3268 data_time: 0.0044 memory: 724 loss: 1.8254 loss_cls: 0.3345 loss_bbox: 0.7304 loss_dfl: 0.2607 loss_ld: 0.4997
2023/07/13 09:30:01 - mmengine - INFO - Epoch(train) [2][2250/3139] lr: 1.2500e-03 eta: 2:54:35 time: 0.3234 data_time: 0.0035 memory: 728 loss: 1.9334 loss_cls: 0.2764 loss_bbox: 0.7136 loss_dfl: 0.2621 loss_ld: 0.6813
2023/07/13 09:30:17 - mmengine - INFO - Epoch(train) [2][2300/3139] lr: 1.2500e-03 eta: 2:54:17 time: 0.3200 data_time: 0.0035 memory: 723 loss: 1.8456 loss_cls: 0.3174 loss_bbox: 0.6934 loss_dfl: 0.2487 loss_ld: 0.5861
2023/07/13 09:30:34 - mmengine - INFO - Epoch(train) [2][2350/3139] lr: 1.2500e-03 eta: 2:54:01 time: 0.3252 data_time: 0.0035 memory: 727 loss: 1.8935 loss_cls: 0.3127 loss_bbox: 0.7063 loss_dfl: 0.2561 loss_ld: 0.6184
2023/07/13 09:30:50 - mmengine - INFO - Epoch(train) [2][2400/3139] lr: 1.2500e-03 eta: 2:53:45 time: 0.3242 data_time: 0.0035 memory: 719 loss: 1.9534 loss_cls: 0.2927 loss_bbox: 0.7259 loss_dfl: 0.2734 loss_ld: 0.6614
2023/07/13 09:31:06 - mmengine - INFO - Epoch(train) [2][2450/3139] lr: 1.2500e-03 eta: 2:53:28 time: 0.3229 data_time: 0.0041 memory: 723 loss: 1.8156 loss_cls: 0.2992 loss_bbox: 0.7202 loss_dfl: 0.2554 loss_ld: 0.5408
2023/07/13 09:31:22 - mmengine - INFO - Epoch(train) [2][2500/3139] lr: 1.2500e-03 eta: 2:53:11 time: 0.3198 data_time: 0.0035 memory: 720 loss: 1.8135 loss_cls: 0.3204 loss_bbox: 0.7004 loss_dfl: 0.2479 loss_ld: 0.5448
2023/07/13 09:31:38 - mmengine - INFO - Epoch(train) [2][2550/3139] lr: 1.2500e-03 eta: 2:52:53 time: 0.3196 data_time: 0.0034 memory: 716 loss: 1.9388 loss_cls: 0.3347 loss_bbox: 0.6979 loss_dfl: 0.2593 loss_ld: 0.6469
2023/07/13 09:31:54 - mmengine - INFO - Epoch(train) [2][2600/3139] lr: 1.2500e-03 eta: 2:52:37 time: 0.3245 data_time: 0.0036 memory: 725 loss: 1.8589 loss_cls: 0.3147 loss_bbox: 0.6931 loss_dfl: 0.2517 loss_ld: 0.5994
2023/07/13 09:32:11 - mmengine - INFO - Epoch(train) [2][2650/3139] lr: 1.2500e-03 eta: 2:52:21 time: 0.3262 data_time: 0.0049 memory: 731 loss: 1.8815 loss_cls: 0.3208 loss_bbox: 0.7220 loss_dfl: 0.2599 loss_ld: 0.5789
2023/07/13 09:32:27 - mmengine - INFO - Epoch(train) [2][2700/3139] lr: 1.2500e-03 eta: 2:52:04 time: 0.3229 data_time: 0.0040 memory: 714 loss: 1.8437 loss_cls: 0.4384 loss_bbox: 0.7151 loss_dfl: 0.2542 loss_ld: 0.4361
2023/07/13 09:32:43 - mmengine - INFO - Epoch(train) [2][2750/3139] lr: 1.2500e-03 eta: 2:51:48 time: 0.3223 data_time: 0.0038 memory: 724 loss: 1.8273 loss_cls: 0.3126 loss_bbox: 0.6736 loss_dfl: 0.2441 loss_ld: 0.5970
2023/07/13 09:32:59 - mmengine - INFO - Epoch(train) [2][2800/3139] lr: 1.2500e-03 eta: 2:51:31 time: 0.3236 data_time: 0.0035 memory: 724 loss: 1.8726 loss_cls: 0.3061 loss_bbox: 0.7400 loss_dfl: 0.2535 loss_ld: 0.5730
2023/07/13 09:33:15 - mmengine - INFO - Epoch(train) [2][2850/3139] lr: 1.2500e-03 eta: 2:51:15 time: 0.3231 data_time: 0.0037 memory: 731 loss: 1.9095 loss_cls: 0.3015 loss_bbox: 0.6970 loss_dfl: 0.2557 loss_ld: 0.6552
2023/07/13 09:33:19 - mmengine - INFO - Exp name: ld_gfl_r18_r101_fpn_1x_vis_20230713_085933
2023/07/13 09:33:31 - mmengine - INFO - Epoch(train) [2][2900/3139] lr: 1.2500e-03 eta: 2:50:57 time: 0.3192 data_time: 0.0036 memory: 722 loss: 1.7870 loss_cls: 0.3112 loss_bbox: 0.6900 loss_dfl: 0.2493 loss_ld: 0.5364
2023/07/13 09:33:47 - mmengine - INFO - Epoch(train) [2][2950/3139] lr: 1.2500e-03 eta: 2:50:41 time: 0.3229 data_time: 0.0039 memory: 725 loss: 1.7961 loss_cls: 0.3078 loss_bbox: 0.6761 loss_dfl: 0.2541 loss_ld: 0.5582
2023/07/13 09:34:03 - mmengine - INFO - Epoch(train) [2][3000/3139] lr: 1.2500e-03 eta: 2:50:24 time: 0.3216 data_time: 0.0033 memory: 721 loss: 1.8967 loss_cls: 0.2707 loss_bbox: 0.7093 loss_dfl: 0.2490 loss_ld: 0.6677
2023/07/13 09:34:20 - mmengine - INFO - Epoch(train) [2][3050/3139] lr: 1.2500e-03 eta: 2:50:07 time: 0.3226 data_time: 0.0039 memory: 726 loss: 1.7837 loss_cls: 0.3187 loss_bbox: 0.6984 loss_dfl: 0.2479 loss_ld: 0.5185
2023/07/13 09:34:36 - mmengine - INFO - Epoch(train) [2][3100/3139] lr: 1.2500e-03 eta: 2:49:50 time: 0.3225 data_time: 0.0043 memory: 723 loss: 1.9092 loss_cls: 0.2882 loss_bbox: 0.7083 loss_dfl: 0.2708 loss_ld: 0.6420
2023/07/13 09:34:48 - mmengine - INFO - Exp name: ld_gfl_r18_r101_fpn_1x_vis_20230713_085933
2023/07/13 09:34:48 - mmengine - INFO - Saving checkpoint at 2 epochs
2023/07/13 09:34:56 - mmengine - INFO - Epoch(val) [2][ 50/548] eta: 0:00:38 time: 0.0765 data_time: 0.0022 memory: 723
2023/07/13 09:35:00 - mmengine - INFO - Epoch(val) [2][100/548] eta: 0:00:33 time: 0.0744 data_time: 0.0015 memory: 497
2023/07/13 09:35:03 - mmengine - INFO - Epoch(val) [2][150/548] eta: 0:00:29 time: 0.0746 data_time: 0.0014 memory: 497
2023/07/13 09:35:07 - mmengine - INFO - Epoch(val) [2][200/548] eta: 0:00:26 time: 0.0746 data_time: 0.0014 memory: 497
2023/07/13 09:35:11 - mmengine - INFO - Epoch(val) [2][250/548] eta: 0:00:22 time: 0.0748 data_time: 0.0014 memory: 497
2023/07/13 09:35:14 - mmengine - INFO - Epoch(val) [2][300/548] eta: 0:00:18 time: 0.0742 data_time: 0.0014 memory: 497
2023/07/13 09:35:18 - mmengine - INFO - Epoch(val) [2][350/548] eta: 0:00:14 time: 0.0743 data_time: 0.0014 memory: 497
2023/07/13 09:35:22 - mmengine - INFO - Epoch(val) [2][400/548] eta: 0:00:11 time: 0.0742 data_time: 0.0014 memory: 497
2023/07/13 09:35:26 - mmengine - INFO - Epoch(val) [2][450/548] eta: 0:00:07 time: 0.0747 data_time: 0.0014 memory: 497
2023/07/13 09:35:29 - mmengine - INFO - Epoch(val) [2][500/548] eta: 0:00:03 time: 0.0738 data_time: 0.0014 memory: 497
2023/07/13 09:35:34 - mmengine - INFO - Evaluating bbox...
2023/07/13 09:35:50 - mmengine - INFO - bbox_mAP_copypaste: 0.047 0.088 0.046 0.012 0.073 0.137
2023/07/13 09:35:50 - mmengine - INFO - Epoch(val) [2][548/548] coco/bbox_mAP: 0.0470 coco/bbox_mAP_50: 0.0880 coco/bbox_mAP_75: 0.0460 coco/bbox_mAP_s: 0.0120 coco/bbox_mAP_m: 0.0730 coco/bbox_mAP_l: 0.1370 data_time: 0.0015 time: 0.0745
2023/07/13 09:36:06 - mmengine - INFO - Epoch(train) [3][ 50/3139] lr: 1.2500e-03 eta: 2:49:21 time: 0.3253 data_time: 0.0053 memory: 736 loss: 1.8826 loss_cls: 0.3176 loss_bbox: 0.7338 loss_dfl: 0.2575 loss_ld: 0.5737
2023/07/13 09:36:22 - mmengine - INFO - Epoch(train) [3][ 100/3139] lr: 1.2500e-03 eta: 2:49:05 time: 0.3260 data_time: 0.0040 memory: 761 loss: 1.9164 loss_cls: 0.3083 loss_bbox: 0.6900 loss_dfl: 0.2560 loss_ld: 0.6622
2023/07/13 09:36:38 - mmengine - INFO - Epoch(train) [3][ 150/3139] lr: 1.2500e-03 eta: 2:48:49 time: 0.3230 data_time: 0.0039 memory: 725 loss: 1.7631 loss_cls: 0.3200 loss_bbox: 0.6550 loss_dfl: 0.2445 loss_ld: 0.5436
2023/07/13 09:36:55 - mmengine - INFO - Epoch(train) [3][ 200/3139] lr: 1.2500e-03 eta: 2:48:33 time: 0.3243 data_time: 0.0038 memory: 738 loss: 1.7691 loss_cls: 0.2909 loss_bbox: 0.6617 loss_dfl: 0.2443 loss_ld: 0.5722
2023/07/13 09:37:11 - mmengine - INFO - Epoch(train) [3][ 250/3139] lr: 1.2500e-03 eta: 2:48:16 time: 0.3234 data_time: 0.0043 memory: 731 loss: 1.8646 loss_cls: 0.3569 loss_bbox: 0.7482 loss_dfl: 0.2608 loss_ld: 0.4988
2023/07/13 09:37:27 - mmengine - INFO - Epoch(train) [3][ 300/3139] lr: 1.2500e-03 eta: 2:48:00 time: 0.3249 data_time: 0.0038 memory: 725 loss: 1.8495 loss_cls: 0.3542 loss_bbox: 0.6976 loss_dfl: 0.2578 loss_ld: 0.5399
2023/07/13 09:37:43 - mmengine - INFO - Epoch(train) [3][ 350/3139] lr: 1.2500e-03 eta: 2:47:44 time: 0.3249 data_time: 0.0038 memory: 719 loss: 1.6635 loss_cls: 0.3014 loss_bbox: 0.6512 loss_dfl: 0.2349 loss_ld: 0.4760
2023/07/13 09:37:59 - mmengine - INFO - Epoch(train) [3][ 400/3139] lr: 1.2500e-03 eta: 2:47:27 time: 0.3192 data_time: 0.0035 memory: 736 loss: 1.8188 loss_cls: 0.2809 loss_bbox: 0.6584 loss_dfl: 0.2473 loss_ld: 0.6323
2023/07/13 09:38:15 - mmengine - INFO - Epoch(train) [3][ 450/3139] lr: 1.2500e-03 eta: 2:47:10 time: 0.3213 data_time: 0.0037 memory: 727 loss: 1.7053 loss_cls: 0.2907 loss_bbox: 0.6477 loss_dfl: 0.2389 loss_ld: 0.5280
2023/07/13 09:38:32 - mmengine - INFO - Epoch(train) [3][ 500/3139] lr: 1.2500e-03 eta: 2:46:54 time: 0.3259 data_time: 0.0039 memory: 739 loss: 1.7975 loss_cls: 0.3063 loss_bbox: 0.6783 loss_dfl: 0.2532 loss_ld: 0.5597
2023/07/13 09:38:48 - mmengine - INFO - Epoch(train) [3][ 550/3139] lr: 1.2500e-03 eta: 2:46:37 time: 0.3224 data_time: 0.0037 memory: 724 loss: 1.6889 loss_cls: 0.3176 loss_bbox: 0.6765 loss_dfl: 0.2397 loss_ld: 0.4551
2023/07/13 09:39:04 - mmengine - INFO - Epoch(train) [3][ 600/3139] lr: 1.2500e-03 eta: 2:46:22 time: 0.3269 data_time: 0.0036 memory: 721 loss: 1.7503 loss_cls: 0.3217 loss_bbox: 0.6744 loss_dfl: 0.2418 loss_ld: 0.5124
2023/07/13 09:39:20 - mmengine - INFO - Epoch(train) [3][ 650/3139] lr: 1.2500e-03 eta: 2:46:05 time: 0.3213 data_time: 0.0038 memory: 728 loss: 1.6895 loss_cls: 0.3153 loss_bbox: 0.6657 loss_dfl: 0.2365 loss_ld: 0.4720
2023/07/13 09:39:36 - mmengine - INFO - Epoch(train) [3][ 700/3139] lr: 1.2500e-03 eta: 2:45:49 time: 0.3268 data_time: 0.0044 memory: 730 loss: 1.7855 loss_cls: 0.2912 loss_bbox: 0.6791 loss_dfl: 0.2483 loss_ld: 0.5669
2023/07/13 09:39:44 - mmengine - INFO - Exp name: ld_gfl_r18_r101_fpn_1x_vis_20230713_085933
2023/07/13 09:39:53 - mmengine - INFO - Epoch(train) [3][ 750/3139] lr: 1.2500e-03 eta: 2:45:34 time: 0.3275 data_time: 0.0047 memory: 728 loss: 1.8173 loss_cls: 0.3525 loss_bbox: 0.7012 loss_dfl: 0.2536 loss_ld: 0.5100
2023/07/13 09:40:09 - mmengine - INFO - Epoch(train) [3][ 800/3139] lr: 1.2500e-03 eta: 2:45:18 time: 0.3237 data_time: 0.0043 memory: 719 loss: 1.6604 loss_cls: 0.3124 loss_bbox: 0.6397 loss_dfl: 0.2305 loss_ld: 0.4778
2023/07/13 09:40:25 - mmengine - INFO - Epoch(train) [3][ 850/3139] lr: 1.2500e-03 eta: 2:45:01 time: 0.3250 data_time: 0.0048 memory: 720 loss: 1.9202 loss_cls: 0.4515 loss_bbox: 0.6893 loss_dfl: 0.2518 loss_ld: 0.5276
2023/07/13 09:40:42 - mmengine - INFO - Epoch(train) [3][ 900/3139] lr: 1.2500e-03 eta: 2:44:46 time: 0.3253 data_time: 0.0052 memory: 734 loss: 1.7818 loss_cls: 0.3226 loss_bbox: 0.6853 loss_dfl: 0.2532 loss_ld: 0.5208
2023/07/13 09:40:58 - mmengine - INFO - Epoch(train) [3][ 950/3139] lr: 1.2500e-03 eta: 2:44:29 time: 0.3225 data_time: 0.0035 memory: 722 loss: 1.7329 loss_cls: 0.2982 loss_bbox: 0.7071 loss_dfl: 0.2441 loss_ld: 0.4836
2023/07/13 09:41:14 - mmengine - INFO - Epoch(train) [3][1000/3139] lr: 1.2500e-03 eta: 2:44:12 time: 0.3203 data_time: 0.0034 memory: 720 loss: 1.7894 loss_cls: 0.3457 loss_bbox: 0.6593 loss_dfl: 0.2498 loss_ld: 0.5346
2023/07/13 09:41:30 - mmengine - INFO - Epoch(train) [3][1050/3139] lr: 1.2500e-03 eta: 2:43:54 time: 0.3179 data_time: 0.0036 memory: 730 loss: 1.8213 loss_cls: 0.2858 loss_bbox: 0.7340 loss_dfl: 0.2595 loss_ld: 0.5420
2023/07/13 09:41:46 - mmengine - INFO - Epoch(train) [3][1100/3139] lr: 1.2500e-03 eta: 2:43:38 time: 0.3255 data_time: 0.0034 memory: 721 loss: 1.7667 loss_cls: 0.2856 loss_bbox: 0.6852 loss_dfl: 0.2427 loss_ld: 0.5531
2023/07/13 09:42:02 - mmengine - INFO - Epoch(train) [3][1150/3139] lr: 1.2500e-03 eta: 2:43:22 time: 0.3229 data_time: 0.0040 memory: 743 loss: 1.7536 loss_cls: 0.2996 loss_bbox: 0.6769 loss_dfl: 0.2433 loss_ld: 0.5338
2023/07/13 09:42:18 - mmengine - INFO - Epoch(train) [3][1200/3139] lr: 1.2500e-03 eta: 2:43:06 time: 0.3229 data_time: 0.0036 memory: 722 loss: 1.8510 loss_cls: 0.2991 loss_bbox: 0.7014 loss_dfl: 0.2586 loss_ld: 0.5920
2023/07/13 09:42:34 - mmengine - INFO - Epoch(train) [3][1250/3139] lr: 1.2500e-03 eta: 2:42:49 time: 0.3247 data_time: 0.0049 memory: 727 loss: 1.7946 loss_cls: 0.3205 loss_bbox: 0.6787 loss_dfl: 0.2571 loss_ld: 0.5383
2023/07/13 09:42:50 - mmengine - INFO - Epoch(train) [3][1300/3139] lr: 1.2500e-03 eta: 2:42:33 time: 0.3212 data_time: 0.0042 memory: 728 loss: 1.7787 loss_cls: 0.3071 loss_bbox: 0.6426 loss_dfl: 0.2497 loss_ld: 0.5793
2023/07/13 09:43:06 - mmengine - INFO - Epoch(train) [3][1350/3139] lr: 1.2500e-03 eta: 2:42:15 time: 0.3182 data_time: 0.0035 memory: 721 loss: 1.6389 loss_cls: 0.3236 loss_bbox: 0.6563 loss_dfl: 0.2367 loss_ld: 0.4223
2023/07/13 09:43:22 - mmengine - INFO - Epoch(train) [3][1400/3139] lr: 1.2500e-03 eta: 2:41:58 time: 0.3204 data_time: 0.0035 memory: 719 loss: 1.6971 loss_cls: 0.3330 loss_bbox: 0.6628 loss_dfl: 0.2419 loss_ld: 0.4595
2023/07/13 09:43:39 - mmengine - INFO - Epoch(train) [3][1450/3139] lr: 1.2500e-03 eta: 2:41:42 time: 0.3257 data_time: 0.0057 memory: 723 loss: 1.7828 loss_cls: 0.2973 loss_bbox: 0.6071 loss_dfl: 0.2327 loss_ld: 0.6457
2023/07/13 09:43:55 - mmengine - INFO - Epoch(train) [3][1500/3139] lr: 1.2500e-03 eta: 2:41:27 time: 0.3257 data_time: 0.0040 memory: 722 loss: 1.7950 loss_cls: 0.3041 loss_bbox: 0.6637 loss_dfl: 0.2658 loss_ld: 0.5614
2023/07/13 09:44:11 - mmengine - INFO - Epoch(train) [3][1550/3139] lr: 1.2500e-03 eta: 2:41:11 time: 0.3259 data_time: 0.0044 memory: 735 loss: 1.8003 loss_cls: 0.2908 loss_bbox: 0.6566 loss_dfl: 0.2495 loss_ld: 0.6034
2023/07/13 09:44:27 - mmengine - INFO - Epoch(train) [3][1600/3139] lr: 1.2500e-03 eta: 2:40:54 time: 0.3230 data_time: 0.0034 memory: 729 loss: 1.7032 loss_cls: 0.2926 loss_bbox: 0.6789 loss_dfl: 0.2369 loss_ld: 0.4949
2023/07/13 09:44:44 - mmengine - INFO - Epoch(train) [3][1650/3139] lr: 1.2500e-03 eta: 2:40:38 time: 0.3247 data_time: 0.0045 memory: 726 loss: 1.5559 loss_cls: 0.2925 loss_bbox: 0.6437 loss_dfl: 0.2308 loss_ld: 0.3889
2023/07/13 09:45:00 - mmengine - INFO - Epoch(train) [3][1700/3139] lr: 1.2500e-03 eta: 2:40:21 time: 0.3177 data_time: 0.0037 memory: 720 loss: 1.7090 loss_cls: 0.3076 loss_bbox: 0.7041 loss_dfl: 0.2446 loss_ld: 0.4528
2023/07/13 09:45:07 - mmengine - INFO - Exp name: ld_gfl_r18_r101_fpn_1x_vis_20230713_085933
2023/07/13 09:45:16 - mmengine - INFO - Epoch(train) [3][1750/3139] lr: 1.2500e-03 eta: 2:40:04 time: 0.3229 data_time: 0.0043 memory: 724 loss: 1.7273 loss_cls: 0.3010 loss_bbox: 0.6569 loss_dfl: 0.2371 loss_ld: 0.5324
2023/07/13 09:45:32 - mmengine - INFO - Epoch(train) [3][1800/3139] lr: 1.2500e-03 eta: 2:39:48 time: 0.3235 data_time: 0.0040 memory: 726 loss: 1.7042 loss_cls: 0.2848 loss_bbox: 0.6160 loss_dfl: 0.2347 loss_ld: 0.5687
2023/07/13 09:45:48 - mmengine - INFO - Epoch(train) [3][1850/3139] lr: 1.2500e-03 eta: 2:39:32 time: 0.3259 data_time: 0.0049 memory: 749 loss: 1.6815 loss_cls: 0.2918 loss_bbox: 0.6143 loss_dfl: 0.2297 loss_ld: 0.5457
2023/07/13 09:46:04 - mmengine - INFO - Epoch(train) [3][1900/3139] lr: 1.2500e-03 eta: 2:39:16 time: 0.3240 data_time: 0.0040 memory: 735 loss: 1.7921 loss_cls: 0.3143 loss_bbox: 0.6799 loss_dfl: 0.2467 loss_ld: 0.5512
2023/07/13 09:46:20 - mmengine - INFO - Epoch(train) [3][1950/3139] lr: 1.2500e-03 eta: 2:38:59 time: 0.3219 data_time: 0.0043 memory: 723 loss: 1.6559 loss_cls: 0.3036 loss_bbox: 0.6732 loss_dfl: 0.2379 loss_ld: 0.4411
2023/07/13 09:46:37 - mmengine - INFO - Epoch(train) [3][2000/3139] lr: 1.2500e-03 eta: 2:38:43 time: 0.3247 data_time: 0.0045 memory: 724 loss: 1.7445 loss_cls: 0.2829 loss_bbox: 0.6628 loss_dfl: 0.2365 loss_ld: 0.5623
2023/07/13 09:46:53 - mmengine - INFO - Epoch(train) [3][2050/3139] lr: 1.2500e-03 eta: 2:38:27 time: 0.3260 data_time: 0.0042 memory: 721 loss: 1.7954 loss_cls: 0.3056 loss_bbox: 0.6592 loss_dfl: 0.2422 loss_ld: 0.5885
2023/07/13 09:47:09 - mmengine - INFO - Epoch(train) [3][2100/3139] lr: 1.2500e-03 eta: 2:38:11 time: 0.3204 data_time: 0.0037 memory: 731 loss: 1.6894 loss_cls: 0.2826 loss_bbox: 0.6194 loss_dfl: 0.2350 loss_ld: 0.5525
2023/07/13 09:47:25 - mmengine - INFO - Epoch(train) [3][2150/3139] lr: 1.2500e-03 eta: 2:37:54 time: 0.3196 data_time: 0.0040 memory: 724 loss: 1.7174 loss_cls: 0.3070 loss_bbox: 0.6485 loss_dfl: 0.2422 loss_ld: 0.5197
2023/07/13 09:47:41 - mmengine - INFO - Epoch(train) [3][2200/3139] lr: 1.2500e-03 eta: 2:37:38 time: 0.3261 data_time: 0.0042 memory: 731 loss: 1.6862 loss_cls: 0.2828 loss_bbox: 0.6432 loss_dfl: 0.2337 loss_ld: 0.5265
2023/07/13 09:47:57 - mmengine - INFO - Epoch(train) [3][2250/3139] lr: 1.2500e-03 eta: 2:37:21 time: 0.3229 data_time: 0.0042 memory: 717 loss: 1.7430 loss_cls: 0.4050 loss_bbox: 0.6291 loss_dfl: 0.2442 loss_ld: 0.4647
2023/07/13 09:48:14 - mmengine - INFO - Epoch(train) [3][2300/3139] lr: 1.2500e-03 eta: 2:37:05 time: 0.3229 data_time: 0.0044 memory: 718 loss: 1.7529 loss_cls: 0.3015 loss_bbox: 0.6391 loss_dfl: 0.2432 loss_ld: 0.5691
2023/07/13 09:48:30 - mmengine - INFO - Epoch(train) [3][2350/3139] lr: 1.2500e-03 eta: 2:36:49 time: 0.3237 data_time: 0.0036 memory: 740 loss: 1.7104 loss_cls: 0.3037 loss_bbox: 0.6394 loss_dfl: 0.2373 loss_ld: 0.5300
2023/07/13 09:48:46 - mmengine - INFO - Epoch(train) [3][2400/3139] lr: 1.2500e-03 eta: 2:36:33 time: 0.3240 data_time: 0.0036 memory: 721 loss: 1.6954 loss_cls: 0.2795 loss_bbox: 0.6491 loss_dfl: 0.2397 loss_ld: 0.5272
2023/07/13 09:49:02 - mmengine - INFO - Epoch(train) [3][2450/3139] lr: 1.2500e-03 eta: 2:36:17 time: 0.3261 data_time: 0.0047 memory: 720 loss: 1.7749 loss_cls: 0.2825 loss_bbox: 0.6929 loss_dfl: 0.2439 loss_ld: 0.5555
2023/07/13 09:49:18 - mmengine - INFO - Epoch(train) [3][2500/3139] lr: 1.2500e-03 eta: 2:36:00 time: 0.3222 data_time: 0.0037 memory: 721 loss: 1.7334 loss_cls: 0.2933 loss_bbox: 0.6363 loss_dfl: 0.2343 loss_ld: 0.5695
2023/07/13 09:49:34 - mmengine - INFO - Epoch(train) [3][2550/3139] lr: 1.2500e-03 eta: 2:35:44 time: 0.3222 data_time: 0.0045 memory: 721 loss: 1.6472 loss_cls: 0.3077 loss_bbox: 0.6057 loss_dfl: 0.2268 loss_ld: 0.5070
2023/07/13 09:49:51 - mmengine - INFO - Epoch(train) [3][2600/3139] lr: 1.2500e-03 eta: 2:35:27 time: 0.3212 data_time: 0.0038 memory: 717 loss: 1.7285 loss_cls: 0.3769 loss_bbox: 0.6477 loss_dfl: 0.2424 loss_ld: 0.4615
2023/07/13 09:50:07 - mmengine - INFO - Epoch(train) [3][2650/3139] lr: 1.2500e-03 eta: 2:35:10 time: 0.3204 data_time: 0.0043 memory: 733 loss: 1.7224 loss_cls: 0.3253 loss_bbox: 0.6217 loss_dfl: 0.2337 loss_ld: 0.5418
2023/07/13 09:50:23 - mmengine - INFO - Epoch(train) [3][2700/3139] lr: 1.2500e-03 eta: 2:34:54 time: 0.3213 data_time: 0.0038 memory: 730 loss: 1.7368 loss_cls: 0.3461 loss_bbox: 0.6826 loss_dfl: 0.2416 loss_ld: 0.4665
2023/07/13 09:50:30 - mmengine - INFO - Exp name: ld_gfl_r18_r101_fpn_1x_vis_20230713_085933
2023/07/13 09:50:39 - mmengine - INFO - Epoch(train) [3][2750/3139] lr: 1.2500e-03 eta: 2:34:38 time: 0.3244 data_time: 0.0048 memory: 728 loss: 1.7753 loss_cls: 0.3013 loss_bbox: 0.6344 loss_dfl: 0.2495 loss_ld: 0.5901
2023/07/13 09:50:55 - mmengine - INFO - Epoch(train) [3][2800/3139] lr: 1.2500e-03 eta: 2:34:22 time: 0.3251 data_time: 0.0045 memory: 717 loss: 1.6345 loss_cls: 0.3108 loss_bbox: 0.6211 loss_dfl: 0.2315 loss_ld: 0.4710
2023/07/13 09:51:11 - mmengine - INFO - Epoch(train) [3][2850/3139] lr: 1.2500e-03 eta: 2:34:05 time: 0.3250 data_time: 0.0038 memory: 729 loss: 1.6595 loss_cls: 0.3047 loss_bbox: 0.6489 loss_dfl: 0.2301 loss_ld: 0.4757
2023/07/13 09:51:28 - mmengine - INFO - Epoch(train) [3][2900/3139] lr: 1.2500e-03 eta: 2:33:49 time: 0.3242 data_time: 0.0048 memory: 722 loss: 1.6273 loss_cls: 0.3099 loss_bbox: 0.6613 loss_dfl: 0.2323 loss_ld: 0.4239
2023/07/13 09:51:44 - mmengine - INFO - Epoch(train) [3][2950/3139] lr: 1.2500e-03 eta: 2:33:33 time: 0.3221 data_time: 0.0036 memory: 727 loss: 1.5958 loss_cls: 0.2899 loss_bbox: 0.6558 loss_dfl: 0.2293 loss_ld: 0.4207
2023/07/13 09:52:00 - mmengine - INFO - Epoch(train) [3][3000/3139] lr: 1.2500e-03 eta: 2:33:17 time: 0.3234 data_time: 0.0051 memory: 752 loss: 1.6329 loss_cls: 0.3156 loss_bbox: 0.6314 loss_dfl: 0.2291 loss_ld: 0.4568
2023/07/13 09:52:16 - mmengine - INFO - Epoch(train) [3][3050/3139] lr: 1.2500e-03 eta: 2:33:01 time: 0.3254 data_time: 0.0046 memory: 723 loss: 1.7130 loss_cls: 0.3481 loss_bbox: 0.6830 loss_dfl: 0.2373 loss_ld: 0.4447
2023/07/13 09:52:32 - mmengine - INFO - Epoch(train) [3][3100/3139] lr: 1.2500e-03 eta: 2:32:45 time: 0.3257 data_time: 0.0041 memory: 723 loss: 1.7310 loss_cls: 0.3720 loss_bbox: 0.6343 loss_dfl: 0.2464 loss_ld: 0.4783
2023/07/13 09:52:45 - mmengine - INFO - Exp name: ld_gfl_r18_r101_fpn_1x_vis_20230713_085933
2023/07/13 09:52:45 - mmengine - INFO - Saving checkpoint at 3 epochs
2023/07/13 09:52:52 - mmengine - INFO - Epoch(val) [3][ 50/548] eta: 0:00:37 time: 0.0761 data_time: 0.0021 memory: 722
2023/07/13 09:52:56 - mmengine - INFO - Epoch(val) [3][100/548] eta: 0:00:33 time: 0.0738 data_time: 0.0014 memory: 497
2023/07/13 09:53:00 - mmengine - INFO - Epoch(val) [3][150/548] eta: 0:00:29 time: 0.0754 data_time: 0.0013 memory: 497
2023/07/13 09:53:03 - mmengine - INFO - Epoch(val) [3][200/548] eta: 0:00:26 time: 0.0737 data_time: 0.0014 memory: 497
2023/07/13 09:53:07 - mmengine - INFO - Epoch(val) [3][250/548] eta: 0:00:22 time: 0.0741 data_time: 0.0013 memory: 497
2023/07/13 09:53:11 - mmengine - INFO - Epoch(val) [3][300/548] eta: 0:00:18 time: 0.0743 data_time: 0.0014 memory: 497
2023/07/13 09:53:14 - mmengine - INFO - Epoch(val) [3][350/548] eta: 0:00:14 time: 0.0742 data_time: 0.0014 memory: 497
2023/07/13 09:53:18 - mmengine - INFO - Epoch(val) [3][400/548] eta: 0:00:10 time: 0.0729 data_time: 0.0013 memory: 497
2023/07/13 09:53:22 - mmengine - INFO - Epoch(val) [3][450/548] eta: 0:00:07 time: 0.0740 data_time: 0.0014 memory: 497
2023/07/13 09:53:25 - mmengine - INFO - Epoch(val) [3][500/548] eta: 0:00:03 time: 0.0731 data_time: 0.0013 memory: 497
2023/07/13 09:53:30 - mmengine - INFO - Evaluating bbox...
2023/07/13 09:53:46 - mmengine - INFO - bbox_mAP_copypaste: 0.053 0.096 0.053 0.015 0.082 0.154
2023/07/13 09:53:46 - mmengine - INFO - Epoch(val) [3][548/548] coco/bbox_mAP: 0.0530 coco/bbox_mAP_50: 0.0960 coco/bbox_mAP_75: 0.0530 coco/bbox_mAP_s: 0.0150 coco/bbox_mAP_m: 0.0820 coco/bbox_mAP_l: 0.1540 data_time: 0.0014 time: 0.0740
2023/07/13 09:54:03 - mmengine - INFO - Epoch(train) [4][ 50/3139] lr: 1.2500e-03 eta: 2:32:15 time: 0.3244 data_time: 0.0056 memory: 725 loss: 1.6609 loss_cls: 0.3170 loss_bbox: 0.6065 loss_dfl: 0.2251 loss_ld: 0.5123
2023/07/13 09:54:19 - mmengine - INFO - Epoch(train) [4][ 100/3139] lr: 1.2500e-03 eta: 2:31:59 time: 0.3242 data_time: 0.0038 memory: 724 loss: 1.6738 loss_cls: 0.3014 loss_bbox: 0.6159 loss_dfl: 0.2317 loss_ld: 0.5248
2023/07/13 09:54:35 - mmengine - INFO - Epoch(train) [4][ 150/3139] lr: 1.2500e-03 eta: 2:31:43 time: 0.3257 data_time: 0.0046 memory: 743 loss: 1.6492 loss_cls: 0.2921 loss_bbox: 0.6131 loss_dfl: 0.2400 loss_ld: 0.5041
2023/07/13 09:54:51 - mmengine - INFO - Epoch(train) [4][ 200/3139] lr: 1.2500e-03 eta: 2:31:27 time: 0.3225 data_time: 0.0038 memory: 728 loss: 1.6534 loss_cls: 0.3130 loss_bbox: 0.6324 loss_dfl: 0.2362 loss_ld: 0.4718
2023/07/13 09:55:07 - mmengine - INFO - Epoch(train) [4][ 250/3139] lr: 1.2500e-03 eta: 2:31:10 time: 0.3237 data_time: 0.0036 memory: 726 loss: 1.7223 loss_cls: 0.3333 loss_bbox: 0.6572 loss_dfl: 0.2403 loss_ld: 0.4915
2023/07/13 09:55:24 - mmengine - INFO - Epoch(train) [4][ 300/3139] lr: 1.2500e-03 eta: 2:30:54 time: 0.3236 data_time: 0.0038 memory: 718 loss: 1.7746 loss_cls: 0.2730 loss_bbox: 0.6352 loss_dfl: 0.2358 loss_ld: 0.6306
2023/07/13 09:55:40 - mmengine - INFO - Epoch(train) [4][ 350/3139] lr: 1.2500e-03 eta: 2:30:38 time: 0.3233 data_time: 0.0038 memory: 735 loss: 1.7653 loss_cls: 0.3134 loss_bbox: 0.6006 loss_dfl: 0.2379 loss_ld: 0.6134
2023/07/13 09:55:56 - mmengine - INFO - Epoch(train) [4][ 400/3139] lr: 1.2500e-03 eta: 2:30:22 time: 0.3225 data_time: 0.0043 memory: 721 loss: 1.5574 loss_cls: 0.3010 loss_bbox: 0.6111 loss_dfl: 0.2195 loss_ld: 0.4258
2023/07/13 09:56:12 - mmengine - INFO - Epoch(train) [4][ 450/3139] lr: 1.2500e-03 eta: 2:30:05 time: 0.3238 data_time: 0.0040 memory: 729 loss: 1.5666 loss_cls: 0.3066 loss_bbox: 0.5807 loss_dfl: 0.2224 loss_ld: 0.4570
2023/07/13 09:56:28 - mmengine - INFO - Epoch(train) [4][ 500/3139] lr: 1.2500e-03 eta: 2:29:49 time: 0.3226 data_time: 0.0041 memory: 717 loss: 1.6884 loss_cls: 0.3584 loss_bbox: 0.6401 loss_dfl: 0.2494 loss_ld: 0.4404
2023/07/13 09:56:44 - mmengine - INFO - Epoch(train) [4][ 550/3139] lr: 1.2500e-03 eta: 2:29:33 time: 0.3227 data_time: 0.0036 memory: 747 loss: 1.6006 loss_cls: 0.3157 loss_bbox: 0.6012 loss_dfl: 0.2243 loss_ld: 0.4594
2023/07/13 09:56:55 - mmengine - INFO - Exp name: ld_gfl_r18_r101_fpn_1x_vis_20230713_085933
2023/07/13 09:57:01 - mmengine - INFO - Epoch(train) [4][ 600/3139] lr: 1.2500e-03 eta: 2:29:17 time: 0.3266 data_time: 0.0045 memory: 748 loss: 1.6206 loss_cls: 0.3020 loss_bbox: 0.6347 loss_dfl: 0.2384 loss_ld: 0.4454
2023/07/13 09:57:17 - mmengine - INFO - Epoch(train) [4][ 650/3139] lr: 1.2500e-03 eta: 2:29:00 time: 0.3210 data_time: 0.0036 memory: 736 loss: 1.5812 loss_cls: 0.2834 loss_bbox: 0.5935 loss_dfl: 0.2254 loss_ld: 0.4789
2023/07/13 09:57:33 - mmengine - INFO - Epoch(train) [4][ 700/3139] lr: 1.2500e-03 eta: 2:28:44 time: 0.3233 data_time: 0.0047 memory: 716 loss: 1.5782 loss_cls: 0.3371 loss_bbox: 0.5906 loss_dfl: 0.2291 loss_ld: 0.4214
2023/07/13 09:57:49 - mmengine - INFO - Epoch(train) [4][ 750/3139] lr: 1.2500e-03 eta: 2:28:28 time: 0.3246 data_time: 0.0043 memory: 734 loss: 1.7681 loss_cls: 0.2940 loss_bbox: 0.6413 loss_dfl: 0.2435 loss_ld: 0.5893
2023/07/13 09:58:05 - mmengine - INFO - Epoch(train) [4][ 800/3139] lr: 1.2500e-03 eta: 2:28:11 time: 0.3224 data_time: 0.0044 memory: 731 loss: 1.6285 loss_cls: 0.2880 loss_bbox: 0.6338 loss_dfl: 0.2335 loss_ld: 0.4731
2023/07/13 09:58:21 - mmengine - INFO - Epoch(train) [4][ 850/3139] lr: 1.2500e-03 eta: 2:27:54 time: 0.3178 data_time: 0.0036 memory: 723 loss: 1.4931 loss_cls: 0.2853 loss_bbox: 0.6277 loss_dfl: 0.2149 loss_ld: 0.3651
2023/07/13 09:58:37 - mmengine - INFO - Epoch(train) [4][ 900/3139] lr: 1.2500e-03 eta: 2:27:38 time: 0.3226 data_time: 0.0034 memory: 724 loss: 1.6793 loss_cls: 0.2839 loss_bbox: 0.6440 loss_dfl: 0.2423 loss_ld: 0.5091
2023/07/13 09:58:54 - mmengine - INFO - Epoch(train) [4][ 950/3139] lr: 1.2500e-03 eta: 2:27:22 time: 0.3253 data_time: 0.0037 memory: 726 loss: 1.7256 loss_cls: 0.3178 loss_bbox: 0.6403 loss_dfl: 0.2459 loss_ld: 0.5216
2023/07/13 09:59:10 - mmengine - INFO - Epoch(train) [4][1000/3139] lr: 1.2500e-03 eta: 2:27:06 time: 0.3235 data_time: 0.0036 memory: 761 loss: 1.6828 loss_cls: 0.2971 loss_bbox: 0.6434 loss_dfl: 0.2328 loss_ld: 0.5095
2023/07/13 09:59:26 - mmengine - INFO - Epoch(train) [4][1050/3139] lr: 1.2500e-03 eta: 2:26:49 time: 0.3238 data_time: 0.0041 memory: 730 loss: 1.5927 loss_cls: 0.3012 loss_bbox: 0.5875 loss_dfl: 0.2294 loss_ld: 0.4746
2023/07/13 09:59:42 - mmengine - INFO - Epoch(train) [4][1100/3139] lr: 1.2500e-03 eta: 2:26:33 time: 0.3229 data_time: 0.0036 memory: 722 loss: 1.7036 loss_cls: 0.2863 loss_bbox: 0.6579 loss_dfl: 0.2439 loss_ld: 0.5155
2023/07/13 09:59:58 - mmengine - INFO - Epoch(train) [4][1150/3139] lr: 1.2500e-03 eta: 2:26:17 time: 0.3234 data_time: 0.0036 memory: 731 loss: 1.7146 loss_cls: 0.2923 loss_bbox: 0.6351 loss_dfl: 0.2355 loss_ld: 0.5517
2023/07/13 10:00:14 - mmengine - INFO - Epoch(train) [4][1200/3139] lr: 1.2500e-03 eta: 2:26:01 time: 0.3231 data_time: 0.0039 memory: 735 loss: 1.6105 loss_cls: 0.3002 loss_bbox: 0.6477 loss_dfl: 0.2305 loss_ld: 0.4322
2023/07/13 10:00:31 - mmengine - INFO - Epoch(train) [4][1250/3139] lr: 1.2500e-03 eta: 2:25:44 time: 0.3223 data_time: 0.0038 memory: 738 loss: 1.4955 loss_cls: 0.2861 loss_bbox: 0.5842 loss_dfl: 0.2179 loss_ld: 0.4073
2023/07/13 10:00:47 - mmengine - INFO - Epoch(train) [4][1300/3139] lr: 1.2500e-03 eta: 2:25:28 time: 0.3223 data_time: 0.0036 memory: 718 loss: 1.5994 loss_cls: 0.2946 loss_bbox: 0.6317 loss_dfl: 0.2308 loss_ld: 0.4423
2023/07/13 10:01:03 - mmengine - INFO - Epoch(train) [4][1350/3139] lr: 1.2500e-03 eta: 2:25:11 time: 0.3210 data_time: 0.0037 memory: 717 loss: 1.5460 loss_cls: 0.3087 loss_bbox: 0.6060 loss_dfl: 0.2235 loss_ld: 0.4077
2023/07/13 10:01:19 - mmengine - INFO - Epoch(train) [4][1400/3139] lr: 1.2500e-03 eta: 2:24:55 time: 0.3250 data_time: 0.0034 memory: 720 loss: 1.6103 loss_cls: 0.3110 loss_bbox: 0.6020 loss_dfl: 0.2288 loss_ld: 0.4685
2023/07/13 10:01:35 - mmengine - INFO - Epoch(train) [4][1450/3139] lr: 1.2500e-03 eta: 2:24:39 time: 0.3202 data_time: 0.0045 memory: 738 loss: 1.6203 loss_cls: 0.3007 loss_bbox: 0.6013 loss_dfl: 0.2261 loss_ld: 0.4921
2023/07/13 10:01:51 - mmengine - INFO - Epoch(train) [4][1500/3139] lr: 1.2500e-03 eta: 2:24:22 time: 0.3196 data_time: 0.0040 memory: 718 loss: 1.6609 loss_cls: 0.2998 loss_bbox: 0.6037 loss_dfl: 0.2358 loss_ld: 0.5216
2023/07/13 10:02:07 - mmengine - INFO - Epoch(train) [4][1550/3139] lr: 1.2500e-03 eta: 2:24:06 time: 0.3238 data_time: 0.0046 memory: 719 loss: 1.5864 loss_cls: 0.3355 loss_bbox: 0.6165 loss_dfl: 0.2303 loss_ld: 0.4042
2023/07/13 10:02:18 - mmengine - INFO - Exp name: ld_gfl_r18_r101_fpn_1x_vis_20230713_085933
2023/07/13 10:02:23 - mmengine - INFO - Epoch(train) [4][1600/3139] lr: 1.2500e-03 eta: 2:23:49 time: 0.3226 data_time: 0.0048 memory: 731 loss: 1.5555 loss_cls: 0.2792 loss_bbox: 0.5924 loss_dfl: 0.2212 loss_ld: 0.4627
2023/07/13 10:02:39 - mmengine - INFO - Epoch(train) [4][1650/3139] lr: 1.2500e-03 eta: 2:23:32 time: 0.3179 data_time: 0.0034 memory: 724 loss: 1.6156 loss_cls: 0.2967 loss_bbox: 0.6349 loss_dfl: 0.2286 loss_ld: 0.4554
2023/07/13 10:02:55 - mmengine - INFO - Epoch(train) [4][1700/3139] lr: 1.2500e-03 eta: 2:23:16 time: 0.3193 data_time: 0.0037 memory: 730 loss: 1.6876 loss_cls: 0.2813 loss_bbox: 0.6413 loss_dfl: 0.2319 loss_ld: 0.5331
2023/07/13 10:03:11 - mmengine - INFO - Epoch(train) [4][1750/3139] lr: 1.2500e-03 eta: 2:22:59 time: 0.3215 data_time: 0.0038 memory: 751 loss: 1.6275 loss_cls: 0.2825 loss_bbox: 0.6073 loss_dfl: 0.2294 loss_ld: 0.5083
2023/07/13 10:03:27 - mmengine - INFO - Epoch(train) [4][1800/3139] lr: 1.2500e-03 eta: 2:22:43 time: 0.3243 data_time: 0.0036 memory: 718 loss: 1.5517 loss_cls: 0.3020 loss_bbox: 0.5795 loss_dfl: 0.2226 loss_ld: 0.4476
2023/07/13 10:03:43 - mmengine - INFO - Epoch(train) [4][1850/3139] lr: 1.2500e-03 eta: 2:22:26 time: 0.3194 data_time: 0.0038 memory: 731 loss: 1.5816 loss_cls: 0.3103 loss_bbox: 0.5761 loss_dfl: 0.2221 loss_ld: 0.4731
2023/07/13 10:04:00 - mmengine - INFO - Epoch(train) [4][1900/3139] lr: 1.2500e-03 eta: 2:22:10 time: 0.3222 data_time: 0.0037 memory: 730 loss: 1.6354 loss_cls: 0.3879 loss_bbox: 0.6260 loss_dfl: 0.2301 loss_ld: 0.3915
2023/07/13 10:04:16 - mmengine - INFO - Epoch(train) [4][1950/3139] lr: 1.2500e-03 eta: 2:21:54 time: 0.3208 data_time: 0.0040 memory: 719 loss: 1.5983 loss_cls: 0.3429 loss_bbox: 0.6295 loss_dfl: 0.2315 loss_ld: 0.3944
2023/07/13 10:04:32 - mmengine - INFO - Epoch(train) [4][2000/3139] lr: 1.2500e-03 eta: 2:21:37 time: 0.3241 data_time: 0.0034 memory: 722 loss: 1.6137 loss_cls: 0.2994 loss_bbox: 0.6279 loss_dfl: 0.2289 loss_ld: 0.4574
2023/07/13 10:04:48 - mmengine - INFO - Epoch(train) [4][2050/3139] lr: 1.2500e-03 eta: 2:21:21 time: 0.3256 data_time: 0.0039 memory: 727 loss: 1.6690 loss_cls: 0.2868 loss_bbox: 0.6604 loss_dfl: 0.2371 loss_ld: 0.4849
2023/07/13 10:05:04 - mmengine - INFO - Epoch(train) [4][2100/3139] lr: 1.2500e-03 eta: 2:21:05 time: 0.3257 data_time: 0.0049 memory: 717 loss: 1.6261 loss_cls: 0.2825 loss_bbox: 0.5595 loss_dfl: 0.2195 loss_ld: 0.5647
2023/07/13 10:05:21 - mmengine - INFO - Epoch(train) [4][2150/3139] lr: 1.2500e-03 eta: 2:20:49 time: 0.3235 data_time: 0.0040 memory: 733 loss: 1.6317 loss_cls: 0.2766 loss_bbox: 0.6097 loss_dfl: 0.2286 loss_ld: 0.5168
2023/07/13 10:05:37 - mmengine - INFO - Epoch(train) [4][2200/3139] lr: 1.2500e-03 eta: 2:20:33 time: 0.3238 data_time: 0.0040 memory: 728 loss: 1.5919 loss_cls: 0.2982 loss_bbox: 0.6204 loss_dfl: 0.2288 loss_ld: 0.4445
2023/07/13 10:05:53 - mmengine - INFO - Epoch(train) [4][2250/3139] lr: 1.2500e-03 eta: 2:20:17 time: 0.3222 data_time: 0.0040 memory: 729 loss: 1.5749 loss_cls: 0.2837 loss_bbox: 0.5812 loss_dfl: 0.2219 loss_ld: 0.4881
2023/07/13 10:06:09 - mmengine - INFO - Epoch(train) [4][2300/3139] lr: 1.2500e-03 eta: 2:20:01 time: 0.3271 data_time: 0.0053 memory: 737 loss: 1.6759 loss_cls: 0.3027 loss_bbox: 0.6070 loss_dfl: 0.2292 loss_ld: 0.5370
2023/07/13 10:06:25 - mmengine - INFO - Epoch(train) [4][2350/3139] lr: 1.2500e-03 eta: 2:19:45 time: 0.3239 data_time: 0.0037 memory: 722 loss: 1.6808 loss_cls: 0.2827 loss_bbox: 0.6676 loss_dfl: 0.2342 loss_ld: 0.4962
2023/07/13 10:06:42 - mmengine - INFO - Epoch(train) [4][2400/3139] lr: 1.2500e-03 eta: 2:19:29 time: 0.3251 data_time: 0.0052 memory: 718 loss: 1.6569 loss_cls: 0.2924 loss_bbox: 0.6039 loss_dfl: 0.2288 loss_ld: 0.5318
2023/07/13 10:06:58 - mmengine - INFO - Epoch(train) [4][2450/3139] lr: 1.2500e-03 eta: 2:19:12 time: 0.3215 data_time: 0.0037 memory: 724 loss: 1.5428 loss_cls: 0.2850 loss_bbox: 0.5973 loss_dfl: 0.2204 loss_ld: 0.4402
2023/07/13 10:07:14 - mmengine - INFO - Epoch(train) [4][2500/3139] lr: 1.2500e-03 eta: 2:18:56 time: 0.3249 data_time: 0.0038 memory: 725 loss: 1.5301 loss_cls: 0.3251 loss_bbox: 0.5905 loss_dfl: 0.2228 loss_ld: 0.3918
2023/07/13 10:07:30 - mmengine - INFO - Epoch(train) [4][2550/3139] lr: 1.2500e-03 eta: 2:18:40 time: 0.3256 data_time: 0.0040 memory: 722 loss: 1.6683 loss_cls: 0.2749 loss_bbox: 0.6364 loss_dfl: 0.2343 loss_ld: 0.5227
2023/07/13 10:07:41 - mmengine - INFO - Exp name: ld_gfl_r18_r101_fpn_1x_vis_20230713_085933
2023/07/13 10:07:46 - mmengine - INFO - Epoch(train) [4][2600/3139] lr: 1.2500e-03 eta: 2:18:24 time: 0.3229 data_time: 0.0043 memory: 721 loss: 1.6208 loss_cls: 0.3127 loss_bbox: 0.6382 loss_dfl: 0.2283 loss_ld: 0.4416
2023/07/13 10:08:03 - mmengine - INFO - Epoch(train) [4][2650/3139] lr: 1.2500e-03 eta: 2:18:07 time: 0.3208 data_time: 0.0039 memory: 720 loss: 1.6255 loss_cls: 0.3084 loss_bbox: 0.6243 loss_dfl: 0.2277 loss_ld: 0.4651
2023/07/13 10:08:19 - mmengine - INFO - Epoch(train) [4][2700/3139] lr: 1.2500e-03 eta: 2:17:51 time: 0.3246 data_time: 0.0041 memory: 728 loss: 1.5179 loss_cls: 0.2919 loss_bbox: 0.5658 loss_dfl: 0.2168 loss_ld: 0.4433
2023/07/13 10:08:35 - mmengine - INFO - Epoch(train) [4][2750/3139] lr: 1.2500e-03 eta: 2:17:35 time: 0.3239 data_time: 0.0041 memory: 722 loss: 1.5299 loss_cls: 0.2885 loss_bbox: 0.6058 loss_dfl: 0.2237 loss_ld: 0.4120
2023/07/13 10:08:51 - mmengine - INFO - Epoch(train) [4][2800/3139] lr: 1.2500e-03 eta: 2:17:19 time: 0.3243 data_time: 0.0037 memory: 718 loss: 1.5205 loss_cls: 0.3011 loss_bbox: 0.6071 loss_dfl: 0.2221 loss_ld: 0.3902
2023/07/13 10:09:07 - mmengine - INFO - Epoch(train) [4][2850/3139] lr: 1.2500e-03 eta: 2:17:03 time: 0.3261 data_time: 0.0049 memory: 720 loss: 1.6026 loss_cls: 0.2897 loss_bbox: 0.6093 loss_dfl: 0.2257 loss_ld: 0.4779
2023/07/13 10:09:23 - mmengine - INFO - Epoch(train) [4][2900/3139] lr: 1.2500e-03 eta: 2:16:46 time: 0.3188 data_time: 0.0036 memory: 728 loss: 1.6273 loss_cls: 0.3026 loss_bbox: 0.5709 loss_dfl: 0.2252 loss_ld: 0.5285
2023/07/13 10:09:40 - mmengine - INFO - Epoch(train) [4][2950/3139] lr: 1.2500e-03 eta: 2:16:30 time: 0.3228 data_time: 0.0041 memory: 718 loss: 1.5321 loss_cls: 0.3416 loss_bbox: 0.5831 loss_dfl: 0.2139 loss_ld: 0.3935
2023/07/13 10:09:56 - mmengine - INFO - Epoch(train) [4][3000/3139] lr: 1.2500e-03 eta: 2:16:14 time: 0.3226 data_time: 0.0041 memory: 714 loss: 1.6321 loss_cls: 0.3229 loss_bbox: 0.6716 loss_dfl: 0.2356 loss_ld: 0.4019
2023/07/13 10:10:12 - mmengine - INFO - Epoch(train) [4][3050/3139] lr: 1.2500e-03 eta: 2:15:58 time: 0.3224 data_time: 0.0046 memory: 724 loss: 1.4739 loss_cls: 0.2961 loss_bbox: 0.5578 loss_dfl: 0.2148 loss_ld: 0.4053
2023/07/13 10:10:28 - mmengine - INFO - Epoch(train) [4][3100/3139] lr: 1.2500e-03 eta: 2:15:41 time: 0.3246 data_time: 0.0043 memory: 714 loss: 1.4723 loss_cls: 0.3252 loss_bbox: 0.5380 loss_dfl: 0.2103 loss_ld: 0.3989
2023/07/13 10:10:41 - mmengine - INFO - Exp name: ld_gfl_r18_r101_fpn_1x_vis_20230713_085933
2023/07/13 10:10:41 - mmengine - INFO - Saving checkpoint at 4 epochs
2023/07/13 10:10:47 - mmengine - INFO - Epoch(val) [4][ 50/548] eta: 0:00:37 time: 0.0755 data_time: 0.0022 memory: 717
2023/07/13 10:10:51 - mmengine - INFO - Epoch(val) [4][100/548] eta: 0:00:33 time: 0.0739 data_time: 0.0014 memory: 497
2023/07/13 10:10:54 - mmengine - INFO - Epoch(val) [4][150/548] eta: 0:00:29 time: 0.0740 data_time: 0.0013 memory: 497
2023/07/13 10:10:58 - mmengine - INFO - Epoch(val) [4][200/548] eta: 0:00:25 time: 0.0734 data_time: 0.0013 memory: 497
2023/07/13 10:11:02 - mmengine - INFO - Epoch(val) [4][250/548] eta: 0:00:22 time: 0.0746 data_time: 0.0014 memory: 497
2023/07/13 10:11:06 - mmengine - INFO - Epoch(val) [4][300/548] eta: 0:00:18 time: 0.0797 data_time: 0.0015 memory: 497
2023/07/13 10:11:10 - mmengine - INFO - Epoch(val) [4][350/548] eta: 0:00:15 time: 0.0802 data_time: 0.0015 memory: 497
2023/07/13 10:11:14 - mmengine - INFO - Epoch(val) [4][400/548] eta: 0:00:11 time: 0.0801 data_time: 0.0017 memory: 497
2023/07/13 10:11:18 - mmengine - INFO - Epoch(val) [4][450/548] eta: 0:00:07 time: 0.0816 data_time: 0.0016 memory: 497
2023/07/13 10:11:22 - mmengine - INFO - Epoch(val) [4][500/548] eta: 0:00:03 time: 0.0801 data_time: 0.0015 memory: 497
2023/07/13 10:11:27 - mmengine - INFO - Evaluating bbox...
2023/07/13 10:11:39 - mmengine - INFO - bbox_mAP_copypaste: 0.066 0.117 0.064 0.018 0.098 0.186
2023/07/13 10:11:39 - mmengine - INFO - Epoch(val) [4][548/548] coco/bbox_mAP: 0.0660 coco/bbox_mAP_50: 0.1170 coco/bbox_mAP_75: 0.0640 coco/bbox_mAP_s: 0.0180 coco/bbox_mAP_m: 0.0980 coco/bbox_mAP_l: 0.1860 data_time: 0.0015 time: 0.0775
2023/07/13 10:11:55 - mmengine - INFO - Epoch(train) [5][ 50/3139] lr: 1.2500e-03 eta: 2:15:13 time: 0.3233 data_time: 0.0052 memory: 719 loss: 1.5870 loss_cls: 0.3097 loss_bbox: 0.5917 loss_dfl: 0.2234 loss_ld: 0.4623
2023/07/13 10:12:11 - mmengine - INFO - Epoch(train) [5][ 100/3139] lr: 1.2500e-03 eta: 2:14:56 time: 0.3238 data_time: 0.0040 memory: 725 loss: 1.6450 loss_cls: 0.2861 loss_bbox: 0.6141 loss_dfl: 0.2267 loss_ld: 0.5181
2023/07/13 10:12:27 - mmengine - INFO - Epoch(train) [5][ 150/3139] lr: 1.2500e-03 eta: 2:14:40 time: 0.3259 data_time: 0.0045 memory: 725 loss: 1.5837 loss_cls: 0.2831 loss_bbox: 0.5994 loss_dfl: 0.2206 loss_ld: 0.4806
2023/07/13 10:12:44 - mmengine - INFO - Epoch(train) [5][ 200/3139] lr: 1.2500e-03 eta: 2:14:24 time: 0.3239 data_time: 0.0034 memory: 751 loss: 1.6039 loss_cls: 0.3022 loss_bbox: 0.6138 loss_dfl: 0.2296 loss_ld: 0.4583
2023/07/13 10:13:00 - mmengine - INFO - Epoch(train) [5][ 250/3139] lr: 1.2500e-03 eta: 2:14:08 time: 0.3196 data_time: 0.0037 memory: 721 loss: 1.5543 loss_cls: 0.3430 loss_bbox: 0.5877 loss_dfl: 0.2187 loss_ld: 0.4049
2023/07/13 10:13:16 - mmengine - INFO - Epoch(train) [5][ 300/3139] lr: 1.2500e-03 eta: 2:13:51 time: 0.3227 data_time: 0.0035 memory: 726 loss: 1.4466 loss_cls: 0.2904 loss_bbox: 0.5653 loss_dfl: 0.2076 loss_ld: 0.3833
2023/07/13 10:13:32 - mmengine - INFO - Epoch(train) [5][ 350/3139] lr: 1.2500e-03 eta: 2:13:35 time: 0.3240 data_time: 0.0037 memory: 720 loss: 1.5930 loss_cls: 0.3079 loss_bbox: 0.5842 loss_dfl: 0.2287 loss_ld: 0.4723
2023/07/13 10:13:48 - mmengine - INFO - Epoch(train) [5][ 400/3139] lr: 1.2500e-03 eta: 2:13:19 time: 0.3226 data_time: 0.0041 memory: 733 loss: 1.5866 loss_cls: 0.3077 loss_bbox: 0.5805 loss_dfl: 0.2263 loss_ld: 0.4721
2023/07/13 10:14:02 - mmengine - INFO - Exp name: ld_gfl_r18_r101_fpn_1x_vis_20230713_085933
2023/07/13 10:14:04 - mmengine - INFO - Epoch(train) [5][ 450/3139] lr: 1.2500e-03 eta: 2:13:03 time: 0.3223 data_time: 0.0041 memory: 726 loss: 1.5262 loss_cls: 0.3127 loss_bbox: 0.5712 loss_dfl: 0.2152 loss_ld: 0.4271
2023/07/13 10:14:20 - mmengine - INFO - Epoch(train) [5][ 500/3139] lr: 1.2500e-03 eta: 2:12:46 time: 0.3227 data_time: 0.0040 memory: 720 loss: 1.5950 loss_cls: 0.2993 loss_bbox: 0.5980 loss_dfl: 0.2258 loss_ld: 0.4719
2023/07/13 10:14:36 - mmengine - INFO - Epoch(train) [5][ 550/3139] lr: 1.2500e-03 eta: 2:12:30 time: 0.3222 data_time: 0.0036 memory: 720 loss: 1.5205 loss_cls: 0.2911 loss_bbox: 0.6022 loss_dfl: 0.2158 loss_ld: 0.4114
2023/07/13 10:14:53 - mmengine - INFO - Epoch(train) [5][ 600/3139] lr: 1.2500e-03 eta: 2:12:14 time: 0.3273 data_time: 0.0047 memory: 728 loss: 1.5082 loss_cls: 0.2707 loss_bbox: 0.5892 loss_dfl: 0.2108 loss_ld: 0.4375
2023/07/13 10:15:09 - mmengine - INFO - Epoch(train) [5][ 650/3139] lr: 1.2500e-03 eta: 2:11:58 time: 0.3219 data_time: 0.0042 memory: 716 loss: 1.5426 loss_cls: 0.3245 loss_bbox: 0.5844 loss_dfl: 0.2186 loss_ld: 0.4152
2023/07/13 10:15:25 - mmengine - INFO - Epoch(train) [5][ 700/3139] lr: 1.2500e-03 eta: 2:11:42 time: 0.3245 data_time: 0.0040 memory: 720 loss: 1.5275 loss_cls: 0.2948 loss_bbox: 0.5751 loss_dfl: 0.2207 loss_ld: 0.4370
2023/07/13 10:15:41 - mmengine - INFO - Epoch(train) [5][ 750/3139] lr: 1.2500e-03 eta: 2:11:25 time: 0.3230 data_time: 0.0039 memory: 718 loss: 1.5166 loss_cls: 0.2733 loss_bbox: 0.6071 loss_dfl: 0.2247 loss_ld: 0.4115
2023/07/13 10:15:57 - mmengine - INFO - Epoch(train) [5][ 800/3139] lr: 1.2500e-03 eta: 2:11:09 time: 0.3235 data_time: 0.0037 memory: 718 loss: 1.5252 loss_cls: 0.3147 loss_bbox: 0.5810 loss_dfl: 0.2234 loss_ld: 0.4061
2023/07/13 10:16:14 - mmengine - INFO - Epoch(train) [5][ 850/3139] lr: 1.2500e-03 eta: 2:10:53 time: 0.3227 data_time: 0.0042 memory: 733 loss: 1.6378 loss_cls: 0.2573 loss_bbox: 0.6580 loss_dfl: 0.2382 loss_ld: 0.4843
2023/07/13 10:16:30 - mmengine - INFO - Epoch(train) [5][ 900/3139] lr: 1.2500e-03 eta: 2:10:37 time: 0.3222 data_time: 0.0041 memory: 725 loss: 1.5498 loss_cls: 0.2875 loss_bbox: 0.5715 loss_dfl: 0.2130 loss_ld: 0.4778
2023/07/13 10:16:46 - mmengine - INFO - Epoch(train) [5][ 950/3139] lr: 1.2500e-03 eta: 2:10:21 time: 0.3240 data_time: 0.0042 memory: 725 loss: 1.5275 loss_cls: 0.2808 loss_bbox: 0.5979 loss_dfl: 0.2199 loss_ld: 0.4288
2023/07/13 10:17:02 - mmengine - INFO - Epoch(train) [5][1000/3139] lr: 1.2500e-03 eta: 2:10:04 time: 0.3238 data_time: 0.0039 memory: 716 loss: 1.6419 loss_cls: 0.2687 loss_bbox: 0.5576 loss_dfl: 0.2245 loss_ld: 0.5910
2023/07/13 10:17:18 - mmengine - INFO - Epoch(train) [5][1050/3139] lr: 1.2500e-03 eta: 2:09:48 time: 0.3235 data_time: 0.0040 memory: 722 loss: 1.5864 loss_cls: 0.2661 loss_bbox: 0.6137 loss_dfl: 0.2259 loss_ld: 0.4806
2023/07/13 10:17:34 - mmengine - INFO - Epoch(train) [5][1100/3139] lr: 1.2500e-03 eta: 2:09:32 time: 0.3229 data_time: 0.0044 memory: 738 loss: 1.5210 loss_cls: 0.2852 loss_bbox: 0.5749 loss_dfl: 0.2200 loss_ld: 0.4410
2023/07/13 10:17:51 - mmengine - INFO - Epoch(train) [5][1150/3139] lr: 1.2500e-03 eta: 2:09:16 time: 0.3260 data_time: 0.0046 memory: 719 loss: 1.4864 loss_cls: 0.3133 loss_bbox: 0.5542 loss_dfl: 0.2194 loss_ld: 0.3995
2023/07/13 10:18:07 - mmengine - INFO - Epoch(train) [5][1200/3139] lr: 1.2500e-03 eta: 2:09:00 time: 0.3232 data_time: 0.0036 memory: 726 loss: 1.5272 loss_cls: 0.2885 loss_bbox: 0.5814 loss_dfl: 0.2172 loss_ld: 0.4401
2023/07/13 10:18:23 - mmengine - INFO - Epoch(train) [5][1250/3139] lr: 1.2500e-03 eta: 2:08:43 time: 0.3220 data_time: 0.0040 memory: 730 loss: 1.4830 loss_cls: 0.3102 loss_bbox: 0.5841 loss_dfl: 0.2209 loss_ld: 0.3677
2023/07/13 10:18:39 - mmengine - INFO - Epoch(train) [5][1300/3139] lr: 1.2500e-03 eta: 2:08:27 time: 0.3257 data_time: 0.0046 memory: 719 loss: 1.6614 loss_cls: 0.2799 loss_bbox: 0.6400 loss_dfl: 0.2401 loss_ld: 0.5014
2023/07/13 10:18:56 - mmengine - INFO - Epoch(train) [5][1350/3139] lr: 1.2500e-03 eta: 2:08:11 time: 0.3263 data_time: 0.0045 memory: 731 loss: 1.6498 loss_cls: 0.3143 loss_bbox: 0.6270 loss_dfl: 0.2318 loss_ld: 0.4768
2023/07/13 10:19:12 - mmengine - INFO - Epoch(train) [5][1400/3139] lr: 1.2500e-03 eta: 2:07:55 time: 0.3257 data_time: 0.0040 memory: 734 loss: 1.5465 loss_cls: 0.2991 loss_bbox: 0.6105 loss_dfl: 0.2219 loss_ld: 0.4151
2023/07/13 10:19:26 - mmengine - INFO - Exp name: ld_gfl_r18_r101_fpn_1x_vis_20230713_085933
2023/07/13 10:19:28 - mmengine - INFO - Epoch(train) [5][1450/3139] lr: 1.2500e-03 eta: 2:07:39 time: 0.3246 data_time: 0.0049 memory: 749 loss: 1.5501 loss_cls: 0.3036 loss_bbox: 0.6085 loss_dfl: 0.2237 loss_ld: 0.4142
2023/07/13 10:19:44 - mmengine - INFO - Epoch(train) [5][1500/3139] lr: 1.2500e-03 eta: 2:07:23 time: 0.3245 data_time: 0.0034 memory: 725 loss: 1.6720 loss_cls: 0.2803 loss_bbox: 0.6502 loss_dfl: 0.2337 loss_ld: 0.5077
2023/07/13 10:20:01 - mmengine - INFO - Epoch(train) [5][1550/3139] lr: 1.2500e-03 eta: 2:07:07 time: 0.3231 data_time: 0.0036 memory: 761 loss: 1.6321 loss_cls: 0.2847 loss_bbox: 0.6272 loss_dfl: 0.2342 loss_ld: 0.4860
2023/07/13 10:20:17 - mmengine - INFO - Epoch(train) [5][1600/3139] lr: 1.2500e-03 eta: 2:06:51 time: 0.3229 data_time: 0.0039 memory: 727 loss: 1.5962 loss_cls: 0.2804 loss_bbox: 0.6020 loss_dfl: 0.2286 loss_ld: 0.4851
2023/07/13 10:20:33 - mmengine - INFO - Epoch(train) [5][1650/3139] lr: 1.2500e-03 eta: 2:06:35 time: 0.3244 data_time: 0.0038 memory: 734 loss: 1.5460 loss_cls: 0.3222 loss_bbox: 0.5528 loss_dfl: 0.2215 loss_ld: 0.4495
2023/07/13 10:20:49 - mmengine - INFO - Epoch(train) [5][1700/3139] lr: 1.2500e-03 eta: 2:06:19 time: 0.3283 data_time: 0.0059 memory: 731 loss: 1.5154 loss_cls: 0.3046 loss_bbox: 0.5546 loss_dfl: 0.2147 loss_ld: 0.4415
2023/07/13 10:21:06 - mmengine - INFO - Epoch(train) [5][1750/3139] lr: 1.2500e-03 eta: 2:06:03 time: 0.3254 data_time: 0.0040 memory: 731 loss: 1.5247 loss_cls: 0.2744 loss_bbox: 0.5852 loss_dfl: 0.2206 loss_ld: 0.4445
2023/07/13 10:21:22 - mmengine - INFO - Epoch(train) [5][1800/3139] lr: 1.2500e-03 eta: 2:05:46 time: 0.3225 data_time: 0.0041 memory: 715 loss: 1.4612 loss_cls: 0.2821 loss_bbox: 0.5300 loss_dfl: 0.2123 loss_ld: 0.4368
2023/07/13 10:21:38 - mmengine - INFO - Epoch(train) [5][1850/3139] lr: 1.2500e-03 eta: 2:05:30 time: 0.3241 data_time: 0.0041 memory: 721 loss: 1.5141 loss_cls: 0.3028 loss_bbox: 0.5729 loss_dfl: 0.2155 loss_ld: 0.4229
2023/07/13 10:21:54 - mmengine - INFO - Epoch(train) [5][1900/3139] lr: 1.2500e-03 eta: 2:05:14 time: 0.3237 data_time: 0.0040 memory: 730 loss: 1.6111 loss_cls: 0.2899 loss_bbox: 0.5888 loss_dfl: 0.2232 loss_ld: 0.5092
2023/07/13 10:22:10 - mmengine - INFO - Epoch(train) [5][1950/3139] lr: 1.2500e-03 eta: 2:04:58 time: 0.3228 data_time: 0.0040 memory: 723 loss: 1.4404 loss_cls: 0.3022 loss_bbox: 0.5509 loss_dfl: 0.2034 loss_ld: 0.3839
2023/07/13 10:22:26 - mmengine - INFO - Epoch(train) [5][2000/3139] lr: 1.2500e-03 eta: 2:04:42 time: 0.3238 data_time: 0.0044 memory: 728 loss: 1.5306 loss_cls: 0.2899 loss_bbox: 0.5833 loss_dfl: 0.2190 loss_ld: 0.4384
2023/07/13 10:22:43 - mmengine - INFO - Epoch(train) [5][2050/3139] lr: 1.2500e-03 eta: 2:04:25 time: 0.3246 data_time: 0.0036 memory: 724 loss: 1.5170 loss_cls: 0.3259 loss_bbox: 0.5574 loss_dfl: 0.2209 loss_ld: 0.4129
2023/07/13 10:22:59 - mmengine - INFO - Epoch(train) [5][2100/3139] lr: 1.2500e-03 eta: 2:04:10 time: 0.3265 data_time: 0.0042 memory: 723 loss: 1.5098 loss_cls: 0.2768 loss_bbox: 0.5936 loss_dfl: 0.2115 loss_ld: 0.4279
2023/07/13 10:23:15 - mmengine - INFO - Epoch(train) [5][2150/3139] lr: 1.2500e-03 eta: 2:03:53 time: 0.3202 data_time: 0.0037 memory: 723 loss: 1.4869 loss_cls: 0.2665 loss_bbox: 0.6001 loss_dfl: 0.2098 loss_ld: 0.4105
2023/07/13 10:23:31 - mmengine - INFO - Epoch(train) [5][2200/3139] lr: 1.2500e-03 eta: 2:03:37 time: 0.3221 data_time: 0.0034 memory: 722 loss: 1.4931 loss_cls: 0.2821 loss_bbox: 0.5613 loss_dfl: 0.2140 loss_ld: 0.4357
2023/07/13 10:23:47 - mmengine - INFO - Epoch(train) [5][2250/3139] lr: 1.2500e-03 eta: 2:03:20 time: 0.3208 data_time: 0.0041 memory: 723 loss: 1.4007 loss_cls: 0.2801 loss_bbox: 0.5559 loss_dfl: 0.2061 loss_ld: 0.3586
2023/07/13 10:24:03 - mmengine - INFO - Epoch(train) [5][2300/3139] lr: 1.2500e-03 eta: 2:03:04 time: 0.3251 data_time: 0.0047 memory: 735 loss: 1.5600 loss_cls: 0.2937 loss_bbox: 0.5785 loss_dfl: 0.2236 loss_ld: 0.4641
2023/07/13 10:24:20 - mmengine - INFO - Epoch(train) [5][2350/3139] lr: 1.2500e-03 eta: 2:02:48 time: 0.3214 data_time: 0.0045 memory: 736 loss: 1.4759 loss_cls: 0.2939 loss_bbox: 0.5487 loss_dfl: 0.2090 loss_ld: 0.4243
2023/07/13 10:24:36 - mmengine - INFO - Epoch(train) [5][2400/3139] lr: 1.2500e-03 eta: 2:02:32 time: 0.3224 data_time: 0.0034 memory: 743 loss: 1.5816 loss_cls: 0.2834 loss_bbox: 0.5899 loss_dfl: 0.2219 loss_ld: 0.4864
2023/07/13 10:24:50 - mmengine - INFO - Exp name: ld_gfl_r18_r101_fpn_1x_vis_20230713_085933
2023/07/13 10:24:52 - mmengine - INFO - Epoch(train) [5][2450/3139] lr: 1.2500e-03 eta: 2:02:16 time: 0.3258 data_time: 0.0046 memory: 739 loss: 1.3679 loss_cls: 0.2983 loss_bbox: 0.5262 loss_dfl: 0.2057 loss_ld: 0.3378
2023/07/13 10:25:08 - mmengine - INFO - Epoch(train) [5][2500/3139] lr: 1.2500e-03 eta: 2:01:59 time: 0.3237 data_time: 0.0035 memory: 724 loss: 1.5274 loss_cls: 0.2788 loss_bbox: 0.5908 loss_dfl: 0.2226 loss_ld: 0.4352
2023/07/13 10:25:24 - mmengine - INFO - Epoch(train) [5][2550/3139] lr: 1.2500e-03 eta: 2:01:43 time: 0.3241 data_time: 0.0040 memory: 722 loss: 1.5777 loss_cls: 0.2909 loss_bbox: 0.5880 loss_dfl: 0.2225 loss_ld: 0.4763
2023/07/13 10:25:41 - mmengine - INFO - Epoch(train) [5][2600/3139] lr: 1.2500e-03 eta: 2:01:27 time: 0.3277 data_time: 0.0057 memory: 718 loss: 1.4542 loss_cls: 0.3093 loss_bbox: 0.5311 loss_dfl: 0.2134 loss_ld: 0.4004
2023/07/13 10:25:57 - mmengine - INFO - Epoch(train) [5][2650/3139] lr: 1.2500e-03 eta: 2:01:11 time: 0.3229 data_time: 0.0040 memory: 729 loss: 1.4725 loss_cls: 0.2797 loss_bbox: 0.5662 loss_dfl: 0.2178 loss_ld: 0.4088
2023/07/13 10:26:13 - mmengine - INFO - Epoch(train) [5][2700/3139] lr: 1.2500e-03 eta: 2:00:55 time: 0.3229 data_time: 0.0047 memory: 728 loss: 1.4876 loss_cls: 0.2955 loss_bbox: 0.5564 loss_dfl: 0.2133 loss_ld: 0.4225
2023/07/13 10:26:29 - mmengine - INFO - Epoch(train) [5][2750/3139] lr: 1.2500e-03 eta: 2:00:38 time: 0.3203 data_time: 0.0041 memory: 720 loss: 1.5981 loss_cls: 0.2754 loss_bbox: 0.6745 loss_dfl: 0.2396 loss_ld: 0.4086
2023/07/13 10:26:45 - mmengine - INFO - Epoch(train) [5][2800/3139] lr: 1.2500e-03 eta: 2:00:22 time: 0.3224 data_time: 0.0042 memory: 739 loss: 1.5540 loss_cls: 0.2639 loss_bbox: 0.5992 loss_dfl: 0.2181 loss_ld: 0.4727
2023/07/13 10:27:01 - mmengine - INFO - Epoch(train) [5][2850/3139] lr: 1.2500e-03 eta: 2:00:06 time: 0.3263 data_time: 0.0040 memory: 723 loss: 1.5763 loss_cls: 0.2890 loss_bbox: 0.5501 loss_dfl: 0.2181 loss_ld: 0.5191
2023/07/13 10:27:18 - mmengine - INFO - Epoch(train) [5][2900/3139] lr: 1.2500e-03 eta: 1:59:50 time: 0.3209 data_time: 0.0035 memory: 717 loss: 1.4625 loss_cls: 0.2731 loss_bbox: 0.5449 loss_dfl: 0.2128 loss_ld: 0.4317
2023/07/13 10:27:34 - mmengine - INFO - Epoch(train) [5][2950/3139] lr: 1.2500e-03 eta: 1:59:34 time: 0.3244 data_time: 0.0036 memory: 746 loss: 1.4578 loss_cls: 0.2799 loss_bbox: 0.5559 loss_dfl: 0.2129 loss_ld: 0.4091
2023/07/13 10:27:50 - mmengine - INFO - Epoch(train) [5][3000/3139] lr: 1.2500e-03 eta: 1:59:17 time: 0.3208 data_time: 0.0042 memory: 724 loss: 1.4898 loss_cls: 0.3076 loss_bbox: 0.6186 loss_dfl: 0.2258 loss_ld: 0.3378
2023/07/13 10:28:06 - mmengine - INFO - Epoch(train) [5][3050/3139] lr: 1.2500e-03 eta: 1:59:01 time: 0.3250 data_time: 0.0045 memory: 722 loss: 1.4977 loss_cls: 0.2848 loss_bbox: 0.5942 loss_dfl: 0.2154 loss_ld: 0.4033
2023/07/13 10:28:22 - mmengine - INFO - Epoch(train) [5][3100/3139] lr: 1.2500e-03 eta: 1:58:45 time: 0.3218 data_time: 0.0040 memory: 717 loss: 1.4411 loss_cls: 0.2774 loss_bbox: 0.5660 loss_dfl: 0.2111 loss_ld: 0.3866
2023/07/13 10:28:35 - mmengine - INFO - Exp name: ld_gfl_r18_r101_fpn_1x_vis_20230713_085933
2023/07/13 10:28:35 - mmengine - INFO - Saving checkpoint at 5 epochs
2023/07/13 10:28:41 - mmengine - INFO - Epoch(val) [5][ 50/548] eta: 0:00:37 time: 0.0746 data_time: 0.0022 memory: 722
2023/07/13 10:28:45 - mmengine - INFO - Epoch(val) [5][100/548] eta: 0:00:33 time: 0.0734 data_time: 0.0014 memory: 497
2023/07/13 10:28:48 - mmengine - INFO - Epoch(val) [5][150/548] eta: 0:00:29 time: 0.0737 data_time: 0.0013 memory: 497
2023/07/13 10:28:52 - mmengine - INFO - Epoch(val) [5][200/548] eta: 0:00:25 time: 0.0736 data_time: 0.0013 memory: 497
2023/07/13 10:28:56 - mmengine - INFO - Epoch(val) [5][250/548] eta: 0:00:21 time: 0.0737 data_time: 0.0013 memory: 497
2023/07/13 10:28:59 - mmengine - INFO - Epoch(val) [5][300/548] eta: 0:00:18 time: 0.0730 data_time: 0.0013 memory: 497
2023/07/13 10:29:03 - mmengine - INFO - Epoch(val) [5][350/548] eta: 0:00:14 time: 0.0730 data_time: 0.0014 memory: 497
2023/07/13 10:29:07 - mmengine - INFO - Epoch(val) [5][400/548] eta: 0:00:10 time: 0.0734 data_time: 0.0013 memory: 497
2023/07/13 10:29:10 - mmengine - INFO - Epoch(val) [5][450/548] eta: 0:00:07 time: 0.0743 data_time: 0.0014 memory: 497
2023/07/13 10:29:14 - mmengine - INFO - Epoch(val) [5][500/548] eta: 0:00:03 time: 0.0732 data_time: 0.0013 memory: 497
2023/07/13 10:29:18 - mmengine - INFO - Evaluating bbox...
2023/07/13 10:29:32 - mmengine - INFO - bbox_mAP_copypaste: 0.076 0.134 0.077 0.020 0.112 0.212
2023/07/13 10:29:32 - mmengine - INFO - Epoch(val) [5][548/548] coco/bbox_mAP: 0.0760 coco/bbox_mAP_50: 0.1340 coco/bbox_mAP_75: 0.0770 coco/bbox_mAP_s: 0.0200 coco/bbox_mAP_m: 0.1120 coco/bbox_mAP_l: 0.2120 data_time: 0.0014 time: 0.0735
2023/07/13 10:29:48 - mmengine - INFO - Epoch(train) [6][ 50/3139] lr: 1.2500e-03 eta: 1:58:16 time: 0.3255 data_time: 0.0055 memory: 718 loss: 1.5618 loss_cls: 0.3030 loss_bbox: 0.5504 loss_dfl: 0.2272 loss_ld: 0.4812
2023/07/13 10:30:05 - mmengine - INFO - Epoch(train) [6][ 100/3139] lr: 1.2500e-03 eta: 1:58:00 time: 0.3363 data_time: 0.0163 memory: 715 loss: 1.4490 loss_cls: 0.2706 loss_bbox: 0.5386 loss_dfl: 0.2074 loss_ld: 0.4324
2023/07/13 10:30:21 - mmengine - INFO - Epoch(train) [6][ 150/3139] lr: 1.2500e-03 eta: 1:57:44 time: 0.3239 data_time: 0.0045 memory: 739 loss: 1.4762 loss_cls: 0.3092 loss_bbox: 0.5758 loss_dfl: 0.2145 loss_ld: 0.3768
2023/07/13 10:30:37 - mmengine - INFO - Epoch(train) [6][ 200/3139] lr: 1.2500e-03 eta: 1:57:28 time: 0.3215 data_time: 0.0046 memory: 735 loss: 1.4730 loss_cls: 0.2697 loss_bbox: 0.5599 loss_dfl: 0.2178 loss_ld: 0.4256
2023/07/13 10:30:54 - mmengine - INFO - Epoch(train) [6][ 250/3139] lr: 1.2500e-03 eta: 1:57:12 time: 0.3239 data_time: 0.0059 memory: 726 loss: 1.5377 loss_cls: 0.2833 loss_bbox: 0.5929 loss_dfl: 0.2173 loss_ld: 0.4441
2023/07/13 10:31:10 - mmengine - INFO - Epoch(train) [6][ 300/3139] lr: 1.2500e-03 eta: 1:56:56 time: 0.3244 data_time: 0.0049 memory: 718 loss: 1.4510 loss_cls: 0.2759 loss_bbox: 0.5443 loss_dfl: 0.2118 loss_ld: 0.4189
2023/07/13 10:31:11 - mmengine - INFO - Exp name: ld_gfl_r18_r101_fpn_1x_vis_20230713_085933
2023/07/13 10:31:26 - mmengine - INFO - Epoch(train) [6][ 350/3139] lr: 1.2500e-03 eta: 1:56:40 time: 0.3248 data_time: 0.0046 memory: 719 loss: 1.5020 loss_cls: 0.2964 loss_bbox: 0.5755 loss_dfl: 0.2176 loss_ld: 0.4125
2023/07/13 10:31:42 - mmengine - INFO - Epoch(train) [6][ 400/3139] lr: 1.2500e-03 eta: 1:56:23 time: 0.3258 data_time: 0.0054 memory: 719 loss: 1.5070 loss_cls: 0.3138 loss_bbox: 0.5358 loss_dfl: 0.2118 loss_ld: 0.4456
2023/07/13 10:31:59 - mmengine - INFO - Epoch(train) [6][ 450/3139] lr: 1.2500e-03 eta: 1:56:07 time: 0.3245 data_time: 0.0044 memory: 728 loss: 1.4172 loss_cls: 0.2548 loss_bbox: 0.5908 loss_dfl: 0.2084 loss_ld: 0.3631
2023/07/13 10:32:15 - mmengine - INFO - Epoch(train) [6][ 500/3139] lr: 1.2500e-03 eta: 1:55:51 time: 0.3260 data_time: 0.0056 memory: 723 loss: 1.5151 loss_cls: 0.2702 loss_bbox: 0.5687 loss_dfl: 0.2127 loss_ld: 0.4635
2023/07/13 10:32:31 - mmengine - INFO - Epoch(train) [6][ 550/3139] lr: 1.2500e-03 eta: 1:55:35 time: 0.3225 data_time: 0.0037 memory: 716 loss: 1.4598 loss_cls: 0.3019 loss_bbox: 0.5408 loss_dfl: 0.2196 loss_ld: 0.3975
2023/07/13 10:32:47 - mmengine - INFO - Epoch(train) [6][ 600/3139] lr: 1.2500e-03 eta: 1:55:19 time: 0.3244 data_time: 0.0041 memory: 752 loss: 1.3938 loss_cls: 0.2829 loss_bbox: 0.5321 loss_dfl: 0.1991 loss_ld: 0.3797
2023/07/13 10:33:03 - mmengine - INFO - Epoch(train) [6][ 650/3139] lr: 1.2500e-03 eta: 1:55:03 time: 0.3235 data_time: 0.0041 memory: 719 loss: 1.4508 loss_cls: 0.3349 loss_bbox: 0.5523 loss_dfl: 0.2130 loss_ld: 0.3506
2023/07/13 10:33:20 - mmengine - INFO - Epoch(train) [6][ 700/3139] lr: 1.2500e-03 eta: 1:54:47 time: 0.3254 data_time: 0.0049 memory: 720 loss: 1.5036 loss_cls: 0.2665 loss_bbox: 0.5783 loss_dfl: 0.2118 loss_ld: 0.4469
2023/07/13 10:33:36 - mmengine - INFO - Epoch(train) [6][ 750/3139] lr: 1.2500e-03 eta: 1:54:30 time: 0.3223 data_time: 0.0035 memory: 734 loss: 1.5234 loss_cls: 0.2776 loss_bbox: 0.6122 loss_dfl: 0.2248 loss_ld: 0.4088
2023/07/13 10:33:52 - mmengine - INFO - Epoch(train) [6][ 800/3139] lr: 1.2500e-03 eta: 1:54:14 time: 0.3269 data_time: 0.0046 memory: 735 loss: 1.4862 loss_cls: 0.3002 loss_bbox: 0.5691 loss_dfl: 0.2206 loss_ld: 0.3963
2023/07/13 10:34:08 - mmengine - INFO - Epoch(train) [6][ 850/3139] lr: 1.2500e-03 eta: 1:53:58 time: 0.3233 data_time: 0.0035 memory: 723 loss: 1.4806 loss_cls: 0.2956 loss_bbox: 0.5461 loss_dfl: 0.2146 loss_ld: 0.4244
2023/07/13 10:34:25 - mmengine - INFO - Epoch(train) [6][ 900/3139] lr: 1.2500e-03 eta: 1:53:42 time: 0.3234 data_time: 0.0039 memory: 727 loss: 1.4754 loss_cls: 0.2752 loss_bbox: 0.5859 loss_dfl: 0.2192 loss_ld: 0.3950
2023/07/13 10:34:41 - mmengine - INFO - Epoch(train) [6][ 950/3139] lr: 1.2500e-03 eta: 1:53:26 time: 0.3227 data_time: 0.0037 memory: 718 loss: 1.4499 loss_cls: 0.2701 loss_bbox: 0.6056 loss_dfl: 0.2184 loss_ld: 0.3558
2023/07/13 10:34:57 - mmengine - INFO - Epoch(train) [6][1000/3139] lr: 1.2500e-03 eta: 1:53:09 time: 0.3218 data_time: 0.0040 memory: 723 loss: 1.4863 loss_cls: 0.3108 loss_bbox: 0.5472 loss_dfl: 0.2163 loss_ld: 0.4120
2023/07/13 10:35:13 - mmengine - INFO - Epoch(train) [6][1050/3139] lr: 1.2500e-03 eta: 1:52:53 time: 0.3269 data_time: 0.0049 memory: 724 loss: 1.5582 loss_cls: 0.2787 loss_bbox: 0.6120 loss_dfl: 0.2298 loss_ld: 0.4377
2023/07/13 10:35:29 - mmengine - INFO - Epoch(train) [6][1100/3139] lr: 1.2500e-03 eta: 1:52:37 time: 0.3248 data_time: 0.0038 memory: 736 loss: 1.4991 loss_cls: 0.3142 loss_bbox: 0.5664 loss_dfl: 0.2184 loss_ld: 0.4001
2023/07/13 10:35:46 - mmengine - INFO - Epoch(train) [6][1150/3139] lr: 1.2500e-03 eta: 1:52:21 time: 0.3237 data_time: 0.0044 memory: 718 loss: 1.4970 loss_cls: 0.2965 loss_bbox: 0.5799 loss_dfl: 0.2221 loss_ld: 0.3985
2023/07/13 10:36:02 - mmengine - INFO - Epoch(train) [6][1200/3139] lr: 1.2500e-03 eta: 1:52:05 time: 0.3224 data_time: 0.0038 memory: 722 loss: 1.5052 loss_cls: 0.2926 loss_bbox: 0.5439 loss_dfl: 0.2098 loss_ld: 0.4588
2023/07/13 10:36:18 - mmengine - INFO - Epoch(train) [6][1250/3139] lr: 1.2500e-03 eta: 1:51:49 time: 0.3286 data_time: 0.0052 memory: 738 loss: 1.5513 loss_cls: 0.2676 loss_bbox: 0.5861 loss_dfl: 0.2146 loss_ld: 0.4830
2023/07/13 10:36:34 - mmengine - INFO - Epoch(train) [6][1300/3139] lr: 1.2500e-03 eta: 1:51:32 time: 0.3180 data_time: 0.0038 memory: 733 loss: 1.3892 loss_cls: 0.3083 loss_bbox: 0.5267 loss_dfl: 0.2073 loss_ld: 0.3470
2023/07/13 10:36:36 - mmengine - INFO - Exp name: ld_gfl_r18_r101_fpn_1x_vis_20230713_085933
2023/07/13 10:36:50 - mmengine - INFO - Epoch(train) [6][1350/3139] lr: 1.2500e-03 eta: 1:51:16 time: 0.3210 data_time: 0.0043 memory: 722 loss: 1.4809 loss_cls: 0.3612 loss_bbox: 0.5595 loss_dfl: 0.2209 loss_ld: 0.3392
2023/07/13 10:37:06 - mmengine - INFO - Epoch(train) [6][1400/3139] lr: 1.2500e-03 eta: 1:51:00 time: 0.3244 data_time: 0.0042 memory: 731 loss: 1.5203 loss_cls: 0.3002 loss_bbox: 0.5608 loss_dfl: 0.2176 loss_ld: 0.4417
2023/07/13 10:37:23 - mmengine - INFO - Epoch(train) [6][1450/3139] lr: 1.2500e-03 eta: 1:50:44 time: 0.3268 data_time: 0.0046 memory: 738 loss: 1.4336 loss_cls: 0.2933 loss_bbox: 0.5227 loss_dfl: 0.2069 loss_ld: 0.4108
2023/07/13 10:37:39 - mmengine - INFO - Epoch(train) [6][1500/3139] lr: 1.2500e-03 eta: 1:50:28 time: 0.3232 data_time: 0.0043 memory: 727 loss: 1.4834 loss_cls: 0.2647 loss_bbox: 0.5462 loss_dfl: 0.2126 loss_ld: 0.4599
2023/07/13 10:37:55 - mmengine - INFO - Epoch(train) [6][1550/3139] lr: 1.2500e-03 eta: 1:50:11 time: 0.3241 data_time: 0.0044 memory: 728 loss: 1.4552 loss_cls: 0.2768 loss_bbox: 0.5508 loss_dfl: 0.2214 loss_ld: 0.4061
2023/07/13 10:38:11 - mmengine - INFO - Epoch(train) [6][1600/3139] lr: 1.2500e-03 eta: 1:49:55 time: 0.3248 data_time: 0.0042 memory: 730 loss: 1.4521 loss_cls: 0.2779 loss_bbox: 0.5500 loss_dfl: 0.2080 loss_ld: 0.4163
2023/07/13 10:38:28 - mmengine - INFO - Epoch(train) [6][1650/3139] lr: 1.2500e-03 eta: 1:49:39 time: 0.3245 data_time: 0.0043 memory: 748 loss: 1.5542 loss_cls: 0.2811 loss_bbox: 0.6290 loss_dfl: 0.2221 loss_ld: 0.4220
2023/07/13 10:38:44 - mmengine - INFO - Epoch(train) [6][1700/3139] lr: 1.2500e-03 eta: 1:49:23 time: 0.3239 data_time: 0.0041 memory: 719 loss: 1.4406 loss_cls: 0.2942 loss_bbox: 0.5626 loss_dfl: 0.2077 loss_ld: 0.3760
2023/07/13 10:39:00 - mmengine - INFO - Epoch(train) [6][1750/3139] lr: 1.2500e-03 eta: 1:49:07 time: 0.3245 data_time: 0.0044 memory: 731 loss: 1.4180 loss_cls: 0.2688 loss_bbox: 0.5571 loss_dfl: 0.2052 loss_ld: 0.3868
2023/07/13 10:39:16 - mmengine - INFO - Epoch(train) [6][1800/3139] lr: 1.2500e-03 eta: 1:48:51 time: 0.3253 data_time: 0.0053 memory: 728 loss: 1.4703 loss_cls: 0.3352 loss_bbox: 0.5732 loss_dfl: 0.2187 loss_ld: 0.3433
2023/07/13 10:39:32 - mmengine - INFO - Epoch(train) [6][1850/3139] lr: 1.2500e-03 eta: 1:48:35 time: 0.3231 data_time: 0.0039 memory: 743 loss: 1.4461 loss_cls: 0.2791 loss_bbox: 0.5771 loss_dfl: 0.2146 loss_ld: 0.3754
2023/07/13 10:39:49 - mmengine - INFO - Epoch(train) [6][1900/3139] lr: 1.2500e-03 eta: 1:48:18 time: 0.3255 data_time: 0.0048 memory: 728 loss: 1.4641 loss_cls: 0.2846 loss_bbox: 0.5721 loss_dfl: 0.2111 loss_ld: 0.3963
2023/07/13 10:40:05 - mmengine - INFO - Epoch(train) [6][1950/3139] lr: 1.2500e-03 eta: 1:48:02 time: 0.3226 data_time: 0.0038 memory: 723 loss: 1.3658 loss_cls: 0.2993 loss_bbox: 0.5270 loss_dfl: 0.2028 loss_ld: 0.3366
2023/07/13 10:40:21 - mmengine - INFO - Epoch(train) [6][2000/3139] lr: 1.2500e-03 eta: 1:47:46 time: 0.3259 data_time: 0.0047 memory: 724 loss: 1.4734 loss_cls: 0.3104 loss_bbox: 0.5660 loss_dfl: 0.2117 loss_ld: 0.3853
2023/07/13 10:40:37 - mmengine - INFO - Epoch(train) [6][2050/3139] lr: 1.2500e-03 eta: 1:47:30 time: 0.3161 data_time: 0.0035 memory: 713 loss: 1.4723 loss_cls: 0.2883 loss_bbox: 0.5558 loss_dfl: 0.2117 loss_ld: 0.4165
2023/07/13 10:40:53 - mmengine - INFO - Epoch(train) [6][2100/3139] lr: 1.2500e-03 eta: 1:47:13 time: 0.3215 data_time: 0.0050 memory: 729 loss: 1.4494 loss_cls: 0.2626 loss_bbox: 0.5574 loss_dfl: 0.2081 loss_ld: 0.4213
2023/07/13 10:41:09 - mmengine - INFO - Epoch(train) [6][2150/3139] lr: 1.2500e-03 eta: 1:46:57 time: 0.3212 data_time: 0.0044 memory: 725 loss: 1.3984 loss_cls: 0.2915 loss_bbox: 0.5740 loss_dfl: 0.2049 loss_ld: 0.3281
2023/07/13 10:41:25 - mmengine - INFO - Epoch(train) [6][2200/3139] lr: 1.2500e-03 eta: 1:46:41 time: 0.3220 data_time: 0.0042 memory: 722 loss: 1.4249 loss_cls: 0.2788 loss_bbox: 0.5516 loss_dfl: 0.2099 loss_ld: 0.3846
2023/07/13 10:41:41 - mmengine - INFO - Epoch(train) [6][2250/3139] lr: 1.2500e-03 eta: 1:46:24 time: 0.3222 data_time: 0.0038 memory: 719 loss: 1.4156 loss_cls: 0.3045 loss_bbox: 0.4991 loss_dfl: 0.2054 loss_ld: 0.4065
2023/07/13 10:41:57 - mmengine - INFO - Epoch(train) [6][2300/3139] lr: 1.2500e-03 eta: 1:46:08 time: 0.3237 data_time: 0.0037 memory: 746 loss: 1.4969 loss_cls: 0.2672 loss_bbox: 0.5588 loss_dfl: 0.2126 loss_ld: 0.4583
2023/07/13 10:41:59 - mmengine - INFO - Exp name: ld_gfl_r18_r101_fpn_1x_vis_20230713_085933
2023/07/13 10:42:14 - mmengine - INFO - Epoch(train) [6][2350/3139] lr: 1.2500e-03 eta: 1:45:52 time: 0.3241 data_time: 0.0047 memory: 722 loss: 1.5010 loss_cls: 0.2939 loss_bbox: 0.5682 loss_dfl: 0.2199 loss_ld: 0.4189
2023/07/13 10:42:30 - mmengine - INFO - Epoch(train) [6][2400/3139] lr: 1.2500e-03 eta: 1:45:36 time: 0.3237 data_time: 0.0039 memory: 724 loss: 1.5057 loss_cls: 0.2798 loss_bbox: 0.5945 loss_dfl: 0.2141 loss_ld: 0.4174
2023/07/13 10:42:46 - mmengine - INFO - Epoch(train) [6][2450/3139] lr: 1.2500e-03 eta: 1:45:20 time: 0.3283 data_time: 0.0060 memory: 722 loss: 1.4331 loss_cls: 0.2923 loss_bbox: 0.5607 loss_dfl: 0.2111 loss_ld: 0.3690
2023/07/13 10:43:02 - mmengine - INFO - Epoch(train) [6][2500/3139] lr: 1.2500e-03 eta: 1:45:04 time: 0.3232 data_time: 0.0042 memory: 729 loss: 1.5680 loss_cls: 0.2843 loss_bbox: 0.5956 loss_dfl: 0.2162 loss_ld: 0.4719
2023/07/13 10:43:19 - mmengine - INFO - Epoch(train) [6][2550/3139] lr: 1.2500e-03 eta: 1:44:47 time: 0.3237 data_time: 0.0045 memory: 731 loss: 1.4080 loss_cls: 0.2632 loss_bbox: 0.5278 loss_dfl: 0.2045 loss_ld: 0.4124
2023/07/13 10:43:35 - mmengine - INFO - Epoch(train) [6][2600/3139] lr: 1.2500e-03 eta: 1:44:31 time: 0.3246 data_time: 0.0044 memory: 720 loss: 1.5215 loss_cls: 0.2894 loss_bbox: 0.5754 loss_dfl: 0.2162 loss_ld: 0.4405
2023/07/13 10:43:51 - mmengine - INFO - Epoch(train) [6][2650/3139] lr: 1.2500e-03 eta: 1:44:15 time: 0.3241 data_time: 0.0043 memory: 735 loss: 1.4660 loss_cls: 0.2775 loss_bbox: 0.5578 loss_dfl: 0.2146 loss_ld: 0.4161
2023/07/13 10:44:07 - mmengine - INFO - Epoch(train) [6][2700/3139] lr: 1.2500e-03 eta: 1:43:59 time: 0.3237 data_time: 0.0040 memory: 725 loss: 1.4928 loss_cls: 0.2724 loss_bbox: 0.5535 loss_dfl: 0.2104 loss_ld: 0.4565
2023/07/13 10:44:24 - mmengine - INFO - Epoch(train) [6][2750/3139] lr: 1.2500e-03 eta: 1:43:43 time: 0.3253 data_time: 0.0040 memory: 723 loss: 1.5198 loss_cls: 0.2872 loss_bbox: 0.5783 loss_dfl: 0.2212 loss_ld: 0.4330
2023/07/13 10:44:40 - mmengine - INFO - Epoch(train) [6][2800/3139] lr: 1.2500e-03 eta: 1:43:27 time: 0.3264 data_time: 0.0048 memory: 734 loss: 1.5031 loss_cls: 0.2930 loss_bbox: 0.5906 loss_dfl: 0.2175 loss_ld: 0.4020
2023/07/13 10:44:56 - mmengine - INFO - Epoch(train) [6][2850/3139] lr: 1.2500e-03 eta: 1:43:11 time: 0.3250 data_time: 0.0041 memory: 730 loss: 1.4463 loss_cls: 0.2796 loss_bbox: 0.5601 loss_dfl: 0.2109 loss_ld: 0.3957
2023/07/13 10:45:12 - mmengine - INFO - Epoch(train) [6][2900/3139] lr: 1.2500e-03 eta: 1:42:55 time: 0.3249 data_time: 0.0038 memory: 721 loss: 1.4619 loss_cls: 0.2732 loss_bbox: 0.5594 loss_dfl: 0.2152 loss_ld: 0.4141
2023/07/13 10:45:28 - mmengine - INFO - Epoch(train) [6][2950/3139] lr: 1.2500e-03 eta: 1:42:38 time: 0.3227 data_time: 0.0038 memory: 726 loss: 1.4056 loss_cls: 0.2890 loss_bbox: 0.5090 loss_dfl: 0.2046 loss_ld: 0.4030
2023/07/13 10:45:45 - mmengine - INFO - Epoch(train) [6][3000/3139] lr: 1.2500e-03 eta: 1:42:22 time: 0.3208 data_time: 0.0035 memory: 717 loss: 1.4688 loss_cls: 0.2788 loss_bbox: 0.5626 loss_dfl: 0.2112 loss_ld: 0.4162
2023/07/13 10:46:01 - mmengine - INFO - Epoch(train) [6][3050/3139] lr: 1.2500e-03 eta: 1:42:06 time: 0.3250 data_time: 0.0039 memory: 717 loss: 1.3784 loss_cls: 0.2680 loss_bbox: 0.5499 loss_dfl: 0.2060 loss_ld: 0.3545
2023/07/13 10:46:17 - mmengine - INFO - Epoch(train) [6][3100/3139] lr: 1.2500e-03 eta: 1:41:50 time: 0.3260 data_time: 0.0054 memory: 724 loss: 1.3641 loss_cls: 0.2749 loss_bbox: 0.5394 loss_dfl: 0.2040 loss_ld: 0.3458
2023/07/13 10:46:30 - mmengine - INFO - Exp name: ld_gfl_r18_r101_fpn_1x_vis_20230713_085933
2023/07/13 10:46:30 - mmengine - INFO - Saving checkpoint at 6 epochs
2023/07/13 10:46:36 - mmengine - INFO - Epoch(val) [6][ 50/548] eta: 0:00:40 time: 0.0817 data_time: 0.0023 memory: 728
2023/07/13 10:46:40 - mmengine - INFO - Epoch(val) [6][100/548] eta: 0:00:36 time: 0.0810 data_time: 0.0016 memory: 497
2023/07/13 10:46:44 - mmengine - INFO - Epoch(val) [6][150/548] eta: 0:00:32 time: 0.0815 data_time: 0.0015 memory: 497
2023/07/13 10:46:48 - mmengine - INFO - Epoch(val) [6][200/548] eta: 0:00:28 time: 0.0807 data_time: 0.0015 memory: 497
2023/07/13 10:46:52 - mmengine - INFO - Epoch(val) [6][250/548] eta: 0:00:24 time: 0.0810 data_time: 0.0015 memory: 497
2023/07/13 10:46:56 - mmengine - INFO - Epoch(val) [6][300/548] eta: 0:00:20 time: 0.0806 data_time: 0.0016 memory: 497
2023/07/13 10:47:01 - mmengine - INFO - Epoch(val) [6][350/548] eta: 0:00:16 time: 0.0803 data_time: 0.0015 memory: 497
2023/07/13 10:47:05 - mmengine - INFO - Epoch(val) [6][400/548] eta: 0:00:11 time: 0.0801 data_time: 0.0015 memory: 497
2023/07/13 10:47:09 - mmengine - INFO - Epoch(val) [6][450/548] eta: 0:00:07 time: 0.0821 data_time: 0.0016 memory: 497
2023/07/13 10:47:13 - mmengine - INFO - Epoch(val) [6][500/548] eta: 0:00:03 time: 0.0804 data_time: 0.0015 memory: 497
2023/07/13 10:47:17 - mmengine - INFO - Evaluating bbox...
2023/07/13 10:47:33 - mmengine - INFO - bbox_mAP_copypaste: 0.079 0.138 0.083 0.021 0.121 0.220
2023/07/13 10:47:33 - mmengine - INFO - Epoch(val) [6][548/548] coco/bbox_mAP: 0.0790 coco/bbox_mAP_50: 0.1380 coco/bbox_mAP_75: 0.0830 coco/bbox_mAP_s: 0.0210 coco/bbox_mAP_m: 0.1210 coco/bbox_mAP_l: 0.2200 data_time: 0.0016 time: 0.0808
2023/07/13 10:47:49 - mmengine - INFO - Epoch(train) [7][ 50/3139] lr: 1.2500e-03 eta: 1:41:21 time: 0.3265 data_time: 0.0065 memory: 724 loss: 1.4282 loss_cls: 0.3088 loss_bbox: 0.4985 loss_dfl: 0.2078 loss_ld: 0.4131
2023/07/13 10:48:05 - mmengine - INFO - Epoch(train) [7][ 100/3139] lr: 1.2500e-03 eta: 1:41:05 time: 0.3245 data_time: 0.0045 memory: 720 loss: 1.4331 loss_cls: 0.2812 loss_bbox: 0.5627 loss_dfl: 0.2104 loss_ld: 0.3788
2023/07/13 10:48:22 - mmengine - INFO - Epoch(train) [7][ 150/3139] lr: 1.2500e-03 eta: 1:40:49 time: 0.3252 data_time: 0.0056 memory: 726 loss: 1.3833 loss_cls: 0.2901 loss_bbox: 0.5681 loss_dfl: 0.2131 loss_ld: 0.3121
2023/07/13 10:48:27 - mmengine - INFO - Exp name: ld_gfl_r18_r101_fpn_1x_vis_20230713_085933
2023/07/13 10:48:38 - mmengine - INFO - Epoch(train) [7][ 200/3139] lr: 1.2500e-03 eta: 1:40:33 time: 0.3268 data_time: 0.0061 memory: 735 loss: 1.4552 loss_cls: 0.2859 loss_bbox: 0.5476 loss_dfl: 0.2133 loss_ld: 0.4083
2023/07/13 10:48:54 - mmengine - INFO - Epoch(train) [7][ 250/3139] lr: 1.2500e-03 eta: 1:40:16 time: 0.3198 data_time: 0.0047 memory: 728 loss: 1.4024 loss_cls: 0.2867 loss_bbox: 0.5826 loss_dfl: 0.2133 loss_ld: 0.3198
2023/07/13 10:49:10 - mmengine - INFO - Epoch(train) [7][ 300/3139] lr: 1.2500e-03 eta: 1:40:00 time: 0.3207 data_time: 0.0044 memory: 722 loss: 1.4317 loss_cls: 0.2868 loss_bbox: 0.5349 loss_dfl: 0.2092 loss_ld: 0.4008
2023/07/13 10:49:26 - mmengine - INFO - Epoch(train) [7][ 350/3139] lr: 1.2500e-03 eta: 1:39:43 time: 0.3187 data_time: 0.0039 memory: 732 loss: 1.4014 loss_cls: 0.3009 loss_bbox: 0.5011 loss_dfl: 0.2017 loss_ld: 0.3978
2023/07/13 10:49:42 - mmengine - INFO - Epoch(train) [7][ 400/3139] lr: 1.2500e-03 eta: 1:39:27 time: 0.3203 data_time: 0.0038 memory: 719 loss: 1.4316 loss_cls: 0.2699 loss_bbox: 0.5013 loss_dfl: 0.2052 loss_ld: 0.4551
2023/07/13 10:49:58 - mmengine - INFO - Epoch(train) [7][ 450/3139] lr: 1.2500e-03 eta: 1:39:11 time: 0.3214 data_time: 0.0039 memory: 734 loss: 1.4775 loss_cls: 0.3040 loss_bbox: 0.5289 loss_dfl: 0.2137 loss_ld: 0.4309
2023/07/13 10:50:14 - mmengine - INFO - Epoch(train) [7][ 500/3139] lr: 1.2500e-03 eta: 1:38:55 time: 0.3250 data_time: 0.0037 memory: 746 loss: 1.5016 loss_cls: 0.2934 loss_bbox: 0.5204 loss_dfl: 0.2091 loss_ld: 0.4788
2023/07/13 10:50:30 - mmengine - INFO - Epoch(train) [7][ 550/3139] lr: 1.2500e-03 eta: 1:38:39 time: 0.3232 data_time: 0.0049 memory: 727 loss: 1.4515 loss_cls: 0.3007 loss_bbox: 0.5379 loss_dfl: 0.2080 loss_ld: 0.4050
2023/07/13 10:50:47 - mmengine - INFO - Epoch(train) [7][ 600/3139] lr: 1.2500e-03 eta: 1:38:22 time: 0.3228 data_time: 0.0048 memory: 720 loss: 1.4159 loss_cls: 0.2875 loss_bbox: 0.5381 loss_dfl: 0.2040 loss_ld: 0.3862
2023/07/13 10:51:03 - mmengine - INFO - Epoch(train) [7][ 650/3139] lr: 1.2500e-03 eta: 1:38:06 time: 0.3247 data_time: 0.0042 memory: 739 loss: 1.4810 loss_cls: 0.2779 loss_bbox: 0.5765 loss_dfl: 0.2124 loss_ld: 0.4141
2023/07/13 10:51:19 - mmengine - INFO - Epoch(train) [7][ 700/3139] lr: 1.2500e-03 eta: 1:37:50 time: 0.3217 data_time: 0.0040 memory: 734 loss: 1.4030 loss_cls: 0.2868 loss_bbox: 0.5622 loss_dfl: 0.2083 loss_ld: 0.3457
2023/07/13 10:51:35 - mmengine - INFO - Epoch(train) [7][ 750/3139] lr: 1.2500e-03 eta: 1:37:34 time: 0.3262 data_time: 0.0041 memory: 761 loss: 1.4338 loss_cls: 0.2674 loss_bbox: 0.5626 loss_dfl: 0.2059 loss_ld: 0.3979
2023/07/13 10:51:51 - mmengine - INFO - Epoch(train) [7][ 800/3139] lr: 1.2500e-03 eta: 1:37:18 time: 0.3229 data_time: 0.0043 memory: 724 loss: 1.3891 loss_cls: 0.2639 loss_bbox: 0.5826 loss_dfl: 0.2100 loss_ld: 0.3325
2023/07/13 10:52:08 - mmengine - INFO - Epoch(train) [7][ 850/3139] lr: 1.2500e-03 eta: 1:37:02 time: 0.3276 data_time: 0.0048 memory: 726 loss: 1.4284 loss_cls: 0.2779 loss_bbox: 0.5264 loss_dfl: 0.2083 loss_ld: 0.4158
2023/07/13 10:52:24 - mmengine - INFO - Epoch(train) [7][ 900/3139] lr: 1.2500e-03 eta: 1:36:45 time: 0.3217 data_time: 0.0038 memory: 743 loss: 1.3843 loss_cls: 0.2664 loss_bbox: 0.5160 loss_dfl: 0.2044 loss_ld: 0.3975
2023/07/13 10:52:40 - mmengine - INFO - Epoch(train) [7][ 950/3139] lr: 1.2500e-03 eta: 1:36:29 time: 0.3223 data_time: 0.0037 memory: 730 loss: 1.3875 loss_cls: 0.2692 loss_bbox: 0.5216 loss_dfl: 0.2036 loss_ld: 0.3931
2023/07/13 10:52:56 - mmengine - INFO - Epoch(train) [7][1000/3139] lr: 1.2500e-03 eta: 1:36:13 time: 0.3258 data_time: 0.0058 memory: 737 loss: 1.3626 loss_cls: 0.2817 loss_bbox: 0.5184 loss_dfl: 0.2033 loss_ld: 0.3592
2023/07/13 10:53:13 - mmengine - INFO - Epoch(train) [7][1050/3139] lr: 1.2500e-03 eta: 1:35:57 time: 0.3250 data_time: 0.0049 memory: 720 loss: 1.3209 loss_cls: 0.2784 loss_bbox: 0.5009 loss_dfl: 0.2009 loss_ld: 0.3406
2023/07/13 10:53:29 - mmengine - INFO - Epoch(train) [7][1100/3139] lr: 1.2500e-03 eta: 1:35:41 time: 0.3246 data_time: 0.0037 memory: 721 loss: 1.4186 loss_cls: 0.2610 loss_bbox: 0.5822 loss_dfl: 0.2118 loss_ld: 0.3637
2023/07/13 10:53:45 - mmengine - INFO - Epoch(train) [7][1150/3139] lr: 1.2500e-03 eta: 1:35:24 time: 0.3211 data_time: 0.0045 memory: 729 loss: 1.4968 loss_cls: 0.2721 loss_bbox: 0.5888 loss_dfl: 0.2159 loss_ld: 0.4200
2023/07/13 10:53:50 - mmengine - INFO - Exp name: ld_gfl_r18_r101_fpn_1x_vis_20230713_085933
2023/07/13 10:54:01 - mmengine - INFO - Epoch(train) [7][1200/3139] lr: 1.2500e-03 eta: 1:35:08 time: 0.3225 data_time: 0.0037 memory: 720 loss: 1.4002 loss_cls: 0.2731 loss_bbox: 0.5268 loss_dfl: 0.2103 loss_ld: 0.3900
2023/07/13 10:54:17 - mmengine - INFO - Epoch(train) [7][1250/3139] lr: 1.2500e-03 eta: 1:34:52 time: 0.3254 data_time: 0.0039 memory: 748 loss: 1.3912 loss_cls: 0.3139 loss_bbox: 0.5369 loss_dfl: 0.2080 loss_ld: 0.3323
2023/07/13 10:54:33 - mmengine - INFO - Epoch(train) [7][1300/3139] lr: 1.2500e-03 eta: 1:34:36 time: 0.3229 data_time: 0.0040 memory: 730 loss: 1.3949 loss_cls: 0.2664 loss_bbox: 0.5187 loss_dfl: 0.2012 loss_ld: 0.4086
2023/07/13 10:54:50 - mmengine - INFO - Epoch(train) [7][1350/3139] lr: 1.2500e-03 eta: 1:34:20 time: 0.3245 data_time: 0.0052 memory: 718 loss: 1.3823 loss_cls: 0.3054 loss_bbox: 0.4844 loss_dfl: 0.2002 loss_ld: 0.3923
2023/07/13 10:55:06 - mmengine - INFO - Epoch(train) [7][1400/3139] lr: 1.2500e-03 eta: 1:34:03 time: 0.3220 data_time: 0.0047 memory: 726 loss: 1.3197 loss_cls: 0.2879 loss_bbox: 0.5139 loss_dfl: 0.1977 loss_ld: 0.3202
2023/07/13 10:55:22 - mmengine - INFO - Epoch(train) [7][1450/3139] lr: 1.2500e-03 eta: 1:33:47 time: 0.3237 data_time: 0.0044 memory: 725 loss: 1.5088 loss_cls: 0.2747 loss_bbox: 0.6048 loss_dfl: 0.2213 loss_ld: 0.4080
2023/07/13 10:55:38 - mmengine - INFO - Epoch(train) [7][1500/3139] lr: 1.2500e-03 eta: 1:33:31 time: 0.3250 data_time: 0.0051 memory: 725 loss: 1.4431 loss_cls: 0.2634 loss_bbox: 0.5638 loss_dfl: 0.2105 loss_ld: 0.4054
2023/07/13 10:55:54 - mmengine - INFO - Epoch(train) [7][1550/3139] lr: 1.2500e-03 eta: 1:33:15 time: 0.3245 data_time: 0.0049 memory: 724 loss: 1.3571 loss_cls: 0.2884 loss_bbox: 0.5246 loss_dfl: 0.2049 loss_ld: 0.3393
2023/07/13 10:56:11 - mmengine - INFO - Epoch(train) [7][1600/3139] lr: 1.2500e-03 eta: 1:32:59 time: 0.3229 data_time: 0.0036 memory: 716 loss: 1.4895 loss_cls: 0.2868 loss_bbox: 0.5969 loss_dfl: 0.2219 loss_ld: 0.3839
2023/07/13 10:56:27 - mmengine - INFO - Epoch(train) [7][1650/3139] lr: 1.2500e-03 eta: 1:32:42 time: 0.3235 data_time: 0.0043 memory: 739 loss: 1.4965 loss_cls: 0.3061 loss_bbox: 0.5693 loss_dfl: 0.2102 loss_ld: 0.4109
2023/07/13 10:56:43 - mmengine - INFO - Epoch(train) [7][1700/3139] lr: 1.2500e-03 eta: 1:32:26 time: 0.3229 data_time: 0.0041 memory: 736 loss: 1.4203 loss_cls: 0.2793 loss_bbox: 0.5563 loss_dfl: 0.2098 loss_ld: 0.3748
2023/07/13 10:56:59 - mmengine - INFO - Epoch(train) [7][1750/3139] lr: 1.2500e-03 eta: 1:32:10 time: 0.3223 data_time: 0.0049 memory: 752 loss: 1.3984 loss_cls: 0.2780 loss_bbox: 0.5484 loss_dfl: 0.2026 loss_ld: 0.3694
2023/07/13 10:57:15 - mmengine - INFO - Epoch(train) [7][1800/3139] lr: 1.2500e-03 eta: 1:31:54 time: 0.3237 data_time: 0.0044 memory: 722 loss: 1.4150 loss_cls: 0.2687 loss_bbox: 0.5425 loss_dfl: 0.2110 loss_ld: 0.3929
2023/07/13 10:57:31 - mmengine - INFO - Epoch(train) [7][1850/3139] lr: 1.2500e-03 eta: 1:31:38 time: 0.3242 data_time: 0.0046 memory: 720 loss: 1.3582 loss_cls: 0.2694 loss_bbox: 0.5212 loss_dfl: 0.2048 loss_ld: 0.3628
2023/07/13 10:57:48 - mmengine - INFO - Epoch(train) [7][1900/3139] lr: 1.2500e-03 eta: 1:31:21 time: 0.3222 data_time: 0.0039 memory: 715 loss: 1.4383 loss_cls: 0.3133 loss_bbox: 0.5498 loss_dfl: 0.2162 loss_ld: 0.3590
2023/07/13 10:58:04 - mmengine - INFO - Epoch(train) [7][1950/3139] lr: 1.2500e-03 eta: 1:31:05 time: 0.3250 data_time: 0.0040 memory: 715 loss: 1.3908 loss_cls: 0.3072 loss_bbox: 0.5531 loss_dfl: 0.2095 loss_ld: 0.3211
2023/07/13 10:58:20 - mmengine - INFO - Epoch(train) [7][2000/3139] lr: 1.2500e-03 eta: 1:30:49 time: 0.3248 data_time: 0.0043 memory: 731 loss: 1.3431 loss_cls: 0.2497 loss_bbox: 0.5300 loss_dfl: 0.1986 loss_ld: 0.3648
2023/07/13 10:58:36 - mmengine - INFO - Epoch(train) [7][2050/3139] lr: 1.2500e-03 eta: 1:30:33 time: 0.3244 data_time: 0.0041 memory: 722 loss: 1.5324 loss_cls: 0.2862 loss_bbox: 0.5753 loss_dfl: 0.2154 loss_ld: 0.4555
2023/07/13 10:58:53 - mmengine - INFO - Epoch(train) [7][2100/3139] lr: 1.2500e-03 eta: 1:30:17 time: 0.3270 data_time: 0.0054 memory: 725 loss: 1.4519 loss_cls: 0.2715 loss_bbox: 0.5522 loss_dfl: 0.2122 loss_ld: 0.4160
2023/07/13 10:59:09 - mmengine - INFO - Epoch(train) [7][2150/3139] lr: 1.2500e-03 eta: 1:30:01 time: 0.3244 data_time: 0.0046 memory: 728 loss: 1.2961 loss_cls: 0.2710 loss_bbox: 0.4890 loss_dfl: 0.1981 loss_ld: 0.3380
2023/07/13 10:59:14 - mmengine - INFO - Exp name: ld_gfl_r18_r101_fpn_1x_vis_20230713_085933
2023/07/13 10:59:25 - mmengine - INFO - Epoch(train) [7][2200/3139] lr: 1.2500e-03 eta: 1:29:45 time: 0.3239 data_time: 0.0045 memory: 727 loss: 1.4459 loss_cls: 0.2990 loss_bbox: 0.5610 loss_dfl: 0.2161 loss_ld: 0.3698
2023/07/13 10:59:41 - mmengine - INFO - Epoch(train) [7][2250/3139] lr: 1.2500e-03 eta: 1:29:28 time: 0.3237 data_time: 0.0062 memory: 721 loss: 1.3848 loss_cls: 0.2766 loss_bbox: 0.5441 loss_dfl: 0.2128 loss_ld: 0.3512
2023/07/13 10:59:57 - mmengine - INFO - Epoch(train) [7][2300/3139] lr: 1.2500e-03 eta: 1:29:12 time: 0.3244 data_time: 0.0047 memory: 738 loss: 1.2992 loss_cls: 0.2924 loss_bbox: 0.5005 loss_dfl: 0.1972 loss_ld: 0.3091
2023/07/13 11:00:14 - mmengine - INFO - Epoch(train) [7][2350/3139] lr: 1.2500e-03 eta: 1:28:56 time: 0.3244 data_time: 0.0050 memory: 723 loss: 1.4591 loss_cls: 0.3058 loss_bbox: 0.5811 loss_dfl: 0.2144 loss_ld: 0.3578
2023/07/13 11:00:30 - mmengine - INFO - Epoch(train) [7][2400/3139] lr: 1.2500e-03 eta: 1:28:40 time: 0.3259 data_time: 0.0042 memory: 723 loss: 1.4369 loss_cls: 0.2912 loss_bbox: 0.5689 loss_dfl: 0.2135 loss_ld: 0.3632
2023/07/13 11:00:46 - mmengine - INFO - Epoch(train) [7][2450/3139] lr: 1.2500e-03 eta: 1:28:24 time: 0.3231 data_time: 0.0045 memory: 716 loss: 1.4387 loss_cls: 0.2817 loss_bbox: 0.5644 loss_dfl: 0.2103 loss_ld: 0.3824
2023/07/13 11:01:02 - mmengine - INFO - Epoch(train) [7][2500/3139] lr: 1.2500e-03 eta: 1:28:08 time: 0.3237 data_time: 0.0043 memory: 725 loss: 1.3705 loss_cls: 0.2750 loss_bbox: 0.4959 loss_dfl: 0.2020 loss_ld: 0.3976
2023/07/13 11:01:19 - mmengine - INFO - Epoch(train) [7][2550/3139] lr: 1.2500e-03 eta: 1:27:51 time: 0.3258 data_time: 0.0046 memory: 726 loss: 1.4087 loss_cls: 0.2727 loss_bbox: 0.5476 loss_dfl: 0.2057 loss_ld: 0.3828
2023/07/13 11:01:35 - mmengine - INFO - Epoch(train) [7][2600/3139] lr: 1.2500e-03 eta: 1:27:35 time: 0.3241 data_time: 0.0035 memory: 734 loss: 1.3668 loss_cls: 0.2714 loss_bbox: 0.5075 loss_dfl: 0.2024 loss_ld: 0.3855
2023/07/13 11:01:51 - mmengine - INFO - Epoch(train) [7][2650/3139] lr: 1.2500e-03 eta: 1:27:19 time: 0.3262 data_time: 0.0046 memory: 725 loss: 1.3526 loss_cls: 0.2686 loss_bbox: 0.5445 loss_dfl: 0.2065 loss_ld: 0.3330
2023/07/13 11:02:07 - mmengine - INFO - Epoch(train) [7][2700/3139] lr: 1.2500e-03 eta: 1:27:03 time: 0.3237 data_time: 0.0041 memory: 722 loss: 1.4276 loss_cls: 0.2606 loss_bbox: 0.5481 loss_dfl: 0.2106 loss_ld: 0.4083
2023/07/13 11:02:24 - mmengine - INFO - Epoch(train) [7][2750/3139] lr: 1.2500e-03 eta: 1:26:47 time: 0.3241 data_time: 0.0038 memory: 722 loss: 1.4438 loss_cls: 0.2915 loss_bbox: 0.5741 loss_dfl: 0.2104 loss_ld: 0.3679
2023/07/13 11:02:40 - mmengine - INFO - Epoch(train) [7][2800/3139] lr: 1.2500e-03 eta: 1:26:31 time: 0.3226 data_time: 0.0046 memory: 730 loss: 1.3689 loss_cls: 0.2739 loss_bbox: 0.5262 loss_dfl: 0.2032 loss_ld: 0.3657
2023/07/13 11:02:56 - mmengine - INFO - Epoch(train) [7][2850/3139] lr: 1.2500e-03 eta: 1:26:14 time: 0.3230 data_time: 0.0040 memory: 722 loss: 1.4578 loss_cls: 0.2479 loss_bbox: 0.5723 loss_dfl: 0.2125 loss_ld: 0.4251
2023/07/13 11:03:12 - mmengine - INFO - Epoch(train) [7][2900/3139] lr: 1.2500e-03 eta: 1:25:58 time: 0.3219 data_time: 0.0040 memory: 735 loss: 1.3961 loss_cls: 0.3004 loss_bbox: 0.5684 loss_dfl: 0.2142 loss_ld: 0.3131
2023/07/13 11:03:28 - mmengine - INFO - Epoch(train) [7][2950/3139] lr: 1.2500e-03 eta: 1:25:42 time: 0.3255 data_time: 0.0048 memory: 728 loss: 1.4112 loss_cls: 0.2772 loss_bbox: 0.4983 loss_dfl: 0.2060 loss_ld: 0.4297
2023/07/13 11:03:44 - mmengine - INFO - Epoch(train) [7][3000/3139] lr: 1.2500e-03 eta: 1:25:26 time: 0.3248 data_time: 0.0045 memory: 722 loss: 1.4907 loss_cls: 0.2831 loss_bbox: 0.5595 loss_dfl: 0.2183 loss_ld: 0.4297
2023/07/13 11:04:01 - mmengine - INFO - Epoch(train) [7][3050/3139] lr: 1.2500e-03 eta: 1:25:10 time: 0.3251 data_time: 0.0040 memory: 730 loss: 1.5072 loss_cls: 0.2674 loss_bbox: 0.5219 loss_dfl: 0.2045 loss_ld: 0.5134
2023/07/13 11:04:17 - mmengine - INFO - Epoch(train) [7][3100/3139] lr: 1.2500e-03 eta: 1:24:53 time: 0.3198 data_time: 0.0042 memory: 730 loss: 1.4002 loss_cls: 0.3106 loss_bbox: 0.5149 loss_dfl: 0.2111 loss_ld: 0.3637
2023/07/13 11:04:29 - mmengine - INFO - Exp name: ld_gfl_r18_r101_fpn_1x_vis_20230713_085933
2023/07/13 11:04:29 - mmengine - INFO - Saving checkpoint at 7 epochs
2023/07/13 11:04:35 - mmengine - INFO - Epoch(val) [7][ 50/548] eta: 0:00:38 time: 0.0767 data_time: 0.0024 memory: 728
2023/07/13 11:04:39 - mmengine - INFO - Epoch(val) [7][100/548] eta: 0:00:34 time: 0.0751 data_time: 0.0015 memory: 497
2023/07/13 11:04:43 - mmengine - INFO - Epoch(val) [7][150/548] eta: 0:00:30 time: 0.0748 data_time: 0.0014 memory: 497
2023/07/13 11:04:47 - mmengine - INFO - Epoch(val) [7][200/548] eta: 0:00:26 time: 0.0758 data_time: 0.0015 memory: 497
2023/07/13 11:04:51 - mmengine - INFO - Epoch(val) [7][250/548] eta: 0:00:22 time: 0.0749 data_time: 0.0014 memory: 497
2023/07/13 11:04:54 - mmengine - INFO - Epoch(val) [7][300/548] eta: 0:00:18 time: 0.0742 data_time: 0.0015 memory: 497
2023/07/13 11:04:58 - mmengine - INFO - Epoch(val) [7][350/548] eta: 0:00:14 time: 0.0742 data_time: 0.0014 memory: 497
2023/07/13 11:05:02 - mmengine - INFO - Epoch(val) [7][400/548] eta: 0:00:11 time: 0.0744 data_time: 0.0014 memory: 497
2023/07/13 11:05:05 - mmengine - INFO - Epoch(val) [7][450/548] eta: 0:00:07 time: 0.0748 data_time: 0.0015 memory: 497
2023/07/13 11:05:09 - mmengine - INFO - Epoch(val) [7][500/548] eta: 0:00:03 time: 0.0747 data_time: 0.0015 memory: 497
2023/07/13 11:05:13 - mmengine - INFO - Evaluating bbox...
2023/07/13 11:05:26 - mmengine - INFO - bbox_mAP_copypaste: 0.092 0.159 0.096 0.023 0.131 0.262
2023/07/13 11:05:26 - mmengine - INFO - Epoch(val) [7][548/548] coco/bbox_mAP: 0.0920 coco/bbox_mAP_50: 0.1590 coco/bbox_mAP_75: 0.0960 coco/bbox_mAP_s: 0.0230 coco/bbox_mAP_m: 0.1310 coco/bbox_mAP_l: 0.2620 data_time: 0.0015 time: 0.0749
2023/07/13 11:05:35 - mmengine - INFO - Exp name: ld_gfl_r18_r101_fpn_1x_vis_20230713_085933
2023/07/13 11:05:42 - mmengine - INFO - Epoch(train) [8][ 50/3139] lr: 1.2500e-03 eta: 1:24:24 time: 0.3243 data_time: 0.0063 memory: 722 loss: 1.4144 loss_cls: 0.2700 loss_bbox: 0.5692 loss_dfl: 0.2053 loss_ld: 0.3699
2023/07/13 11:05:58 - mmengine - INFO - Epoch(train) [8][ 100/3139] lr: 1.2500e-03 eta: 1:24:08 time: 0.3245 data_time: 0.0041 memory: 726 loss: 1.3326 loss_cls: 0.2820 loss_bbox: 0.4831 loss_dfl: 0.2027 loss_ld: 0.3649
2023/07/13 11:06:15 - mmengine - INFO - Epoch(train) [8][ 150/3139] lr: 1.2500e-03 eta: 1:23:52 time: 0.3289 data_time: 0.0058 memory: 717 loss: 1.4507 loss_cls: 0.3069 loss_bbox: 0.5347 loss_dfl: 0.2109 loss_ld: 0.3981
2023/07/13 11:06:31 - mmengine - INFO - Epoch(train) [8][ 200/3139] lr: 1.2500e-03 eta: 1:23:36 time: 0.3249 data_time: 0.0039 memory: 747 loss: 1.3148 loss_cls: 0.2862 loss_bbox: 0.4894 loss_dfl: 0.1964 loss_ld: 0.3429
2023/07/13 11:06:47 - mmengine - INFO - Epoch(train) [8][ 250/3139] lr: 1.2500e-03 eta: 1:23:20 time: 0.3237 data_time: 0.0037 memory: 735 loss: 1.3929 loss_cls: 0.2776 loss_bbox: 0.5155 loss_dfl: 0.2068 loss_ld: 0.3929
2023/07/13 11:07:04 - mmengine - INFO - Epoch(train) [8][ 300/3139] lr: 1.2500e-03 eta: 1:23:04 time: 0.3241 data_time: 0.0038 memory: 738 loss: 1.3656 loss_cls: 0.2774 loss_bbox: 0.4885 loss_dfl: 0.2004 loss_ld: 0.3993
2023/07/13 11:07:20 - mmengine - INFO - Epoch(train) [8][ 350/3139] lr: 1.2500e-03 eta: 1:22:48 time: 0.3241 data_time: 0.0045 memory: 719 loss: 1.3877 loss_cls: 0.2745 loss_bbox: 0.5570 loss_dfl: 0.2094 loss_ld: 0.3467
2023/07/13 11:07:36 - mmengine - INFO - Epoch(train) [8][ 400/3139] lr: 1.2500e-03 eta: 1:22:31 time: 0.3230 data_time: 0.0037 memory: 740 loss: 1.4406 loss_cls: 0.2670 loss_bbox: 0.5085 loss_dfl: 0.2112 loss_ld: 0.4539
2023/07/13 11:07:52 - mmengine - INFO - Epoch(train) [8][ 450/3139] lr: 1.2500e-03 eta: 1:22:15 time: 0.3234 data_time: 0.0047 memory: 733 loss: 1.3083 loss_cls: 0.2674 loss_bbox: 0.5188 loss_dfl: 0.1974 loss_ld: 0.3248
2023/07/13 11:08:08 - mmengine - INFO - Epoch(train) [8][ 500/3139] lr: 1.2500e-03 eta: 1:21:59 time: 0.3227 data_time: 0.0037 memory: 718 loss: 1.4169 loss_cls: 0.2794 loss_bbox: 0.5329 loss_dfl: 0.2046 loss_ld: 0.4000
2023/07/13 11:08:24 - mmengine - INFO - Epoch(train) [8][ 550/3139] lr: 1.2500e-03 eta: 1:21:43 time: 0.3217 data_time: 0.0035 memory: 738 loss: 1.4341 loss_cls: 0.2641 loss_bbox: 0.5821 loss_dfl: 0.2235 loss_ld: 0.3644
2023/07/13 11:08:41 - mmengine - INFO - Epoch(train) [8][ 600/3139] lr: 1.2500e-03 eta: 1:21:27 time: 0.3265 data_time: 0.0056 memory: 752 loss: 1.4175 loss_cls: 0.2596 loss_bbox: 0.5902 loss_dfl: 0.2082 loss_ld: 0.3595
2023/07/13 11:08:57 - mmengine - INFO - Epoch(train) [8][ 650/3139] lr: 1.2500e-03 eta: 1:21:10 time: 0.3255 data_time: 0.0046 memory: 718 loss: 1.3580 loss_cls: 0.2814 loss_bbox: 0.4849 loss_dfl: 0.2004 loss_ld: 0.3914
2023/07/13 11:09:13 - mmengine - INFO - Epoch(train) [8][ 700/3139] lr: 1.2500e-03 eta: 1:20:54 time: 0.3243 data_time: 0.0047 memory: 723 loss: 1.4132 loss_cls: 0.3057 loss_bbox: 0.5336 loss_dfl: 0.2139 loss_ld: 0.3600
2023/07/13 11:09:29 - mmengine - INFO - Epoch(train) [8][ 750/3139] lr: 1.2500e-03 eta: 1:20:38 time: 0.3232 data_time: 0.0036 memory: 730 loss: 1.4185 loss_cls: 0.2710 loss_bbox: 0.5383 loss_dfl: 0.2114 loss_ld: 0.3978
2023/07/13 11:09:46 - mmengine - INFO - Epoch(train) [8][ 800/3139] lr: 1.2500e-03 eta: 1:20:22 time: 0.3237 data_time: 0.0044 memory: 714 loss: 1.3746 loss_cls: 0.2571 loss_bbox: 0.5551 loss_dfl: 0.2064 loss_ld: 0.3560
2023/07/13 11:10:02 - mmengine - INFO - Epoch(train) [8][ 850/3139] lr: 1.2500e-03 eta: 1:20:06 time: 0.3249 data_time: 0.0055 memory: 736 loss: 1.3410 loss_cls: 0.2528 loss_bbox: 0.5191 loss_dfl: 0.2005 loss_ld: 0.3686
2023/07/13 11:10:18 - mmengine - INFO - Epoch(train) [8][ 900/3139] lr: 1.2500e-03 eta: 1:19:49 time: 0.3209 data_time: 0.0042 memory: 722 loss: 1.3495 loss_cls: 0.2753 loss_bbox: 0.4938 loss_dfl: 0.2018 loss_ld: 0.3786
2023/07/13 11:10:34 - mmengine - INFO - Epoch(train) [8][ 950/3139] lr: 1.2500e-03 eta: 1:19:33 time: 0.3247 data_time: 0.0051 memory: 719 loss: 1.3778 loss_cls: 0.2832 loss_bbox: 0.5587 loss_dfl: 0.2147 loss_ld: 0.3212
2023/07/13 11:10:50 - mmengine - INFO - Epoch(train) [8][1000/3139] lr: 1.2500e-03 eta: 1:19:17 time: 0.3200 data_time: 0.0038 memory: 728 loss: 1.3727 loss_cls: 0.2730 loss_bbox: 0.5527 loss_dfl: 0.2019 loss_ld: 0.3451
2023/07/13 11:10:59 - mmengine - INFO - Exp name: ld_gfl_r18_r101_fpn_1x_vis_20230713_085933
2023/07/13 11:11:06 - mmengine - INFO - Epoch(train) [8][1050/3139] lr: 1.2500e-03 eta: 1:19:01 time: 0.3257 data_time: 0.0052 memory: 726 loss: 1.3827 loss_cls: 0.2702 loss_bbox: 0.5208 loss_dfl: 0.2040 loss_ld: 0.3877
2023/07/13 11:11:23 - mmengine - INFO - Epoch(train) [8][1100/3139] lr: 1.2500e-03 eta: 1:18:45 time: 0.3256 data_time: 0.0053 memory: 715 loss: 1.3165 loss_cls: 0.2704 loss_bbox: 0.5012 loss_dfl: 0.1997 loss_ld: 0.3451
2023/07/13 11:11:39 - mmengine - INFO - Epoch(train) [8][1150/3139] lr: 1.2500e-03 eta: 1:18:29 time: 0.3267 data_time: 0.0043 memory: 723 loss: 1.3811 loss_cls: 0.2478 loss_bbox: 0.5198 loss_dfl: 0.2011 loss_ld: 0.4123
2023/07/13 11:11:55 - mmengine - INFO - Epoch(train) [8][1200/3139] lr: 1.2500e-03 eta: 1:18:12 time: 0.3245 data_time: 0.0044 memory: 720 loss: 1.3759 loss_cls: 0.2545 loss_bbox: 0.5337 loss_dfl: 0.2062 loss_ld: 0.3815
2023/07/13 11:12:11 - mmengine - INFO - Epoch(train) [8][1250/3139] lr: 1.2500e-03 eta: 1:17:56 time: 0.3244 data_time: 0.0052 memory: 717 loss: 1.3390 loss_cls: 0.2655 loss_bbox: 0.5255 loss_dfl: 0.2002 loss_ld: 0.3478
2023/07/13 11:12:28 - mmengine - INFO - Epoch(train) [8][1300/3139] lr: 1.2500e-03 eta: 1:17:40 time: 0.3221 data_time: 0.0039 memory: 719 loss: 1.3301 loss_cls: 0.3015 loss_bbox: 0.5022 loss_dfl: 0.1966 loss_ld: 0.3297
2023/07/13 11:12:44 - mmengine - INFO - Epoch(train) [8][1350/3139] lr: 1.2500e-03 eta: 1:17:24 time: 0.3243 data_time: 0.0049 memory: 734 loss: 1.3398 loss_cls: 0.2825 loss_bbox: 0.4893 loss_dfl: 0.1985 loss_ld: 0.3695
2023/07/13 11:13:00 - mmengine - INFO - Epoch(train) [8][1400/3139] lr: 1.2500e-03 eta: 1:17:08 time: 0.3241 data_time: 0.0043 memory: 730 loss: 1.3648 loss_cls: 0.2504 loss_bbox: 0.5164 loss_dfl: 0.1996 loss_ld: 0.3984
2023/07/13 11:13:16 - mmengine - INFO - Epoch(train) [8][1450/3139] lr: 1.2500e-03 eta: 1:16:51 time: 0.3224 data_time: 0.0040 memory: 748 loss: 1.3701 loss_cls: 0.2711 loss_bbox: 0.5327 loss_dfl: 0.1992 loss_ld: 0.3671
2023/07/13 11:13:32 - mmengine - INFO - Epoch(train) [8][1500/3139] lr: 1.2500e-03 eta: 1:16:35 time: 0.3244 data_time: 0.0044 memory: 727 loss: 1.2810 loss_cls: 0.2888 loss_bbox: 0.4668 loss_dfl: 0.1905 loss_ld: 0.3349
2023/07/13 11:13:49 - mmengine - INFO - Epoch(train) [8][1550/3139] lr: 1.2500e-03 eta: 1:16:19 time: 0.3285 data_time: 0.0047 memory: 722 loss: 1.3855 loss_cls: 0.2875 loss_bbox: 0.5343 loss_dfl: 0.2070 loss_ld: 0.3567
2023/07/13 11:14:05 - mmengine - INFO - Epoch(train) [8][1600/3139] lr: 1.2500e-03 eta: 1:16:03 time: 0.3258 data_time: 0.0054 memory: 721 loss: 1.4322 loss_cls: 0.2907 loss_bbox: 0.5742 loss_dfl: 0.2149 loss_ld: 0.3523
2023/07/13 11:14:21 - mmengine - INFO - Epoch(train) [8][1650/3139] lr: 1.2500e-03 eta: 1:15:47 time: 0.3255 data_time: 0.0051 memory: 730 loss: 1.4318 loss_cls: 0.2818 loss_bbox: 0.5378 loss_dfl: 0.2076 loss_ld: 0.4046
2023/07/13 11:14:38 - mmengine - INFO - Epoch(train) [8][1700/3139] lr: 1.2500e-03 eta: 1:15:31 time: 0.3247 data_time: 0.0049 memory: 728 loss: 1.3974 loss_cls: 0.2656 loss_bbox: 0.5420 loss_dfl: 0.2075 loss_ld: 0.3822
2023/07/13 11:14:54 - mmengine - INFO - Epoch(train) [8][1750/3139] lr: 1.2500e-03 eta: 1:15:15 time: 0.3255 data_time: 0.0050 memory: 724 loss: 1.3416 loss_cls: 0.2662 loss_bbox: 0.4932 loss_dfl: 0.2040 loss_ld: 0.3782
2023/07/13 11:15:10 - mmengine - INFO - Epoch(train) [8][1800/3139] lr: 1.2500e-03 eta: 1:14:58 time: 0.3241 data_time: 0.0038 memory: 719 loss: 1.4180 loss_cls: 0.2552 loss_bbox: 0.5351 loss_dfl: 0.2022 loss_ld: 0.4255
2023/07/13 11:15:26 - mmengine - INFO - Epoch(train) [8][1850/3139] lr: 1.2500e-03 eta: 1:14:42 time: 0.3232 data_time: 0.0043 memory: 730 loss: 1.3413 loss_cls: 0.2811 loss_bbox: 0.5260 loss_dfl: 0.2016 loss_ld: 0.3325
2023/07/13 11:15:42 - mmengine - INFO - Epoch(train) [8][1900/3139] lr: 1.2500e-03 eta: 1:14:26 time: 0.3240 data_time: 0.0054 memory: 719 loss: 1.3654 loss_cls: 0.2669 loss_bbox: 0.4937 loss_dfl: 0.2038 loss_ld: 0.4010
2023/07/13 11:15:58 - mmengine - INFO - Epoch(train) [8][1950/3139] lr: 1.2500e-03 eta: 1:14:10 time: 0.3199 data_time: 0.0040 memory: 722 loss: 1.3743 loss_cls: 0.2891 loss_bbox: 0.5150 loss_dfl: 0.2049 loss_ld: 0.3653
2023/07/13 11:16:15 - mmengine - INFO - Epoch(train) [8][2000/3139] lr: 1.2500e-03 eta: 1:13:54 time: 0.3221 data_time: 0.0047 memory: 734 loss: 1.4129 loss_cls: 0.2837 loss_bbox: 0.5541 loss_dfl: 0.2100 loss_ld: 0.3651
2023/07/13 11:16:23 - mmengine - INFO - Exp name: ld_gfl_r18_r101_fpn_1x_vis_20230713_085933
2023/07/13 11:16:30 - mmengine - INFO - Epoch(train) [8][2050/3139] lr: 1.2500e-03 eta: 1:13:37 time: 0.3178 data_time: 0.0042 memory: 718 loss: 1.3423 loss_cls: 0.2764 loss_bbox: 0.5336 loss_dfl: 0.2013 loss_ld: 0.3310
2023/07/13 11:16:47 - mmengine - INFO - Epoch(train) [8][2100/3139] lr: 1.2500e-03 eta: 1:13:21 time: 0.3210 data_time: 0.0041 memory: 762 loss: 1.4917 loss_cls: 0.2771 loss_bbox: 0.5775 loss_dfl: 0.2191 loss_ld: 0.4180
2023/07/13 11:17:02 - mmengine - INFO - Epoch(train) [8][2150/3139] lr: 1.2500e-03 eta: 1:13:05 time: 0.3186 data_time: 0.0045 memory: 730 loss: 1.3312 loss_cls: 0.2777 loss_bbox: 0.5507 loss_dfl: 0.2011 loss_ld: 0.3017
2023/07/13 11:17:18 - mmengine - INFO - Epoch(train) [8][2200/3139] lr: 1.2500e-03 eta: 1:12:48 time: 0.3161 data_time: 0.0041 memory: 720 loss: 1.3960 loss_cls: 0.3026 loss_bbox: 0.5572 loss_dfl: 0.2091 loss_ld: 0.3271
2023/07/13 11:17:34 - mmengine - INFO - Epoch(train) [8][2250/3139] lr: 1.2500e-03 eta: 1:12:32 time: 0.3237 data_time: 0.0051 memory: 716 loss: 1.3309 loss_cls: 0.3018 loss_bbox: 0.5303 loss_dfl: 0.2044 loss_ld: 0.2943
2023/07/13 11:17:51 - mmengine - INFO - Epoch(train) [8][2300/3139] lr: 1.2500e-03 eta: 1:12:16 time: 0.3202 data_time: 0.0037 memory: 720 loss: 1.4057 loss_cls: 0.2922 loss_bbox: 0.5727 loss_dfl: 0.2128 loss_ld: 0.3279
2023/07/13 11:18:07 - mmengine - INFO - Epoch(train) [8][2350/3139] lr: 1.2500e-03 eta: 1:11:59 time: 0.3213 data_time: 0.0041 memory: 723 loss: 1.4028 loss_cls: 0.2575 loss_bbox: 0.5831 loss_dfl: 0.2113 loss_ld: 0.3508
2023/07/13 11:18:23 - mmengine - INFO - Epoch(train) [8][2400/3139] lr: 1.2500e-03 eta: 1:11:43 time: 0.3243 data_time: 0.0044 memory: 729 loss: 1.3738 loss_cls: 0.2666 loss_bbox: 0.5415 loss_dfl: 0.2030 loss_ld: 0.3627
2023/07/13 11:18:39 - mmengine - INFO - Epoch(train) [8][2450/3139] lr: 1.2500e-03 eta: 1:11:27 time: 0.3189 data_time: 0.0039 memory: 722 loss: 1.3515 loss_cls: 0.3076 loss_bbox: 0.5601 loss_dfl: 0.2089 loss_ld: 0.2748
2023/07/13 11:18:55 - mmengine - INFO - Epoch(train) [8][2500/3139] lr: 1.2500e-03 eta: 1:11:11 time: 0.3238 data_time: 0.0043 memory: 727 loss: 1.3708 loss_cls: 0.2667 loss_bbox: 0.5338 loss_dfl: 0.2030 loss_ld: 0.3673
2023/07/13 11:19:11 - mmengine - INFO - Epoch(train) [8][2550/3139] lr: 1.2500e-03 eta: 1:10:55 time: 0.3252 data_time: 0.0039 memory: 726 loss: 1.3650 loss_cls: 0.2647 loss_bbox: 0.5036 loss_dfl: 0.1960 loss_ld: 0.4007
2023/07/13 11:19:27 - mmengine - INFO - Epoch(train) [8][2600/3139] lr: 1.2500e-03 eta: 1:10:39 time: 0.3245 data_time: 0.0059 memory: 730 loss: 1.3290 loss_cls: 0.2922 loss_bbox: 0.5241 loss_dfl: 0.2020 loss_ld: 0.3106
2023/07/13 11:19:44 - mmengine - INFO - Epoch(train) [8][2650/3139] lr: 1.2500e-03 eta: 1:10:22 time: 0.3223 data_time: 0.0047 memory: 720 loss: 1.3354 loss_cls: 0.2671 loss_bbox: 0.5109 loss_dfl: 0.1995 loss_ld: 0.3580
2023/07/13 11:20:00 - mmengine - INFO - Epoch(train) [8][2700/3139] lr: 1.2500e-03 eta: 1:10:06 time: 0.3251 data_time: 0.0044 memory: 724 loss: 1.3890 loss_cls: 0.2873 loss_bbox: 0.5595 loss_dfl: 0.2112 loss_ld: 0.3310
2023/07/13 11:20:16 - mmengine - INFO - Epoch(train) [8][2750/3139] lr: 1.2500e-03 eta: 1:09:50 time: 0.3220 data_time: 0.0047 memory: 722 loss: 1.3080 loss_cls: 0.2667 loss_bbox: 0.4950 loss_dfl: 0.1954 loss_ld: 0.3510
2023/07/13 11:20:32 - mmengine - INFO - Epoch(train) [8][2800/3139] lr: 1.2500e-03 eta: 1:09:34 time: 0.3206 data_time: 0.0057 memory: 718 loss: 1.3306 loss_cls: 0.2765 loss_bbox: 0.5113 loss_dfl: 0.2044 loss_ld: 0.3384
2023/07/13 11:20:48 - mmengine - INFO - Epoch(train) [8][2850/3139] lr: 1.2500e-03 eta: 1:09:17 time: 0.3191 data_time: 0.0044 memory: 728 loss: 1.4454 loss_cls: 0.2693 loss_bbox: 0.5706 loss_dfl: 0.2111 loss_ld: 0.3943
2023/07/13 11:21:04 - mmengine - INFO - Epoch(train) [8][2900/3139] lr: 1.2500e-03 eta: 1:09:01 time: 0.3213 data_time: 0.0044 memory: 738 loss: 1.3739 loss_cls: 0.2836 loss_bbox: 0.5400 loss_dfl: 0.2051 loss_ld: 0.3452
2023/07/13 11:21:20 - mmengine - INFO - Epoch(train) [8][2950/3139] lr: 1.2500e-03 eta: 1:08:45 time: 0.3237 data_time: 0.0053 memory: 732 loss: 1.4174 loss_cls: 0.2586 loss_bbox: 0.5640 loss_dfl: 0.2085 loss_ld: 0.3863
2023/07/13 11:21:36 - mmengine - INFO - Epoch(train) [8][3000/3139] lr: 1.2500e-03 eta: 1:08:29 time: 0.3205 data_time: 0.0039 memory: 721 loss: 1.2969 loss_cls: 0.2494 loss_bbox: 0.5192 loss_dfl: 0.1978 loss_ld: 0.3307
2023/07/13 11:21:45 - mmengine - INFO - Exp name: ld_gfl_r18_r101_fpn_1x_vis_20230713_085933
2023/07/13 11:21:52 - mmengine - INFO - Epoch(train) [8][3050/3139] lr: 1.2500e-03 eta: 1:08:12 time: 0.3183 data_time: 0.0042 memory: 715 loss: 1.3892 loss_cls: 0.2794 loss_bbox: 0.5125 loss_dfl: 0.2067 loss_ld: 0.3906
2023/07/13 11:22:08 - mmengine - INFO - Epoch(train) [8][3100/3139] lr: 1.2500e-03 eta: 1:07:56 time: 0.3178 data_time: 0.0045 memory: 715 loss: 1.3082 loss_cls: 0.2581 loss_bbox: 0.4917 loss_dfl: 0.1964 loss_ld: 0.3620
2023/07/13 11:22:21 - mmengine - INFO - Exp name: ld_gfl_r18_r101_fpn_1x_vis_20230713_085933
2023/07/13 11:22:21 - mmengine - INFO - Saving checkpoint at 8 epochs
2023/07/13 11:22:27 - mmengine - INFO - Epoch(val) [8][ 50/548] eta: 0:00:37 time: 0.0759 data_time: 0.0024 memory: 729
2023/07/13 11:22:31 - mmengine - INFO - Epoch(val) [8][100/548] eta: 0:00:33 time: 0.0743 data_time: 0.0014 memory: 497
2023/07/13 11:22:34 - mmengine - INFO - Epoch(val) [8][150/548] eta: 0:00:29 time: 0.0742 data_time: 0.0013 memory: 497
2023/07/13 11:22:38 - mmengine - INFO - Epoch(val) [8][200/548] eta: 0:00:25 time: 0.0743 data_time: 0.0014 memory: 497
2023/07/13 11:22:42 - mmengine - INFO - Epoch(val) [8][250/548] eta: 0:00:22 time: 0.0743 data_time: 0.0013 memory: 497
2023/07/13 11:22:46 - mmengine - INFO - Epoch(val) [8][300/548] eta: 0:00:18 time: 0.0738 data_time: 0.0013 memory: 497
2023/07/13 11:22:49 - mmengine - INFO - Epoch(val) [8][350/548] eta: 0:00:14 time: 0.0737 data_time: 0.0014 memory: 497
2023/07/13 11:22:53 - mmengine - INFO - Epoch(val) [8][400/548] eta: 0:00:11 time: 0.0742 data_time: 0.0014 memory: 497
2023/07/13 11:22:57 - mmengine - INFO - Epoch(val) [8][450/548] eta: 0:00:07 time: 0.0752 data_time: 0.0015 memory: 497
2023/07/13 11:23:00 - mmengine - INFO - Epoch(val) [8][500/548] eta: 0:00:03 time: 0.0740 data_time: 0.0014 memory: 497
2023/07/13 11:23:05 - mmengine - INFO - Evaluating bbox...
2023/07/13 11:23:19 - mmengine - INFO - bbox_mAP_copypaste: 0.088 0.148 0.094 0.024 0.130 0.231
2023/07/13 11:23:19 - mmengine - INFO - Epoch(val) [8][548/548] coco/bbox_mAP: 0.0880 coco/bbox_mAP_50: 0.1480 coco/bbox_mAP_75: 0.0940 coco/bbox_mAP_s: 0.0240 coco/bbox_mAP_m: 0.1300 coco/bbox_mAP_l: 0.2310 data_time: 0.0015 time: 0.0743
2023/07/13 11:23:35 - mmengine - INFO - Epoch(train) [9][ 50/3139] lr: 1.2500e-04 eta: 1:07:27 time: 0.3269 data_time: 0.0071 memory: 735 loss: 1.3473 loss_cls: 0.2625 loss_bbox: 0.5659 loss_dfl: 0.2029 loss_ld: 0.3159
2023/07/13 11:23:51 - mmengine - INFO - Epoch(train) [9][ 100/3139] lr: 1.2500e-04 eta: 1:07:11 time: 0.3216 data_time: 0.0044 memory: 723 loss: 1.2487 loss_cls: 0.2574 loss_bbox: 0.4744 loss_dfl: 0.1943 loss_ld: 0.3227
2023/07/13 11:24:08 - mmengine - INFO - Epoch(train) [9][ 150/3139] lr: 1.2500e-04 eta: 1:06:55 time: 0.3230 data_time: 0.0042 memory: 736 loss: 1.2396 loss_cls: 0.2573 loss_bbox: 0.5001 loss_dfl: 0.1933 loss_ld: 0.2888
2023/07/13 11:24:24 - mmengine - INFO - Epoch(train) [9][ 200/3139] lr: 1.2500e-04 eta: 1:06:39 time: 0.3250 data_time: 0.0051 memory: 728 loss: 1.2888 loss_cls: 0.2662 loss_bbox: 0.5338 loss_dfl: 0.2013 loss_ld: 0.2875
2023/07/13 11:24:40 - mmengine - INFO - Epoch(train) [9][ 250/3139] lr: 1.2500e-04 eta: 1:06:23 time: 0.3248 data_time: 0.0046 memory: 720 loss: 1.2527 loss_cls: 0.2654 loss_bbox: 0.4967 loss_dfl: 0.1960 loss_ld: 0.2946
2023/07/13 11:24:56 - mmengine - INFO - Epoch(train) [9][ 300/3139] lr: 1.2500e-04 eta: 1:06:06 time: 0.3249 data_time: 0.0049 memory: 718 loss: 1.2549 loss_cls: 0.2618 loss_bbox: 0.4835 loss_dfl: 0.1943 loss_ld: 0.3153
2023/07/13 11:25:12 - mmengine - INFO - Epoch(train) [9][ 350/3139] lr: 1.2500e-04 eta: 1:05:50 time: 0.3231 data_time: 0.0038 memory: 716 loss: 1.2280 loss_cls: 0.2629 loss_bbox: 0.4922 loss_dfl: 0.1960 loss_ld: 0.2770
2023/07/13 11:25:29 - mmengine - INFO - Epoch(train) [9][ 400/3139] lr: 1.2500e-04 eta: 1:05:34 time: 0.3207 data_time: 0.0038 memory: 751 loss: 1.2605 loss_cls: 0.2549 loss_bbox: 0.4877 loss_dfl: 0.1951 loss_ld: 0.3229
2023/07/13 11:25:45 - mmengine - INFO - Epoch(train) [9][ 450/3139] lr: 1.2500e-04 eta: 1:05:18 time: 0.3258 data_time: 0.0047 memory: 725 loss: 1.1991 loss_cls: 0.2700 loss_bbox: 0.4538 loss_dfl: 0.1902 loss_ld: 0.2850
2023/07/13 11:26:01 - mmengine - INFO - Epoch(train) [9][ 500/3139] lr: 1.2500e-04 eta: 1:05:02 time: 0.3272 data_time: 0.0057 memory: 722 loss: 1.2144 loss_cls: 0.2339 loss_bbox: 0.4751 loss_dfl: 0.1889 loss_ld: 0.3165
2023/07/13 11:26:17 - mmengine - INFO - Epoch(train) [9][ 550/3139] lr: 1.2500e-04 eta: 1:04:45 time: 0.3213 data_time: 0.0035 memory: 739 loss: 1.2632 loss_cls: 0.2730 loss_bbox: 0.4912 loss_dfl: 0.1968 loss_ld: 0.3021
2023/07/13 11:26:34 - mmengine - INFO - Epoch(train) [9][ 600/3139] lr: 1.2500e-04 eta: 1:04:29 time: 0.3267 data_time: 0.0047 memory: 734 loss: 1.2557 loss_cls: 0.2600 loss_bbox: 0.4719 loss_dfl: 0.1947 loss_ld: 0.3290
2023/07/13 11:26:50 - mmengine - INFO - Epoch(train) [9][ 650/3139] lr: 1.2500e-04 eta: 1:04:13 time: 0.3242 data_time: 0.0043 memory: 725 loss: 1.1711 loss_cls: 0.2394 loss_bbox: 0.4700 loss_dfl: 0.1856 loss_ld: 0.2761
2023/07/13 11:27:06 - mmengine - INFO - Epoch(train) [9][ 700/3139] lr: 1.2500e-04 eta: 1:03:57 time: 0.3237 data_time: 0.0039 memory: 713 loss: 1.2435 loss_cls: 0.2698 loss_bbox: 0.4941 loss_dfl: 0.2036 loss_ld: 0.2760
2023/07/13 11:27:22 - mmengine - INFO - Epoch(train) [9][ 750/3139] lr: 1.2500e-04 eta: 1:03:41 time: 0.3246 data_time: 0.0043 memory: 729 loss: 1.2771 loss_cls: 0.2709 loss_bbox: 0.4946 loss_dfl: 0.1984 loss_ld: 0.3132
2023/07/13 11:27:39 - mmengine - INFO - Epoch(train) [9][ 800/3139] lr: 1.2500e-04 eta: 1:03:25 time: 0.3267 data_time: 0.0052 memory: 730 loss: 1.2391 loss_cls: 0.2492 loss_bbox: 0.4773 loss_dfl: 0.1947 loss_ld: 0.3179
2023/07/13 11:27:55 - mmengine - INFO - Epoch(train) [9][ 850/3139] lr: 1.2500e-04 eta: 1:03:08 time: 0.3223 data_time: 0.0042 memory: 713 loss: 1.2725 loss_cls: 0.2521 loss_bbox: 0.4711 loss_dfl: 0.1945 loss_ld: 0.3547
2023/07/13 11:28:07 - mmengine - INFO - Exp name: ld_gfl_r18_r101_fpn_1x_vis_20230713_085933
2023/07/13 11:28:11 - mmengine - INFO - Epoch(train) [9][ 900/3139] lr: 1.2500e-04 eta: 1:02:52 time: 0.3248 data_time: 0.0054 memory: 722 loss: 1.2348 loss_cls: 0.2442 loss_bbox: 0.5268 loss_dfl: 0.1962 loss_ld: 0.2676
2023/07/13 11:28:27 - mmengine - INFO - Epoch(train) [9][ 950/3139] lr: 1.2500e-04 eta: 1:02:36 time: 0.3233 data_time: 0.0052 memory: 739 loss: 1.2494 loss_cls: 0.2498 loss_bbox: 0.4975 loss_dfl: 0.1943 loss_ld: 0.3077
2023/07/13 11:28:43 - mmengine - INFO - Epoch(train) [9][1000/3139] lr: 1.2500e-04 eta: 1:02:20 time: 0.3259 data_time: 0.0050 memory: 743 loss: 1.2567 loss_cls: 0.2512 loss_bbox: 0.4794 loss_dfl: 0.1956 loss_ld: 0.3306
2023/07/13 11:29:00 - mmengine - INFO - Epoch(train) [9][1050/3139] lr: 1.2500e-04 eta: 1:02:04 time: 0.3224 data_time: 0.0039 memory: 728 loss: 1.1486 loss_cls: 0.2422 loss_bbox: 0.4500 loss_dfl: 0.1853 loss_ld: 0.2711
2023/07/13 11:29:16 - mmengine - INFO - Epoch(train) [9][1100/3139] lr: 1.2500e-04 eta: 1:01:48 time: 0.3255 data_time: 0.0051 memory: 730 loss: 1.2315 loss_cls: 0.2482 loss_bbox: 0.4918 loss_dfl: 0.1901 loss_ld: 0.3014
2023/07/13 11:29:32 - mmengine - INFO - Epoch(train) [9][1150/3139] lr: 1.2500e-04 eta: 1:01:32 time: 0.3274 data_time: 0.0056 memory: 731 loss: 1.2414 loss_cls: 0.2476 loss_bbox: 0.5017 loss_dfl: 0.1923 loss_ld: 0.2998
2023/07/13 11:29:49 - mmengine - INFO - Epoch(train) [9][1200/3139] lr: 1.2500e-04 eta: 1:01:15 time: 0.3273 data_time: 0.0066 memory: 726 loss: 1.2496 loss_cls: 0.2458 loss_bbox: 0.5141 loss_dfl: 0.1911 loss_ld: 0.2985
2023/07/13 11:30:05 - mmengine - INFO - Epoch(train) [9][1250/3139] lr: 1.2500e-04 eta: 1:00:59 time: 0.3244 data_time: 0.0039 memory: 721 loss: 1.2399 loss_cls: 0.2455 loss_bbox: 0.4791 loss_dfl: 0.1952 loss_ld: 0.3199
2023/07/13 11:30:21 - mmengine - INFO - Epoch(train) [9][1300/3139] lr: 1.2500e-04 eta: 1:00:43 time: 0.3236 data_time: 0.0039 memory: 718 loss: 1.1843 loss_cls: 0.2352 loss_bbox: 0.4721 loss_dfl: 0.1872 loss_ld: 0.2899
2023/07/13 11:30:37 - mmengine - INFO - Epoch(train) [9][1350/3139] lr: 1.2500e-04 eta: 1:00:27 time: 0.3225 data_time: 0.0041 memory: 728 loss: 1.1968 loss_cls: 0.2405 loss_bbox: 0.4717 loss_dfl: 0.1927 loss_ld: 0.2917
2023/07/13 11:30:53 - mmengine - INFO - Epoch(train) [9][1400/3139] lr: 1.2500e-04 eta: 1:00:11 time: 0.3226 data_time: 0.0038 memory: 717 loss: 1.2120 loss_cls: 0.2688 loss_bbox: 0.4497 loss_dfl: 0.1896 loss_ld: 0.3040
2023/07/13 11:31:10 - mmengine - INFO - Epoch(train) [9][1450/3139] lr: 1.2500e-04 eta: 0:59:54 time: 0.3248 data_time: 0.0050 memory: 731 loss: 1.2858 loss_cls: 0.2546 loss_bbox: 0.5295 loss_dfl: 0.1992 loss_ld: 0.3025
2023/07/13 11:31:26 - mmengine - INFO - Epoch(train) [9][1500/3139] lr: 1.2500e-04 eta: 0:59:38 time: 0.3228 data_time: 0.0046 memory: 725 loss: 1.1687 loss_cls: 0.2425 loss_bbox: 0.4535 loss_dfl: 0.1873 loss_ld: 0.2854
2023/07/13 11:31:42 - mmengine - INFO - Epoch(train) [9][1550/3139] lr: 1.2500e-04 eta: 0:59:22 time: 0.3218 data_time: 0.0041 memory: 725 loss: 1.1884 loss_cls: 0.2403 loss_bbox: 0.5029 loss_dfl: 0.1877 loss_ld: 0.2574
2023/07/13 11:31:58 - mmengine - INFO - Epoch(train) [9][1600/3139] lr: 1.2500e-04 eta: 0:59:06 time: 0.3201 data_time: 0.0039 memory: 735 loss: 1.2581 loss_cls: 0.2741 loss_bbox: 0.4919 loss_dfl: 0.1964 loss_ld: 0.2956
2023/07/13 11:32:14 - mmengine - INFO - Epoch(train) [9][1650/3139] lr: 1.2500e-04 eta: 0:58:50 time: 0.3236 data_time: 0.0041 memory: 730 loss: 1.2905 loss_cls: 0.2302 loss_bbox: 0.5238 loss_dfl: 0.2006 loss_ld: 0.3358
2023/07/13 11:32:30 - mmengine - INFO - Epoch(train) [9][1700/3139] lr: 1.2500e-04 eta: 0:58:33 time: 0.3207 data_time: 0.0042 memory: 717 loss: 1.1960 loss_cls: 0.2693 loss_bbox: 0.4549 loss_dfl: 0.1891 loss_ld: 0.2827
2023/07/13 11:32:46 - mmengine - INFO - Epoch(train) [9][1750/3139] lr: 1.2500e-04 eta: 0:58:17 time: 0.3273 data_time: 0.0059 memory: 722 loss: 1.1931 loss_cls: 0.2414 loss_bbox: 0.4688 loss_dfl: 0.1911 loss_ld: 0.2919
2023/07/13 11:33:03 - mmengine - INFO - Epoch(train) [9][1800/3139] lr: 1.2500e-04 eta: 0:58:01 time: 0.3268 data_time: 0.0062 memory: 718 loss: 1.2021 loss_cls: 0.2395 loss_bbox: 0.4841 loss_dfl: 0.1880 loss_ld: 0.2905
2023/07/13 11:33:19 - mmengine - INFO - Epoch(train) [9][1850/3139] lr: 1.2500e-04 eta: 0:57:45 time: 0.3259 data_time: 0.0039 memory: 728 loss: 1.2342 loss_cls: 0.2521 loss_bbox: 0.4896 loss_dfl: 0.1896 loss_ld: 0.3029
2023/07/13 11:33:31 - mmengine - INFO - Exp name: ld_gfl_r18_r101_fpn_1x_vis_20230713_085933
2023/07/13 11:33:35 - mmengine - INFO - Epoch(train) [9][1900/3139] lr: 1.2500e-04 eta: 0:57:29 time: 0.3246 data_time: 0.0045 memory: 733 loss: 1.1834 loss_cls: 0.2314 loss_bbox: 0.4605 loss_dfl: 0.1864 loss_ld: 0.3051
2023/07/13 11:33:51 - mmengine - INFO - Epoch(train) [9][1950/3139] lr: 1.2500e-04 eta: 0:57:13 time: 0.3227 data_time: 0.0037 memory: 723 loss: 1.2651 loss_cls: 0.2483 loss_bbox: 0.4855 loss_dfl: 0.1946 loss_ld: 0.3367
2023/07/13 11:34:08 - mmengine - INFO - Epoch(train) [9][2000/3139] lr: 1.2500e-04 eta: 0:56:56 time: 0.3248 data_time: 0.0047 memory: 722 loss: 1.2578 loss_cls: 0.2659 loss_bbox: 0.4594 loss_dfl: 0.1973 loss_ld: 0.3352
2023/07/13 11:34:24 - mmengine - INFO - Epoch(train) [9][2050/3139] lr: 1.2500e-04 eta: 0:56:40 time: 0.3258 data_time: 0.0057 memory: 723 loss: 1.2219 loss_cls: 0.2499 loss_bbox: 0.4759 loss_dfl: 0.1923 loss_ld: 0.3037
2023/07/13 11:34:40 - mmengine - INFO - Epoch(train) [9][2100/3139] lr: 1.2500e-04 eta: 0:56:24 time: 0.3255 data_time: 0.0045 memory: 719 loss: 1.2703 loss_cls: 0.2464 loss_bbox: 0.4557 loss_dfl: 0.1925 loss_ld: 0.3757
2023/07/13 11:34:56 - mmengine - INFO - Epoch(train) [9][2150/3139] lr: 1.2500e-04 eta: 0:56:08 time: 0.3193 data_time: 0.0052 memory: 731 loss: 1.2446 loss_cls: 0.2458 loss_bbox: 0.5070 loss_dfl: 0.1937 loss_ld: 0.2981
2023/07/13 11:35:12 - mmengine - INFO - Epoch(train) [9][2200/3139] lr: 1.2500e-04 eta: 0:55:52 time: 0.3182 data_time: 0.0041 memory: 728 loss: 1.1493 loss_cls: 0.2280 loss_bbox: 0.4627 loss_dfl: 0.1821 loss_ld: 0.2765
2023/07/13 11:35:28 - mmengine - INFO - Epoch(train) [9][2250/3139] lr: 1.2500e-04 eta: 0:55:35 time: 0.3223 data_time: 0.0039 memory: 722 loss: 1.2738 loss_cls: 0.2506 loss_bbox: 0.4808 loss_dfl: 0.1942 loss_ld: 0.3483
2023/07/13 11:35:44 - mmengine - INFO - Epoch(train) [9][2300/3139] lr: 1.2500e-04 eta: 0:55:19 time: 0.3249 data_time: 0.0051 memory: 730 loss: 1.1455 loss_cls: 0.2500 loss_bbox: 0.4384 loss_dfl: 0.1831 loss_ld: 0.2739
2023/07/13 11:36:01 - mmengine - INFO - Epoch(train) [9][2350/3139] lr: 1.2500e-04 eta: 0:55:03 time: 0.3263 data_time: 0.0048 memory: 724 loss: 1.2137 loss_cls: 0.2262 loss_bbox: 0.4623 loss_dfl: 0.1850 loss_ld: 0.3401
2023/07/13 11:36:17 - mmengine - INFO - Epoch(train) [9][2400/3139] lr: 1.2500e-04 eta: 0:54:47 time: 0.3261 data_time: 0.0044 memory: 722 loss: 1.2706 loss_cls: 0.2544 loss_bbox: 0.4802 loss_dfl: 0.1997 loss_ld: 0.3362
2023/07/13 11:36:33 - mmengine - INFO - Epoch(train) [9][2450/3139] lr: 1.2500e-04 eta: 0:54:31 time: 0.3270 data_time: 0.0054 memory: 722 loss: 1.1875 loss_cls: 0.2478 loss_bbox: 0.4662 loss_dfl: 0.1908 loss_ld: 0.2827
2023/07/13 11:36:50 - mmengine - INFO - Epoch(train) [9][2500/3139] lr: 1.2500e-04 eta: 0:54:15 time: 0.3283 data_time: 0.0049 memory: 721 loss: 1.2376 loss_cls: 0.2320 loss_bbox: 0.4545 loss_dfl: 0.1914 loss_ld: 0.3598
2023/07/13 11:37:06 - mmengine - INFO - Epoch(train) [9][2550/3139] lr: 1.2500e-04 eta: 0:53:59 time: 0.3261 data_time: 0.0057 memory: 722 loss: 1.2354 loss_cls: 0.2486 loss_bbox: 0.5193 loss_dfl: 0.1929 loss_ld: 0.2746
2023/07/13 11:37:22 - mmengine - INFO - Epoch(train) [9][2600/3139] lr: 1.2500e-04 eta: 0:53:42 time: 0.3236 data_time: 0.0046 memory: 721 loss: 1.2549 loss_cls: 0.2502 loss_bbox: 0.4912 loss_dfl: 0.1921 loss_ld: 0.3215
2023/07/13 11:37:39 - mmengine - INFO - Epoch(train) [9][2650/3139] lr: 1.2500e-04 eta: 0:53:26 time: 0.3236 data_time: 0.0040 memory: 718 loss: 1.2683 loss_cls: 0.2600 loss_bbox: 0.4970 loss_dfl: 0.2011 loss_ld: 0.3102
2023/07/13 11:37:55 - mmengine - INFO - Epoch(train) [9][2700/3139] lr: 1.2500e-04 eta: 0:53:10 time: 0.3245 data_time: 0.0044 memory: 717 loss: 1.2316 loss_cls: 0.2738 loss_bbox: 0.4917 loss_dfl: 0.1933 loss_ld: 0.2728
2023/07/13 11:38:11 - mmengine - INFO - Epoch(train) [9][2750/3139] lr: 1.2500e-04 eta: 0:52:54 time: 0.3245 data_time: 0.0041 memory: 739 loss: 1.1684 loss_cls: 0.2452 loss_bbox: 0.4285 loss_dfl: 0.1817 loss_ld: 0.3130
2023/07/13 11:38:27 - mmengine - INFO - Epoch(train) [9][2800/3139] lr: 1.2500e-04 eta: 0:52:38 time: 0.3269 data_time: 0.0056 memory: 734 loss: 1.2725 loss_cls: 0.2456 loss_bbox: 0.5170 loss_dfl: 0.1956 loss_ld: 0.3143
2023/07/13 11:38:44 - mmengine - INFO - Epoch(train) [9][2850/3139] lr: 1.2500e-04 eta: 0:52:22 time: 0.3247 data_time: 0.0043 memory: 723 loss: 1.2577 loss_cls: 0.2568 loss_bbox: 0.5124 loss_dfl: 0.2012 loss_ld: 0.2873
2023/07/13 11:38:56 - mmengine - INFO - Exp name: ld_gfl_r18_r101_fpn_1x_vis_20230713_085933
2023/07/13 11:39:00 - mmengine - INFO - Epoch(train) [9][2900/3139] lr: 1.2500e-04 eta: 0:52:05 time: 0.3238 data_time: 0.0047 memory: 721 loss: 1.2145 loss_cls: 0.2553 loss_bbox: 0.4855 loss_dfl: 0.1951 loss_ld: 0.2785
2023/07/13 11:39:16 - mmengine - INFO - Epoch(train) [9][2950/3139] lr: 1.2500e-04 eta: 0:51:49 time: 0.3219 data_time: 0.0040 memory: 727 loss: 1.2428 loss_cls: 0.2435 loss_bbox: 0.4885 loss_dfl: 0.1980 loss_ld: 0.3128
2023/07/13 11:39:32 - mmengine - INFO - Epoch(train) [9][3000/3139] lr: 1.2500e-04 eta: 0:51:33 time: 0.3230 data_time: 0.0042 memory: 749 loss: 1.2665 loss_cls: 0.2942 loss_bbox: 0.5096 loss_dfl: 0.1928 loss_ld: 0.2700
2023/07/13 11:39:48 - mmengine - INFO - Epoch(train) [9][3050/3139] lr: 1.2500e-04 eta: 0:51:17 time: 0.3248 data_time: 0.0055 memory: 719 loss: 1.2241 loss_cls: 0.2632 loss_bbox: 0.5003 loss_dfl: 0.1921 loss_ld: 0.2685
2023/07/13 11:40:05 - mmengine - INFO - Epoch(train) [9][3100/3139] lr: 1.2500e-04 eta: 0:51:01 time: 0.3269 data_time: 0.0059 memory: 714 loss: 1.1613 loss_cls: 0.2526 loss_bbox: 0.4429 loss_dfl: 0.1890 loss_ld: 0.2768
2023/07/13 11:40:17 - mmengine - INFO - Exp name: ld_gfl_r18_r101_fpn_1x_vis_20230713_085933
2023/07/13 11:40:17 - mmengine - INFO - Saving checkpoint at 9 epochs
2023/07/13 11:40:24 - mmengine - INFO - Epoch(val) [9][ 50/548] eta: 0:00:38 time: 0.0764 data_time: 0.0023 memory: 747
2023/07/13 11:40:28 - mmengine - INFO - Epoch(val) [9][100/548] eta: 0:00:33 time: 0.0738 data_time: 0.0014 memory: 497
2023/07/13 11:40:32 - mmengine - INFO - Epoch(val) [9][150/548] eta: 0:00:29 time: 0.0741 data_time: 0.0014 memory: 497
2023/07/13 11:40:35 - mmengine - INFO - Epoch(val) [9][200/548] eta: 0:00:25 time: 0.0738 data_time: 0.0015 memory: 497
2023/07/13 11:40:39 - mmengine - INFO - Epoch(val) [9][250/548] eta: 0:00:22 time: 0.0798 data_time: 0.0015 memory: 497
2023/07/13 11:40:43 - mmengine - INFO - Epoch(val) [9][300/548] eta: 0:00:18 time: 0.0745 data_time: 0.0015 memory: 497
2023/07/13 11:40:47 - mmengine - INFO - Epoch(val) [9][350/548] eta: 0:00:14 time: 0.0754 data_time: 0.0015 memory: 497
2023/07/13 11:40:51 - mmengine - INFO - Epoch(val) [9][400/548] eta: 0:00:11 time: 0.0754 data_time: 0.0015 memory: 497
2023/07/13 11:40:55 - mmengine - INFO - Epoch(val) [9][450/548] eta: 0:00:07 time: 0.0818 data_time: 0.0018 memory: 497
2023/07/13 11:40:59 - mmengine - INFO - Epoch(val) [9][500/548] eta: 0:00:03 time: 0.0802 data_time: 0.0015 memory: 497
2023/07/13 11:41:03 - mmengine - INFO - Evaluating bbox...
2023/07/13 11:41:18 - mmengine - INFO - bbox_mAP_copypaste: 0.104 0.173 0.111 0.029 0.150 0.287
2023/07/13 11:41:18 - mmengine - INFO - Epoch(val) [9][548/548] coco/bbox_mAP: 0.1040 coco/bbox_mAP_50: 0.1730 coco/bbox_mAP_75: 0.1110 coco/bbox_mAP_s: 0.0290 coco/bbox_mAP_m: 0.1500 coco/bbox_mAP_l: 0.2870 data_time: 0.0016 time: 0.0768
2023/07/13 11:41:34 - mmengine - INFO - Epoch(train) [10][ 50/3139] lr: 1.2500e-04 eta: 0:50:32 time: 0.3265 data_time: 0.0054 memory: 749 loss: 1.2177 loss_cls: 0.2798 loss_bbox: 0.4894 loss_dfl: 0.1970 loss_ld: 0.2515
2023/07/13 11:41:51 - mmengine - INFO - Epoch(train) [10][ 100/3139] lr: 1.2500e-04 eta: 0:50:16 time: 0.3259 data_time: 0.0059 memory: 720 loss: 1.2503 loss_cls: 0.2552 loss_bbox: 0.4884 loss_dfl: 0.1936 loss_ld: 0.3130
2023/07/13 11:42:07 - mmengine - INFO - Epoch(train) [10][ 150/3139] lr: 1.2500e-04 eta: 0:50:00 time: 0.3240 data_time: 0.0040 memory: 728 loss: 1.2495 loss_cls: 0.2466 loss_bbox: 0.5115 loss_dfl: 0.1949 loss_ld: 0.2965
2023/07/13 11:42:23 - mmengine - INFO - Epoch(train) [10][ 200/3139] lr: 1.2500e-04 eta: 0:49:43 time: 0.3221 data_time: 0.0043 memory: 726 loss: 1.2286 loss_cls: 0.2382 loss_bbox: 0.4799 loss_dfl: 0.1930 loss_ld: 0.3175
2023/07/13 11:42:39 - mmengine - INFO - Epoch(train) [10][ 250/3139] lr: 1.2500e-04 eta: 0:49:27 time: 0.3245 data_time: 0.0050 memory: 752 loss: 1.2961 loss_cls: 0.2551 loss_bbox: 0.5053 loss_dfl: 0.2019 loss_ld: 0.3339
2023/07/13 11:42:56 - mmengine - INFO - Epoch(train) [10][ 300/3139] lr: 1.2500e-04 eta: 0:49:11 time: 0.3265 data_time: 0.0048 memory: 727 loss: 1.2394 loss_cls: 0.2444 loss_bbox: 0.4874 loss_dfl: 0.1938 loss_ld: 0.3139
2023/07/13 11:43:12 - mmengine - INFO - Epoch(train) [10][ 350/3139] lr: 1.2500e-04 eta: 0:48:55 time: 0.3227 data_time: 0.0042 memory: 731 loss: 1.2467 loss_cls: 0.2317 loss_bbox: 0.5031 loss_dfl: 0.1978 loss_ld: 0.3142
2023/07/13 11:43:28 - mmengine - INFO - Epoch(train) [10][ 400/3139] lr: 1.2500e-04 eta: 0:48:39 time: 0.3238 data_time: 0.0040 memory: 719 loss: 1.2236 loss_cls: 0.2535 loss_bbox: 0.5045 loss_dfl: 0.1956 loss_ld: 0.2701
2023/07/13 11:43:44 - mmengine - INFO - Epoch(train) [10][ 450/3139] lr: 1.2500e-04 eta: 0:48:22 time: 0.3247 data_time: 0.0041 memory: 726 loss: 1.2078 loss_cls: 0.2553 loss_bbox: 0.4639 loss_dfl: 0.1916 loss_ld: 0.2971
2023/07/13 11:44:00 - mmengine - INFO - Epoch(train) [10][ 500/3139] lr: 1.2500e-04 eta: 0:48:06 time: 0.3234 data_time: 0.0043 memory: 721 loss: 1.2389 loss_cls: 0.2589 loss_bbox: 0.5026 loss_dfl: 0.1938 loss_ld: 0.2835
2023/07/13 11:44:16 - mmengine - INFO - Epoch(train) [10][ 550/3139] lr: 1.2500e-04 eta: 0:47:50 time: 0.3177 data_time: 0.0042 memory: 733 loss: 1.1247 loss_cls: 0.2339 loss_bbox: 0.4444 loss_dfl: 0.1812 loss_ld: 0.2652
2023/07/13 11:44:32 - mmengine - INFO - Epoch(train) [10][ 600/3139] lr: 1.2500e-04 eta: 0:47:34 time: 0.3230 data_time: 0.0042 memory: 730 loss: 1.2644 loss_cls: 0.2595 loss_bbox: 0.4865 loss_dfl: 0.1979 loss_ld: 0.3205
2023/07/13 11:44:49 - mmengine - INFO - Epoch(train) [10][ 650/3139] lr: 1.2500e-04 eta: 0:47:18 time: 0.3237 data_time: 0.0041 memory: 731 loss: 1.1762 loss_cls: 0.2540 loss_bbox: 0.4496 loss_dfl: 0.1861 loss_ld: 0.2865
2023/07/13 11:45:05 - mmengine - INFO - Epoch(train) [10][ 700/3139] lr: 1.2500e-04 eta: 0:47:01 time: 0.3223 data_time: 0.0050 memory: 738 loss: 1.1346 loss_cls: 0.2409 loss_bbox: 0.4590 loss_dfl: 0.1840 loss_ld: 0.2507
2023/07/13 11:45:21 - mmengine - INFO - Exp name: ld_gfl_r18_r101_fpn_1x_vis_20230713_085933
2023/07/13 11:45:21 - mmengine - INFO - Epoch(train) [10][ 750/3139] lr: 1.2500e-04 eta: 0:46:45 time: 0.3254 data_time: 0.0049 memory: 716 loss: 1.1291 loss_cls: 0.2478 loss_bbox: 0.4283 loss_dfl: 0.1809 loss_ld: 0.2721
2023/07/13 11:45:37 - mmengine - INFO - Epoch(train) [10][ 800/3139] lr: 1.2500e-04 eta: 0:46:29 time: 0.3169 data_time: 0.0044 memory: 735 loss: 1.2224 loss_cls: 0.2396 loss_bbox: 0.4718 loss_dfl: 0.1860 loss_ld: 0.3249
2023/07/13 11:45:53 - mmengine - INFO - Epoch(train) [10][ 850/3139] lr: 1.2500e-04 eta: 0:46:13 time: 0.3186 data_time: 0.0051 memory: 721 loss: 1.2213 loss_cls: 0.2643 loss_bbox: 0.4687 loss_dfl: 0.1945 loss_ld: 0.2938
2023/07/13 11:46:09 - mmengine - INFO - Epoch(train) [10][ 900/3139] lr: 1.2500e-04 eta: 0:45:56 time: 0.3238 data_time: 0.0060 memory: 747 loss: 1.2673 loss_cls: 0.2467 loss_bbox: 0.4713 loss_dfl: 0.1930 loss_ld: 0.3562
2023/07/13 11:46:25 - mmengine - INFO - Epoch(train) [10][ 950/3139] lr: 1.2500e-04 eta: 0:45:40 time: 0.3268 data_time: 0.0055 memory: 724 loss: 1.2589 loss_cls: 0.2650 loss_bbox: 0.5004 loss_dfl: 0.1986 loss_ld: 0.2949
2023/07/13 11:46:42 - mmengine - INFO - Epoch(train) [10][1000/3139] lr: 1.2500e-04 eta: 0:45:24 time: 0.3237 data_time: 0.0042 memory: 728 loss: 1.2755 loss_cls: 0.2458 loss_bbox: 0.5185 loss_dfl: 0.1982 loss_ld: 0.3130
2023/07/13 11:46:58 - mmengine - INFO - Epoch(train) [10][1050/3139] lr: 1.2500e-04 eta: 0:45:08 time: 0.3273 data_time: 0.0045 memory: 727 loss: 1.2150 loss_cls: 0.2514 loss_bbox: 0.4788 loss_dfl: 0.1934 loss_ld: 0.2913
2023/07/13 11:47:14 - mmengine - INFO - Epoch(train) [10][1100/3139] lr: 1.2500e-04 eta: 0:44:52 time: 0.3266 data_time: 0.0053 memory: 722 loss: 1.2518 loss_cls: 0.2733 loss_bbox: 0.4935 loss_dfl: 0.1951 loss_ld: 0.2898
2023/07/13 11:47:30 - mmengine - INFO - Epoch(train) [10][1150/3139] lr: 1.2500e-04 eta: 0:44:36 time: 0.3236 data_time: 0.0043 memory: 717 loss: 1.2149 loss_cls: 0.2582 loss_bbox: 0.4822 loss_dfl: 0.1935 loss_ld: 0.2809
2023/07/13 11:47:47 - mmengine - INFO - Epoch(train) [10][1200/3139] lr: 1.2500e-04 eta: 0:44:20 time: 0.3238 data_time: 0.0044 memory: 716 loss: 1.1944 loss_cls: 0.2540 loss_bbox: 0.4827 loss_dfl: 0.1962 loss_ld: 0.2614
2023/07/13 11:48:03 - mmengine - INFO - Epoch(train) [10][1250/3139] lr: 1.2500e-04 eta: 0:44:03 time: 0.3261 data_time: 0.0050 memory: 717 loss: 1.1337 loss_cls: 0.2663 loss_bbox: 0.4173 loss_dfl: 0.1811 loss_ld: 0.2690
2023/07/13 11:48:19 - mmengine - INFO - Epoch(train) [10][1300/3139] lr: 1.2500e-04 eta: 0:43:47 time: 0.3212 data_time: 0.0038 memory: 721 loss: 1.2079 loss_cls: 0.2347 loss_bbox: 0.4630 loss_dfl: 0.1886 loss_ld: 0.3216
2023/07/13 11:48:36 - mmengine - INFO - Epoch(train) [10][1350/3139] lr: 1.2500e-04 eta: 0:43:31 time: 0.3293 data_time: 0.0064 memory: 729 loss: 1.2399 loss_cls: 0.2432 loss_bbox: 0.4776 loss_dfl: 0.1928 loss_ld: 0.3262
2023/07/13 11:48:52 - mmengine - INFO - Epoch(train) [10][1400/3139] lr: 1.2500e-04 eta: 0:43:15 time: 0.3264 data_time: 0.0063 memory: 736 loss: 1.2599 loss_cls: 0.2348 loss_bbox: 0.4713 loss_dfl: 0.1933 loss_ld: 0.3605
2023/07/13 11:49:08 - mmengine - INFO - Epoch(train) [10][1450/3139] lr: 1.2500e-04 eta: 0:42:59 time: 0.3252 data_time: 0.0043 memory: 724 loss: 1.2432 loss_cls: 0.2582 loss_bbox: 0.4671 loss_dfl: 0.1946 loss_ld: 0.3232
2023/07/13 11:49:24 - mmengine - INFO - Epoch(train) [10][1500/3139] lr: 1.2500e-04 eta: 0:42:43 time: 0.3219 data_time: 0.0037 memory: 739 loss: 1.1955 loss_cls: 0.2486 loss_bbox: 0.4817 loss_dfl: 0.1901 loss_ld: 0.2751
2023/07/13 11:49:40 - mmengine - INFO - Epoch(train) [10][1550/3139] lr: 1.2500e-04 eta: 0:42:26 time: 0.3238 data_time: 0.0051 memory: 725 loss: 1.1882 loss_cls: 0.2570 loss_bbox: 0.4774 loss_dfl: 0.1898 loss_ld: 0.2641
2023/07/13 11:49:57 - mmengine - INFO - Epoch(train) [10][1600/3139] lr: 1.2500e-04 eta: 0:42:10 time: 0.3236 data_time: 0.0046 memory: 718 loss: 1.1636 loss_cls: 0.2474 loss_bbox: 0.4599 loss_dfl: 0.1869 loss_ld: 0.2693
2023/07/13 11:50:13 - mmengine - INFO - Epoch(train) [10][1650/3139] lr: 1.2500e-04 eta: 0:41:54 time: 0.3239 data_time: 0.0040 memory: 718 loss: 1.2405 loss_cls: 0.2479 loss_bbox: 0.4701 loss_dfl: 0.1916 loss_ld: 0.3309
2023/07/13 11:50:29 - mmengine - INFO - Epoch(train) [10][1700/3139] lr: 1.2500e-04 eta: 0:41:38 time: 0.3234 data_time: 0.0044 memory: 725 loss: 1.1332 loss_cls: 0.2422 loss_bbox: 0.4520 loss_dfl: 0.1813 loss_ld: 0.2577
2023/07/13 11:50:45 - mmengine - INFO - Exp name: ld_gfl_r18_r101_fpn_1x_vis_20230713_085933
2023/07/13 11:50:45 - mmengine - INFO - Epoch(train) [10][1750/3139] lr: 1.2500e-04 eta: 0:41:22 time: 0.3260 data_time: 0.0047 memory: 719 loss: 1.1978 loss_cls: 0.2409 loss_bbox: 0.4930 loss_dfl: 0.1872 loss_ld: 0.2767
2023/07/13 11:51:01 - mmengine - INFO - Epoch(train) [10][1800/3139] lr: 1.2500e-04 eta: 0:41:05 time: 0.3208 data_time: 0.0043 memory: 728 loss: 1.1760 loss_cls: 0.2386 loss_bbox: 0.5025 loss_dfl: 0.1911 loss_ld: 0.2438
2023/07/13 11:51:18 - mmengine - INFO - Epoch(train) [10][1850/3139] lr: 1.2500e-04 eta: 0:40:49 time: 0.3259 data_time: 0.0053 memory: 731 loss: 1.2506 loss_cls: 0.2527 loss_bbox: 0.4925 loss_dfl: 0.1982 loss_ld: 0.3072
2023/07/13 11:51:34 - mmengine - INFO - Epoch(train) [10][1900/3139] lr: 1.2500e-04 eta: 0:40:33 time: 0.3263 data_time: 0.0045 memory: 730 loss: 1.2023 loss_cls: 0.2507 loss_bbox: 0.4673 loss_dfl: 0.1889 loss_ld: 0.2953
2023/07/13 11:51:50 - mmengine - INFO - Epoch(train) [10][1950/3139] lr: 1.2500e-04 eta: 0:40:17 time: 0.3298 data_time: 0.0060 memory: 738 loss: 1.1793 loss_cls: 0.2470 loss_bbox: 0.4623 loss_dfl: 0.1871 loss_ld: 0.2829
2023/07/13 11:52:07 - mmengine - INFO - Epoch(train) [10][2000/3139] lr: 1.2500e-04 eta: 0:40:01 time: 0.3240 data_time: 0.0043 memory: 724 loss: 1.1872 loss_cls: 0.2568 loss_bbox: 0.4895 loss_dfl: 0.1919 loss_ld: 0.2490
2023/07/13 11:52:23 - mmengine - INFO - Epoch(train) [10][2050/3139] lr: 1.2500e-04 eta: 0:39:45 time: 0.3240 data_time: 0.0039 memory: 720 loss: 1.1916 loss_cls: 0.2387 loss_bbox: 0.4883 loss_dfl: 0.1888 loss_ld: 0.2757
2023/07/13 11:52:39 - mmengine - INFO - Epoch(train) [10][2100/3139] lr: 1.2500e-04 eta: 0:39:28 time: 0.3239 data_time: 0.0047 memory: 717 loss: 1.1983 loss_cls: 0.2375 loss_bbox: 0.4656 loss_dfl: 0.1903 loss_ld: 0.3049
2023/07/13 11:52:55 - mmengine - INFO - Epoch(train) [10][2150/3139] lr: 1.2500e-04 eta: 0:39:12 time: 0.3249 data_time: 0.0048 memory: 726 loss: 1.2119 loss_cls: 0.2396 loss_bbox: 0.5048 loss_dfl: 0.1951 loss_ld: 0.2724
2023/07/13 11:53:12 - mmengine - INFO - Epoch(train) [10][2200/3139] lr: 1.2500e-04 eta: 0:38:56 time: 0.3252 data_time: 0.0047 memory: 739 loss: 1.2090 loss_cls: 0.2469 loss_bbox: 0.4549 loss_dfl: 0.1907 loss_ld: 0.3164
2023/07/13 11:53:28 - mmengine - INFO - Epoch(train) [10][2250/3139] lr: 1.2500e-04 eta: 0:38:40 time: 0.3252 data_time: 0.0050 memory: 761 loss: 1.2033 loss_cls: 0.2383 loss_bbox: 0.4725 loss_dfl: 0.1897 loss_ld: 0.3027
2023/07/13 11:53:44 - mmengine - INFO - Epoch(train) [10][2300/3139] lr: 1.2500e-04 eta: 0:38:24 time: 0.3275 data_time: 0.0049 memory: 724 loss: 1.2538 loss_cls: 0.2691 loss_bbox: 0.4996 loss_dfl: 0.1958 loss_ld: 0.2894
2023/07/13 11:54:01 - mmengine - INFO - Epoch(train) [10][2350/3139] lr: 1.2500e-04 eta: 0:38:08 time: 0.3254 data_time: 0.0049 memory: 722 loss: 1.1284 loss_cls: 0.2512 loss_bbox: 0.4458 loss_dfl: 0.1864 loss_ld: 0.2451
2023/07/13 11:54:17 - mmengine - INFO - Epoch(train) [10][2400/3139] lr: 1.2500e-04 eta: 0:37:51 time: 0.3202 data_time: 0.0041 memory: 719 loss: 1.2091 loss_cls: 0.2443 loss_bbox: 0.4935 loss_dfl: 0.1875 loss_ld: 0.2837
2023/07/13 11:54:33 - mmengine - INFO - Epoch(train) [10][2450/3139] lr: 1.2500e-04 eta: 0:37:35 time: 0.3249 data_time: 0.0046 memory: 722 loss: 1.2162 loss_cls: 0.2322 loss_bbox: 0.4611 loss_dfl: 0.1911 loss_ld: 0.3317
2023/07/13 11:54:49 - mmengine - INFO - Epoch(train) [10][2500/3139] lr: 1.2500e-04 eta: 0:37:19 time: 0.3235 data_time: 0.0039 memory: 731 loss: 1.2185 loss_cls: 0.2553 loss_bbox: 0.4890 loss_dfl: 0.1977 loss_ld: 0.2764
2023/07/13 11:55:05 - mmengine - INFO - Epoch(train) [10][2550/3139] lr: 1.2500e-04 eta: 0:37:03 time: 0.3227 data_time: 0.0045 memory: 719 loss: 1.2120 loss_cls: 0.2452 loss_bbox: 0.4683 loss_dfl: 0.1921 loss_ld: 0.3065
2023/07/13 11:55:21 - mmengine - INFO - Epoch(train) [10][2600/3139] lr: 1.2500e-04 eta: 0:36:47 time: 0.3232 data_time: 0.0046 memory: 716 loss: 1.2094 loss_cls: 0.2558 loss_bbox: 0.4813 loss_dfl: 0.1893 loss_ld: 0.2830
2023/07/13 11:55:38 - mmengine - INFO - Epoch(train) [10][2650/3139] lr: 1.2500e-04 eta: 0:36:30 time: 0.3238 data_time: 0.0064 memory: 722 loss: 1.1792 loss_cls: 0.2255 loss_bbox: 0.4487 loss_dfl: 0.1868 loss_ld: 0.3183
2023/07/13 11:55:54 - mmengine - INFO - Epoch(train) [10][2700/3139] lr: 1.2500e-04 eta: 0:36:14 time: 0.3228 data_time: 0.0052 memory: 743 loss: 1.2271 loss_cls: 0.2394 loss_bbox: 0.4341 loss_dfl: 0.1881 loss_ld: 0.3655
2023/07/13 11:56:09 - mmengine - INFO - Exp name: ld_gfl_r18_r101_fpn_1x_vis_20230713_085933
2023/07/13 11:56:10 - mmengine - INFO - Epoch(train) [10][2750/3139] lr: 1.2500e-04 eta: 0:35:58 time: 0.3187 data_time: 0.0039 memory: 725 loss: 1.2399 loss_cls: 0.2371 loss_bbox: 0.5109 loss_dfl: 0.1943 loss_ld: 0.2975
2023/07/13 11:56:26 - mmengine - INFO - Epoch(train) [10][2800/3139] lr: 1.2500e-04 eta: 0:35:42 time: 0.3199 data_time: 0.0041 memory: 726 loss: 1.2392 loss_cls: 0.2554 loss_bbox: 0.4999 loss_dfl: 0.1938 loss_ld: 0.2902
2023/07/13 11:56:42 - mmengine - INFO - Epoch(train) [10][2850/3139] lr: 1.2500e-04 eta: 0:35:26 time: 0.3256 data_time: 0.0058 memory: 728 loss: 1.2184 loss_cls: 0.2472 loss_bbox: 0.4856 loss_dfl: 0.1944 loss_ld: 0.2912
2023/07/13 11:56:58 - mmengine - INFO - Epoch(train) [10][2900/3139] lr: 1.2500e-04 eta: 0:35:09 time: 0.3210 data_time: 0.0044 memory: 715 loss: 1.1950 loss_cls: 0.2396 loss_bbox: 0.4839 loss_dfl: 0.1940 loss_ld: 0.2774
2023/07/13 11:57:14 - mmengine - INFO - Epoch(train) [10][2950/3139] lr: 1.2500e-04 eta: 0:34:53 time: 0.3232 data_time: 0.0038 memory: 728 loss: 1.2083 loss_cls: 0.2173 loss_bbox: 0.4828 loss_dfl: 0.1903 loss_ld: 0.3179
2023/07/13 11:57:30 - mmengine - INFO - Epoch(train) [10][3000/3139] lr: 1.2500e-04 eta: 0:34:37 time: 0.3238 data_time: 0.0040 memory: 717 loss: 1.1855 loss_cls: 0.2478 loss_bbox: 0.4771 loss_dfl: 0.1898 loss_ld: 0.2708
2023/07/13 11:57:47 - mmengine - INFO - Epoch(train) [10][3050/3139] lr: 1.2500e-04 eta: 0:34:21 time: 0.3271 data_time: 0.0046 memory: 717 loss: 1.1969 loss_cls: 0.2554 loss_bbox: 0.4589 loss_dfl: 0.1901 loss_ld: 0.2926
2023/07/13 11:58:03 - mmengine - INFO - Epoch(train) [10][3100/3139] lr: 1.2500e-04 eta: 0:34:05 time: 0.3248 data_time: 0.0042 memory: 719 loss: 1.1892 loss_cls: 0.2525 loss_bbox: 0.4341 loss_dfl: 0.1891 loss_ld: 0.3134
2023/07/13 11:58:15 - mmengine - INFO - Exp name: ld_gfl_r18_r101_fpn_1x_vis_20230713_085933
2023/07/13 11:58:15 - mmengine - INFO - Saving checkpoint at 10 epochs
2023/07/13 11:58:23 - mmengine - INFO - Epoch(val) [10][ 50/548] eta: 0:00:37 time: 0.0751 data_time: 0.0019 memory: 731
2023/07/13 11:58:26 - mmengine - INFO - Epoch(val) [10][100/548] eta: 0:00:33 time: 0.0737 data_time: 0.0014 memory: 497
2023/07/13 11:58:30 - mmengine - INFO - Epoch(val) [10][150/548] eta: 0:00:29 time: 0.0739 data_time: 0.0014 memory: 497
2023/07/13 11:58:34 - mmengine - INFO - Epoch(val) [10][200/548] eta: 0:00:25 time: 0.0736 data_time: 0.0014 memory: 497
2023/07/13 11:58:37 - mmengine - INFO - Epoch(val) [10][250/548] eta: 0:00:22 time: 0.0743 data_time: 0.0014 memory: 497
2023/07/13 11:58:41 - mmengine - INFO - Epoch(val) [10][300/548] eta: 0:00:18 time: 0.0733 data_time: 0.0014 memory: 497
2023/07/13 11:58:45 - mmengine - INFO - Epoch(val) [10][350/548] eta: 0:00:14 time: 0.0735 data_time: 0.0013 memory: 497
2023/07/13 11:58:48 - mmengine - INFO - Epoch(val) [10][400/548] eta: 0:00:10 time: 0.0740 data_time: 0.0014 memory: 497
2023/07/13 11:58:52 - mmengine - INFO - Epoch(val) [10][450/548] eta: 0:00:07 time: 0.0760 data_time: 0.0015 memory: 497
2023/07/13 11:58:56 - mmengine - INFO - Epoch(val) [10][500/548] eta: 0:00:03 time: 0.0783 data_time: 0.0015 memory: 497
2023/07/13 11:59:01 - mmengine - INFO - Evaluating bbox...
2023/07/13 11:59:15 - mmengine - INFO - bbox_mAP_copypaste: 0.106 0.176 0.114 0.030 0.152 0.294
2023/07/13 11:59:16 - mmengine - INFO - Epoch(val) [10][548/548] coco/bbox_mAP: 0.1060 coco/bbox_mAP_50: 0.1760 coco/bbox_mAP_75: 0.1140 coco/bbox_mAP_s: 0.0300 coco/bbox_mAP_m: 0.1520 coco/bbox_mAP_l: 0.2940 data_time: 0.0015 time: 0.0750
2023/07/13 11:59:33 - mmengine - INFO - Epoch(train) [11][ 50/3139] lr: 1.2500e-04 eta: 0:33:36 time: 0.3487 data_time: 0.0284 memory: 727 loss: 1.2236 loss_cls: 0.2321 loss_bbox: 0.5062 loss_dfl: 0.1933 loss_ld: 0.2921
2023/07/13 11:59:49 - mmengine - INFO - Epoch(train) [11][ 100/3139] lr: 1.2500e-04 eta: 0:33:20 time: 0.3251 data_time: 0.0052 memory: 726 loss: 1.1504 loss_cls: 0.2462 loss_bbox: 0.4401 loss_dfl: 0.1880 loss_ld: 0.2761
2023/07/13 12:00:05 - mmengine - INFO - Epoch(train) [11][ 150/3139] lr: 1.2500e-04 eta: 0:33:04 time: 0.3224 data_time: 0.0041 memory: 717 loss: 1.1470 loss_cls: 0.2413 loss_bbox: 0.4835 loss_dfl: 0.1846 loss_ld: 0.2375
2023/07/13 12:00:22 - mmengine - INFO - Epoch(train) [11][ 200/3139] lr: 1.2500e-04 eta: 0:32:47 time: 0.3252 data_time: 0.0050 memory: 720 loss: 1.1683 loss_cls: 0.2531 loss_bbox: 0.4735 loss_dfl: 0.1865 loss_ld: 0.2552
2023/07/13 12:00:38 - mmengine - INFO - Epoch(train) [11][ 250/3139] lr: 1.2500e-04 eta: 0:32:31 time: 0.3233 data_time: 0.0042 memory: 725 loss: 1.1745 loss_cls: 0.2475 loss_bbox: 0.4605 loss_dfl: 0.1897 loss_ld: 0.2767
2023/07/13 12:00:54 - mmengine - INFO - Epoch(train) [11][ 300/3139] lr: 1.2500e-04 eta: 0:32:15 time: 0.3245 data_time: 0.0038 memory: 724 loss: 1.1941 loss_cls: 0.2468 loss_bbox: 0.4622 loss_dfl: 0.1886 loss_ld: 0.2966
2023/07/13 12:01:10 - mmengine - INFO - Epoch(train) [11][ 350/3139] lr: 1.2500e-04 eta: 0:31:59 time: 0.3228 data_time: 0.0043 memory: 719 loss: 1.2189 loss_cls: 0.2594 loss_bbox: 0.4448 loss_dfl: 0.1897 loss_ld: 0.3250
2023/07/13 12:01:26 - mmengine - INFO - Epoch(train) [11][ 400/3139] lr: 1.2500e-04 eta: 0:31:43 time: 0.3244 data_time: 0.0045 memory: 723 loss: 1.1743 loss_cls: 0.2434 loss_bbox: 0.4510 loss_dfl: 0.1887 loss_ld: 0.2913
2023/07/13 12:01:44 - mmengine - INFO - Epoch(train) [11][ 450/3139] lr: 1.2500e-04 eta: 0:31:27 time: 0.3577 data_time: 0.0383 memory: 724 loss: 1.1679 loss_cls: 0.2518 loss_bbox: 0.4564 loss_dfl: 0.1895 loss_ld: 0.2702
2023/07/13 12:02:01 - mmengine - INFO - Epoch(train) [11][ 500/3139] lr: 1.2500e-04 eta: 0:31:11 time: 0.3266 data_time: 0.0059 memory: 747 loss: 1.2163 loss_cls: 0.2450 loss_bbox: 0.4849 loss_dfl: 0.1891 loss_ld: 0.2974
2023/07/13 12:02:17 - mmengine - INFO - Epoch(train) [11][ 550/3139] lr: 1.2500e-04 eta: 0:30:54 time: 0.3242 data_time: 0.0039 memory: 725 loss: 1.2020 loss_cls: 0.2539 loss_bbox: 0.4576 loss_dfl: 0.1925 loss_ld: 0.2980
2023/07/13 12:02:33 - mmengine - INFO - Epoch(train) [11][ 600/3139] lr: 1.2500e-04 eta: 0:30:38 time: 0.3258 data_time: 0.0046 memory: 728 loss: 1.1803 loss_cls: 0.2555 loss_bbox: 0.4708 loss_dfl: 0.1861 loss_ld: 0.2680
2023/07/13 12:02:36 - mmengine - INFO - Exp name: ld_gfl_r18_r101_fpn_1x_vis_20230713_085933
2023/07/13 12:02:49 - mmengine - INFO - Epoch(train) [11][ 650/3139] lr: 1.2500e-04 eta: 0:30:22 time: 0.3259 data_time: 0.0049 memory: 725 loss: 1.2111 loss_cls: 0.2388 loss_bbox: 0.5050 loss_dfl: 0.1942 loss_ld: 0.2731
2023/07/13 12:03:06 - mmengine - INFO - Epoch(train) [11][ 700/3139] lr: 1.2500e-04 eta: 0:30:06 time: 0.3242 data_time: 0.0045 memory: 749 loss: 1.1850 loss_cls: 0.2377 loss_bbox: 0.4485 loss_dfl: 0.1850 loss_ld: 0.3139
2023/07/13 12:03:22 - mmengine - INFO - Epoch(train) [11][ 750/3139] lr: 1.2500e-04 eta: 0:29:50 time: 0.3257 data_time: 0.0044 memory: 717 loss: 1.2151 loss_cls: 0.2623 loss_bbox: 0.4750 loss_dfl: 0.1907 loss_ld: 0.2872
2023/07/13 12:03:38 - mmengine - INFO - Epoch(train) [11][ 800/3139] lr: 1.2500e-04 eta: 0:29:34 time: 0.3236 data_time: 0.0038 memory: 739 loss: 1.2042 loss_cls: 0.2293 loss_bbox: 0.4492 loss_dfl: 0.1837 loss_ld: 0.3420
2023/07/13 12:03:54 - mmengine - INFO - Epoch(train) [11][ 850/3139] lr: 1.2500e-04 eta: 0:29:17 time: 0.3254 data_time: 0.0044 memory: 730 loss: 1.2148 loss_cls: 0.2354 loss_bbox: 0.4821 loss_dfl: 0.1902 loss_ld: 0.3072
2023/07/13 12:04:11 - mmengine - INFO - Epoch(train) [11][ 900/3139] lr: 1.2500e-04 eta: 0:29:01 time: 0.3247 data_time: 0.0048 memory: 734 loss: 1.1883 loss_cls: 0.2449 loss_bbox: 0.4806 loss_dfl: 0.1944 loss_ld: 0.2684
2023/07/13 12:04:27 - mmengine - INFO - Epoch(train) [11][ 950/3139] lr: 1.2500e-04 eta: 0:28:45 time: 0.3226 data_time: 0.0041 memory: 730 loss: 1.1513 loss_cls: 0.2370 loss_bbox: 0.4630 loss_dfl: 0.1874 loss_ld: 0.2639
2023/07/13 12:04:43 - mmengine - INFO - Epoch(train) [11][1000/3139] lr: 1.2500e-04 eta: 0:28:29 time: 0.3265 data_time: 0.0057 memory: 728 loss: 1.1948 loss_cls: 0.2332 loss_bbox: 0.4887 loss_dfl: 0.1911 loss_ld: 0.2818
2023/07/13 12:04:59 - mmengine - INFO - Epoch(train) [11][1050/3139] lr: 1.2500e-04 eta: 0:28:13 time: 0.3223 data_time: 0.0046 memory: 714 loss: 1.1708 loss_cls: 0.2593 loss_bbox: 0.4735 loss_dfl: 0.1828 loss_ld: 0.2552
2023/07/13 12:05:15 - mmengine - INFO - Epoch(train) [11][1100/3139] lr: 1.2500e-04 eta: 0:27:56 time: 0.3218 data_time: 0.0038 memory: 731 loss: 1.1562 loss_cls: 0.2368 loss_bbox: 0.4708 loss_dfl: 0.1857 loss_ld: 0.2628
2023/07/13 12:05:32 - mmengine - INFO - Epoch(train) [11][1150/3139] lr: 1.2500e-04 eta: 0:27:40 time: 0.3229 data_time: 0.0046 memory: 728 loss: 1.2290 loss_cls: 0.2674 loss_bbox: 0.4916 loss_dfl: 0.1941 loss_ld: 0.2760
2023/07/13 12:05:48 - mmengine - INFO - Epoch(train) [11][1200/3139] lr: 1.2500e-04 eta: 0:27:24 time: 0.3248 data_time: 0.0043 memory: 722 loss: 1.2213 loss_cls: 0.2409 loss_bbox: 0.5026 loss_dfl: 0.1915 loss_ld: 0.2864
2023/07/13 12:06:04 - mmengine - INFO - Epoch(train) [11][1250/3139] lr: 1.2500e-04 eta: 0:27:08 time: 0.3288 data_time: 0.0062 memory: 730 loss: 1.2993 loss_cls: 0.2443 loss_bbox: 0.5213 loss_dfl: 0.2067 loss_ld: 0.3271
2023/07/13 12:06:21 - mmengine - INFO - Epoch(train) [11][1300/3139] lr: 1.2500e-04 eta: 0:26:52 time: 0.3253 data_time: 0.0058 memory: 714 loss: 1.2402 loss_cls: 0.2616 loss_bbox: 0.4825 loss_dfl: 0.1979 loss_ld: 0.2982
2023/07/13 12:06:37 - mmengine - INFO - Epoch(train) [11][1350/3139] lr: 1.2500e-04 eta: 0:26:35 time: 0.3207 data_time: 0.0042 memory: 724 loss: 1.1523 loss_cls: 0.2527 loss_bbox: 0.4509 loss_dfl: 0.1827 loss_ld: 0.2659
2023/07/13 12:06:53 - mmengine - INFO - Epoch(train) [11][1400/3139] lr: 1.2500e-04 eta: 0:26:19 time: 0.3224 data_time: 0.0045 memory: 752 loss: 1.2442 loss_cls: 0.2456 loss_bbox: 0.4988 loss_dfl: 0.1938 loss_ld: 0.3061
2023/07/13 12:07:09 - mmengine - INFO - Epoch(train) [11][1450/3139] lr: 1.2500e-04 eta: 0:26:03 time: 0.3232 data_time: 0.0042 memory: 721 loss: 1.2260 loss_cls: 0.2471 loss_bbox: 0.5113 loss_dfl: 0.1932 loss_ld: 0.2743
2023/07/13 12:07:25 - mmengine - INFO - Epoch(train) [11][1500/3139] lr: 1.2500e-04 eta: 0:25:47 time: 0.3248 data_time: 0.0042 memory: 743 loss: 1.2419 loss_cls: 0.2638 loss_bbox: 0.4905 loss_dfl: 0.1925 loss_ld: 0.2951
2023/07/13 12:07:41 - mmengine - INFO - Epoch(train) [11][1550/3139] lr: 1.2500e-04 eta: 0:25:31 time: 0.3256 data_time: 0.0038 memory: 719 loss: 1.2683 loss_cls: 0.2752 loss_bbox: 0.4866 loss_dfl: 0.1918 loss_ld: 0.3146
2023/07/13 12:07:58 - mmengine - INFO - Epoch(train) [11][1600/3139] lr: 1.2500e-04 eta: 0:25:15 time: 0.3274 data_time: 0.0056 memory: 725 loss: 1.2503 loss_cls: 0.2761 loss_bbox: 0.4910 loss_dfl: 0.1931 loss_ld: 0.2902
2023/07/13 12:08:01 - mmengine - INFO - Exp name: ld_gfl_r18_r101_fpn_1x_vis_20230713_085933
2023/07/13 12:08:14 - mmengine - INFO - Epoch(train) [11][1650/3139] lr: 1.2500e-04 eta: 0:24:58 time: 0.3263 data_time: 0.0052 memory: 728 loss: 1.1922 loss_cls: 0.2304 loss_bbox: 0.4700 loss_dfl: 0.1903 loss_ld: 0.3015
2023/07/13 12:08:30 - mmengine - INFO - Epoch(train) [11][1700/3139] lr: 1.2500e-04 eta: 0:24:42 time: 0.3236 data_time: 0.0035 memory: 762 loss: 1.1163 loss_cls: 0.2413 loss_bbox: 0.4113 loss_dfl: 0.1821 loss_ld: 0.2817
2023/07/13 12:08:46 - mmengine - INFO - Epoch(train) [11][1750/3139] lr: 1.2500e-04 eta: 0:24:26 time: 0.3233 data_time: 0.0041 memory: 722 loss: 1.2238 loss_cls: 0.2456 loss_bbox: 0.4865 loss_dfl: 0.1917 loss_ld: 0.3001
2023/07/13 12:09:03 - mmengine - INFO - Epoch(train) [11][1800/3139] lr: 1.2500e-04 eta: 0:24:10 time: 0.3250 data_time: 0.0042 memory: 733 loss: 1.2372 loss_cls: 0.2599 loss_bbox: 0.4670 loss_dfl: 0.1928 loss_ld: 0.3175
2023/07/13 12:09:19 - mmengine - INFO - Epoch(train) [11][1850/3139] lr: 1.2500e-04 eta: 0:23:54 time: 0.3225 data_time: 0.0050 memory: 717 loss: 1.1934 loss_cls: 0.2624 loss_bbox: 0.4707 loss_dfl: 0.1969 loss_ld: 0.2635
2023/07/13 12:09:35 - mmengine - INFO - Epoch(train) [11][1900/3139] lr: 1.2500e-04 eta: 0:23:37 time: 0.3252 data_time: 0.0041 memory: 726 loss: 1.1468 loss_cls: 0.2350 loss_bbox: 0.4407 loss_dfl: 0.1833 loss_ld: 0.2877
2023/07/13 12:09:51 - mmengine - INFO - Epoch(train) [11][1950/3139] lr: 1.2500e-04 eta: 0:23:21 time: 0.3243 data_time: 0.0040 memory: 721 loss: 1.2369 loss_cls: 0.2493 loss_bbox: 0.4573 loss_dfl: 0.1944 loss_ld: 0.3359
2023/07/13 12:10:08 - mmengine - INFO - Epoch(train) [11][2000/3139] lr: 1.2500e-04 eta: 0:23:05 time: 0.3256 data_time: 0.0049 memory: 722 loss: 1.2107 loss_cls: 0.2486 loss_bbox: 0.4535 loss_dfl: 0.1858 loss_ld: 0.3228
2023/07/13 12:10:24 - mmengine - INFO - Epoch(train) [11][2050/3139] lr: 1.2500e-04 eta: 0:22:49 time: 0.3243 data_time: 0.0048 memory: 724 loss: 1.1932 loss_cls: 0.2423 loss_bbox: 0.4826 loss_dfl: 0.1900 loss_ld: 0.2782
2023/07/13 12:10:40 - mmengine - INFO - Epoch(train) [11][2100/3139] lr: 1.2500e-04 eta: 0:22:33 time: 0.3235 data_time: 0.0046 memory: 720 loss: 1.1958 loss_cls: 0.2405 loss_bbox: 0.4563 loss_dfl: 0.1899 loss_ld: 0.3091
2023/07/13 12:10:56 - mmengine - INFO - Epoch(train) [11][2150/3139] lr: 1.2500e-04 eta: 0:22:16 time: 0.3239 data_time: 0.0039 memory: 724 loss: 1.1824 loss_cls: 0.2463 loss_bbox: 0.4620 loss_dfl: 0.1882 loss_ld: 0.2860
2023/07/13 12:11:12 - mmengine - INFO - Epoch(train) [11][2200/3139] lr: 1.2500e-04 eta: 0:22:00 time: 0.3225 data_time: 0.0053 memory: 728 loss: 1.1747 loss_cls: 0.2408 loss_bbox: 0.4678 loss_dfl: 0.1860 loss_ld: 0.2801
2023/07/13 12:11:28 - mmengine - INFO - Epoch(train) [11][2250/3139] lr: 1.2500e-04 eta: 0:21:44 time: 0.3220 data_time: 0.0046 memory: 722 loss: 1.2942 loss_cls: 0.2577 loss_bbox: 0.5115 loss_dfl: 0.1989 loss_ld: 0.3262
2023/07/13 12:11:45 - mmengine - INFO - Epoch(train) [11][2300/3139] lr: 1.2500e-04 eta: 0:21:28 time: 0.3216 data_time: 0.0043 memory: 718 loss: 1.2191 loss_cls: 0.2533 loss_bbox: 0.4808 loss_dfl: 0.1921 loss_ld: 0.2928
2023/07/13 12:12:01 - mmengine - INFO - Epoch(train) [11][2350/3139] lr: 1.2500e-04 eta: 0:21:12 time: 0.3228 data_time: 0.0047 memory: 720 loss: 1.1961 loss_cls: 0.2387 loss_bbox: 0.4615 loss_dfl: 0.1874 loss_ld: 0.3085
2023/07/13 12:12:17 - mmengine - INFO - Epoch(train) [11][2400/3139] lr: 1.2500e-04 eta: 0:20:55 time: 0.3216 data_time: 0.0036 memory: 721 loss: 1.2196 loss_cls: 0.2414 loss_bbox: 0.4733 loss_dfl: 0.1896 loss_ld: 0.3152
2023/07/13 12:12:33 - mmengine - INFO - Epoch(train) [11][2450/3139] lr: 1.2500e-04 eta: 0:20:39 time: 0.3261 data_time: 0.0055 memory: 728 loss: 1.1946 loss_cls: 0.2423 loss_bbox: 0.4694 loss_dfl: 0.1892 loss_ld: 0.2937
2023/07/13 12:12:49 - mmengine - INFO - Epoch(train) [11][2500/3139] lr: 1.2500e-04 eta: 0:20:23 time: 0.3230 data_time: 0.0040 memory: 738 loss: 1.1457 loss_cls: 0.2427 loss_bbox: 0.4468 loss_dfl: 0.1893 loss_ld: 0.2670
2023/07/13 12:13:06 - mmengine - INFO - Epoch(train) [11][2550/3139] lr: 1.2500e-04 eta: 0:20:07 time: 0.3255 data_time: 0.0049 memory: 723 loss: 1.2458 loss_cls: 0.2291 loss_bbox: 0.4753 loss_dfl: 0.1883 loss_ld: 0.3531
2023/07/13 12:13:22 - mmengine - INFO - Epoch(train) [11][2600/3139] lr: 1.2500e-04 eta: 0:19:51 time: 0.3198 data_time: 0.0040 memory: 727 loss: 1.2106 loss_cls: 0.2371 loss_bbox: 0.4990 loss_dfl: 0.1968 loss_ld: 0.2777
2023/07/13 12:13:25 - mmengine - INFO - Exp name: ld_gfl_r18_r101_fpn_1x_vis_20230713_085933
2023/07/13 12:13:38 - mmengine - INFO - Epoch(train) [11][2650/3139] lr: 1.2500e-04 eta: 0:19:34 time: 0.3223 data_time: 0.0039 memory: 736 loss: 1.1502 loss_cls: 0.2485 loss_bbox: 0.4699 loss_dfl: 0.1888 loss_ld: 0.2430
2023/07/13 12:13:54 - mmengine - INFO - Epoch(train) [11][2700/3139] lr: 1.2500e-04 eta: 0:19:18 time: 0.3238 data_time: 0.0044 memory: 736 loss: 1.2106 loss_cls: 0.2508 loss_bbox: 0.4743 loss_dfl: 0.1900 loss_ld: 0.2956
2023/07/13 12:14:10 - mmengine - INFO - Epoch(train) [11][2750/3139] lr: 1.2500e-04 eta: 0:19:02 time: 0.3257 data_time: 0.0061 memory: 731 loss: 1.1862 loss_cls: 0.2468 loss_bbox: 0.4687 loss_dfl: 0.1928 loss_ld: 0.2779
2023/07/13 12:14:26 - mmengine - INFO - Epoch(train) [11][2800/3139] lr: 1.2500e-04 eta: 0:18:46 time: 0.3235 data_time: 0.0047 memory: 724 loss: 1.1403 loss_cls: 0.2295 loss_bbox: 0.4768 loss_dfl: 0.1847 loss_ld: 0.2492
2023/07/13 12:14:43 - mmengine - INFO - Epoch(train) [11][2850/3139] lr: 1.2500e-04 eta: 0:18:30 time: 0.3255 data_time: 0.0050 memory: 716 loss: 1.1358 loss_cls: 0.2390 loss_bbox: 0.4549 loss_dfl: 0.1825 loss_ld: 0.2594
2023/07/13 12:14:59 - mmengine - INFO - Epoch(train) [11][2900/3139] lr: 1.2500e-04 eta: 0:18:13 time: 0.3206 data_time: 0.0043 memory: 729 loss: 1.2350 loss_cls: 0.2442 loss_bbox: 0.5052 loss_dfl: 0.1927 loss_ld: 0.2929
2023/07/13 12:15:15 - mmengine - INFO - Epoch(train) [11][2950/3139] lr: 1.2500e-04 eta: 0:17:57 time: 0.3234 data_time: 0.0039 memory: 738 loss: 1.2387 loss_cls: 0.2609 loss_bbox: 0.5123 loss_dfl: 0.1933 loss_ld: 0.2722
2023/07/13 12:15:31 - mmengine - INFO - Epoch(train) [11][3000/3139] lr: 1.2500e-04 eta: 0:17:41 time: 0.3195 data_time: 0.0043 memory: 719 loss: 1.1482 loss_cls: 0.2411 loss_bbox: 0.4766 loss_dfl: 0.1848 loss_ld: 0.2456
2023/07/13 12:15:47 - mmengine - INFO - Epoch(train) [11][3050/3139] lr: 1.2500e-04 eta: 0:17:25 time: 0.3252 data_time: 0.0045 memory: 731 loss: 1.2554 loss_cls: 0.2565 loss_bbox: 0.4805 loss_dfl: 0.1909 loss_ld: 0.3275
2023/07/13 12:16:03 - mmengine - INFO - Epoch(train) [11][3100/3139] lr: 1.2500e-04 eta: 0:17:09 time: 0.3220 data_time: 0.0038 memory: 720 loss: 1.1899 loss_cls: 0.2417 loss_bbox: 0.4601 loss_dfl: 0.1886 loss_ld: 0.2995
2023/07/13 12:16:16 - mmengine - INFO - Exp name: ld_gfl_r18_r101_fpn_1x_vis_20230713_085933
2023/07/13 12:16:16 - mmengine - INFO - Saving checkpoint at 11 epochs
2023/07/13 12:16:22 - mmengine - INFO - Epoch(val) [11][ 50/548] eta: 0:00:37 time: 0.0750 data_time: 0.0019 memory: 717
2023/07/13 12:16:26 - mmengine - INFO - Epoch(val) [11][100/548] eta: 0:00:33 time: 0.0732 data_time: 0.0014 memory: 497
2023/07/13 12:16:30 - mmengine - INFO - Epoch(val) [11][150/548] eta: 0:00:30 time: 0.0783 data_time: 0.0015 memory: 497
2023/07/13 12:16:34 - mmengine - INFO - Epoch(val) [11][200/548] eta: 0:00:26 time: 0.0805 data_time: 0.0016 memory: 497
2023/07/13 12:16:38 - mmengine - INFO - Epoch(val) [11][250/548] eta: 0:00:23 time: 0.0808 data_time: 0.0015 memory: 497
2023/07/13 12:16:42 - mmengine - INFO - Epoch(val) [11][300/548] eta: 0:00:19 time: 0.0801 data_time: 0.0015 memory: 497
2023/07/13 12:16:46 - mmengine - INFO - Epoch(val) [11][350/548] eta: 0:00:15 time: 0.0770 data_time: 0.0015 memory: 497
2023/07/13 12:16:49 - mmengine - INFO - Epoch(val) [11][400/548] eta: 0:00:11 time: 0.0740 data_time: 0.0014 memory: 497
2023/07/13 12:16:53 - mmengine - INFO - Epoch(val) [11][450/548] eta: 0:00:07 time: 0.0752 data_time: 0.0015 memory: 497
2023/07/13 12:16:57 - mmengine - INFO - Epoch(val) [11][500/548] eta: 0:00:03 time: 0.0740 data_time: 0.0014 memory: 497
2023/07/13 12:17:01 - mmengine - INFO - Evaluating bbox...
2023/07/13 12:17:16 - mmengine - INFO - bbox_mAP_copypaste: 0.106 0.178 0.114 0.029 0.150 0.301
2023/07/13 12:17:16 - mmengine - INFO - Epoch(val) [11][548/548] coco/bbox_mAP: 0.1060 coco/bbox_mAP_50: 0.1780 coco/bbox_mAP_75: 0.1140 coco/bbox_mAP_s: 0.0290 coco/bbox_mAP_m: 0.1500 coco/bbox_mAP_l: 0.3010 data_time: 0.0015 time: 0.0765
2023/07/13 12:17:32 - mmengine - INFO - Epoch(train) [12][ 50/3139] lr: 1.2500e-05 eta: 0:16:40 time: 0.3243 data_time: 0.0063 memory: 736 loss: 1.1746 loss_cls: 0.2516 loss_bbox: 0.4843 loss_dfl: 0.1900 loss_ld: 0.2488
2023/07/13 12:17:48 - mmengine - INFO - Epoch(train) [12][ 100/3139] lr: 1.2500e-05 eta: 0:16:24 time: 0.3245 data_time: 0.0042 memory: 720 loss: 1.2346 loss_cls: 0.2458 loss_bbox: 0.4900 loss_dfl: 0.1956 loss_ld: 0.3031
2023/07/13 12:18:04 - mmengine - INFO - Epoch(train) [12][ 150/3139] lr: 1.2500e-05 eta: 0:16:07 time: 0.3205 data_time: 0.0041 memory: 720 loss: 1.2152 loss_cls: 0.2326 loss_bbox: 0.4936 loss_dfl: 0.1930 loss_ld: 0.2959
2023/07/13 12:18:20 - mmengine - INFO - Epoch(train) [12][ 200/3139] lr: 1.2500e-05 eta: 0:15:51 time: 0.3218 data_time: 0.0041 memory: 720 loss: 1.2453 loss_cls: 0.2425 loss_bbox: 0.4880 loss_dfl: 0.1945 loss_ld: 0.3203
2023/07/13 12:18:37 - mmengine - INFO - Epoch(train) [12][ 250/3139] lr: 1.2500e-05 eta: 0:15:35 time: 0.3244 data_time: 0.0043 memory: 715 loss: 1.1671 loss_cls: 0.2438 loss_bbox: 0.4768 loss_dfl: 0.1894 loss_ld: 0.2572
2023/07/13 12:18:53 - mmengine - INFO - Epoch(train) [12][ 300/3139] lr: 1.2500e-05 eta: 0:15:19 time: 0.3231 data_time: 0.0054 memory: 718 loss: 1.0983 loss_cls: 0.2439 loss_bbox: 0.4141 loss_dfl: 0.1789 loss_ld: 0.2615
2023/07/13 12:19:09 - mmengine - INFO - Epoch(train) [12][ 350/3139] lr: 1.2500e-05 eta: 0:15:03 time: 0.3246 data_time: 0.0046 memory: 749 loss: 1.2101 loss_cls: 0.2450 loss_bbox: 0.4672 loss_dfl: 0.1933 loss_ld: 0.3046
2023/07/13 12:19:25 - mmengine - INFO - Epoch(train) [12][ 400/3139] lr: 1.2500e-05 eta: 0:14:46 time: 0.3250 data_time: 0.0053 memory: 733 loss: 1.1897 loss_cls: 0.2397 loss_bbox: 0.4704 loss_dfl: 0.1893 loss_ld: 0.2902
2023/07/13 12:19:42 - mmengine - INFO - Epoch(train) [12][ 450/3139] lr: 1.2500e-05 eta: 0:14:30 time: 0.3249 data_time: 0.0051 memory: 719 loss: 1.1459 loss_cls: 0.2391 loss_bbox: 0.4423 loss_dfl: 0.1863 loss_ld: 0.2782
2023/07/13 12:19:48 - mmengine - INFO - Exp name: ld_gfl_r18_r101_fpn_1x_vis_20230713_085933
2023/07/13 12:19:58 - mmengine - INFO - Epoch(train) [12][ 500/3139] lr: 1.2500e-05 eta: 0:14:14 time: 0.3247 data_time: 0.0052 memory: 717 loss: 1.1956 loss_cls: 0.2541 loss_bbox: 0.4512 loss_dfl: 0.1881 loss_ld: 0.3022
2023/07/13 12:20:14 - mmengine - INFO - Epoch(train) [12][ 550/3139] lr: 1.2500e-05 eta: 0:13:58 time: 0.3229 data_time: 0.0044 memory: 727 loss: 1.1815 loss_cls: 0.2313 loss_bbox: 0.5024 loss_dfl: 0.1901 loss_ld: 0.2577
2023/07/13 12:20:30 - mmengine - INFO - Epoch(train) [12][ 600/3139] lr: 1.2500e-05 eta: 0:13:42 time: 0.3253 data_time: 0.0048 memory: 743 loss: 1.1483 loss_cls: 0.2502 loss_bbox: 0.4687 loss_dfl: 0.1866 loss_ld: 0.2428
2023/07/13 12:20:46 - mmengine - INFO - Epoch(train) [12][ 650/3139] lr: 1.2500e-05 eta: 0:13:26 time: 0.3191 data_time: 0.0047 memory: 731 loss: 1.1650 loss_cls: 0.2273 loss_bbox: 0.4644 loss_dfl: 0.1890 loss_ld: 0.2843
2023/07/13 12:21:03 - mmengine - INFO - Epoch(train) [12][ 700/3139] lr: 1.2500e-05 eta: 0:13:09 time: 0.3256 data_time: 0.0038 memory: 734 loss: 1.2436 loss_cls: 0.2404 loss_bbox: 0.4757 loss_dfl: 0.1930 loss_ld: 0.3345
2023/07/13 12:21:19 - mmengine - INFO - Epoch(train) [12][ 750/3139] lr: 1.2500e-05 eta: 0:12:53 time: 0.3197 data_time: 0.0038 memory: 730 loss: 1.1643 loss_cls: 0.2274 loss_bbox: 0.4844 loss_dfl: 0.1869 loss_ld: 0.2656
2023/07/13 12:21:35 - mmengine - INFO - Epoch(train) [12][ 800/3139] lr: 1.2500e-05 eta: 0:12:37 time: 0.3232 data_time: 0.0039 memory: 721 loss: 1.1777 loss_cls: 0.2397 loss_bbox: 0.4815 loss_dfl: 0.1872 loss_ld: 0.2693
2023/07/13 12:21:51 - mmengine - INFO - Epoch(train) [12][ 850/3139] lr: 1.2500e-05 eta: 0:12:21 time: 0.3244 data_time: 0.0039 memory: 725 loss: 1.2224 loss_cls: 0.2437 loss_bbox: 0.4905 loss_dfl: 0.1902 loss_ld: 0.2979
2023/07/13 12:22:07 - mmengine - INFO - Epoch(train) [12][ 900/3139] lr: 1.2500e-05 eta: 0:12:05 time: 0.3237 data_time: 0.0044 memory: 737 loss: 1.1405 loss_cls: 0.2252 loss_bbox: 0.4562 loss_dfl: 0.1830 loss_ld: 0.2762
2023/07/13 12:22:23 - mmengine - INFO - Epoch(train) [12][ 950/3139] lr: 1.2500e-05 eta: 0:11:48 time: 0.3265 data_time: 0.0042 memory: 729 loss: 1.2238 loss_cls: 0.2508 loss_bbox: 0.4770 loss_dfl: 0.1906 loss_ld: 0.3054
2023/07/13 12:22:40 - mmengine - INFO - Epoch(train) [12][1000/3139] lr: 1.2500e-05 eta: 0:11:32 time: 0.3293 data_time: 0.0068 memory: 730 loss: 1.1691 loss_cls: 0.2491 loss_bbox: 0.4610 loss_dfl: 0.1877 loss_ld: 0.2713
2023/07/13 12:22:56 - mmengine - INFO - Epoch(train) [12][1050/3139] lr: 1.2500e-05 eta: 0:11:16 time: 0.3220 data_time: 0.0038 memory: 723 loss: 1.1927 loss_cls: 0.2453 loss_bbox: 0.4768 loss_dfl: 0.1924 loss_ld: 0.2782
2023/07/13 12:23:12 - mmengine - INFO - Epoch(train) [12][1100/3139] lr: 1.2500e-05 eta: 0:11:00 time: 0.3245 data_time: 0.0040 memory: 735 loss: 1.2186 loss_cls: 0.2332 loss_bbox: 0.5000 loss_dfl: 0.1936 loss_ld: 0.2917
2023/07/13 12:23:29 - mmengine - INFO - Epoch(train) [12][1150/3139] lr: 1.2500e-05 eta: 0:10:44 time: 0.3240 data_time: 0.0041 memory: 723 loss: 1.2151 loss_cls: 0.2457 loss_bbox: 0.4632 loss_dfl: 0.1887 loss_ld: 0.3175
2023/07/13 12:23:45 - mmengine - INFO - Epoch(train) [12][1200/3139] lr: 1.2500e-05 eta: 0:10:27 time: 0.3248 data_time: 0.0047 memory: 720 loss: 1.1923 loss_cls: 0.2637 loss_bbox: 0.4702 loss_dfl: 0.1887 loss_ld: 0.2697
2023/07/13 12:24:01 - mmengine - INFO - Epoch(train) [12][1250/3139] lr: 1.2500e-05 eta: 0:10:11 time: 0.3254 data_time: 0.0051 memory: 731 loss: 1.2070 loss_cls: 0.2325 loss_bbox: 0.4787 loss_dfl: 0.1861 loss_ld: 0.3097
2023/07/13 12:24:17 - mmengine - INFO - Epoch(train) [12][1300/3139] lr: 1.2500e-05 eta: 0:09:55 time: 0.3212 data_time: 0.0046 memory: 717 loss: 1.1637 loss_cls: 0.2503 loss_bbox: 0.4659 loss_dfl: 0.1869 loss_ld: 0.2607
2023/07/13 12:24:34 - mmengine - INFO - Epoch(train) [12][1350/3139] lr: 1.2500e-05 eta: 0:09:39 time: 0.3296 data_time: 0.0055 memory: 751 loss: 1.1443 loss_cls: 0.2288 loss_bbox: 0.4377 loss_dfl: 0.1848 loss_ld: 0.2929
2023/07/13 12:24:50 - mmengine - INFO - Epoch(train) [12][1400/3139] lr: 1.2500e-05 eta: 0:09:23 time: 0.3227 data_time: 0.0044 memory: 730 loss: 1.2217 loss_cls: 0.2403 loss_bbox: 0.4977 loss_dfl: 0.1911 loss_ld: 0.2927
2023/07/13 12:25:06 - mmengine - INFO - Epoch(train) [12][1450/3139] lr: 1.2500e-05 eta: 0:09:06 time: 0.3231 data_time: 0.0049 memory: 716 loss: 1.2251 loss_cls: 0.2563 loss_bbox: 0.4879 loss_dfl: 0.1967 loss_ld: 0.2842
2023/07/13 12:25:13 - mmengine - INFO - Exp name: ld_gfl_r18_r101_fpn_1x_vis_20230713_085933
2023/07/13 12:25:22 - mmengine - INFO - Epoch(train) [12][1500/3139] lr: 1.2500e-05 eta: 0:08:50 time: 0.3213 data_time: 0.0043 memory: 722 loss: 1.1678 loss_cls: 0.2552 loss_bbox: 0.4863 loss_dfl: 0.1895 loss_ld: 0.2368
2023/07/13 12:25:38 - mmengine - INFO - Epoch(train) [12][1550/3139] lr: 1.2500e-05 eta: 0:08:34 time: 0.3221 data_time: 0.0041 memory: 735 loss: 1.1817 loss_cls: 0.2345 loss_bbox: 0.4659 loss_dfl: 0.1886 loss_ld: 0.2927
2023/07/13 12:25:54 - mmengine - INFO - Epoch(train) [12][1600/3139] lr: 1.2500e-05 eta: 0:08:18 time: 0.3250 data_time: 0.0048 memory: 718 loss: 1.1470 loss_cls: 0.2468 loss_bbox: 0.4426 loss_dfl: 0.1905 loss_ld: 0.2670
2023/07/13 12:26:11 - mmengine - INFO - Epoch(train) [12][1650/3139] lr: 1.2500e-05 eta: 0:08:02 time: 0.3258 data_time: 0.0051 memory: 761 loss: 1.1481 loss_cls: 0.2454 loss_bbox: 0.4509 loss_dfl: 0.1848 loss_ld: 0.2670
2023/07/13 12:26:27 - mmengine - INFO - Epoch(train) [12][1700/3139] lr: 1.2500e-05 eta: 0:07:46 time: 0.3254 data_time: 0.0052 memory: 721 loss: 1.1865 loss_cls: 0.2358 loss_bbox: 0.4594 loss_dfl: 0.1842 loss_ld: 0.3070
2023/07/13 12:26:43 - mmengine - INFO - Epoch(train) [12][1750/3139] lr: 1.2500e-05 eta: 0:07:29 time: 0.3237 data_time: 0.0038 memory: 717 loss: 1.2031 loss_cls: 0.2439 loss_bbox: 0.4490 loss_dfl: 0.1886 loss_ld: 0.3216
2023/07/13 12:26:59 - mmengine - INFO - Epoch(train) [12][1800/3139] lr: 1.2500e-05 eta: 0:07:13 time: 0.3234 data_time: 0.0042 memory: 719 loss: 1.1345 loss_cls: 0.2508 loss_bbox: 0.4638 loss_dfl: 0.1836 loss_ld: 0.2362
2023/07/13 12:27:15 - mmengine - INFO - Epoch(train) [12][1850/3139] lr: 1.2500e-05 eta: 0:06:57 time: 0.3224 data_time: 0.0047 memory: 728 loss: 1.1307 loss_cls: 0.2440 loss_bbox: 0.4368 loss_dfl: 0.1846 loss_ld: 0.2653
2023/07/13 12:27:32 - mmengine - INFO - Epoch(train) [12][1900/3139] lr: 1.2500e-05 eta: 0:06:41 time: 0.3250 data_time: 0.0046 memory: 728 loss: 1.2123 loss_cls: 0.2338 loss_bbox: 0.4853 loss_dfl: 0.1940 loss_ld: 0.2991
2023/07/13 12:27:48 - mmengine - INFO - Epoch(train) [12][1950/3139] lr: 1.2500e-05 eta: 0:06:25 time: 0.3304 data_time: 0.0055 memory: 731 loss: 1.2245 loss_cls: 0.2415 loss_bbox: 0.5063 loss_dfl: 0.1933 loss_ld: 0.2833
2023/07/13 12:28:04 - mmengine - INFO - Epoch(train) [12][2000/3139] lr: 1.2500e-05 eta: 0:06:08 time: 0.3186 data_time: 0.0044 memory: 726 loss: 1.1360 loss_cls: 0.2305 loss_bbox: 0.4318 loss_dfl: 0.1832 loss_ld: 0.2905
2023/07/13 12:28:20 - mmengine - INFO - Epoch(train) [12][2050/3139] lr: 1.2500e-05 eta: 0:05:52 time: 0.3179 data_time: 0.0042 memory: 719 loss: 1.2056 loss_cls: 0.2445 loss_bbox: 0.4739 loss_dfl: 0.1917 loss_ld: 0.2956
2023/07/13 12:28:36 - mmengine - INFO - Epoch(train) [12][2100/3139] lr: 1.2500e-05 eta: 0:05:36 time: 0.3232 data_time: 0.0045 memory: 738 loss: 1.1473 loss_cls: 0.2394 loss_bbox: 0.4621 loss_dfl: 0.1838 loss_ld: 0.2620
2023/07/13 12:28:52 - mmengine - INFO - Epoch(train) [12][2150/3139] lr: 1.2500e-05 eta: 0:05:20 time: 0.3232 data_time: 0.0039 memory: 728 loss: 1.2740 loss_cls: 0.2653 loss_bbox: 0.5257 loss_dfl: 0.2003 loss_ld: 0.2827
2023/07/13 12:29:09 - mmengine - INFO - Epoch(train) [12][2200/3139] lr: 1.2500e-05 eta: 0:05:04 time: 0.3264 data_time: 0.0049 memory: 721 loss: 1.2042 loss_cls: 0.2441 loss_bbox: 0.4724 loss_dfl: 0.1887 loss_ld: 0.2989
2023/07/13 12:29:25 - mmengine - INFO - Epoch(train) [12][2250/3139] lr: 1.2500e-05 eta: 0:04:47 time: 0.3279 data_time: 0.0055 memory: 721 loss: 1.1704 loss_cls: 0.2420 loss_bbox: 0.4602 loss_dfl: 0.1886 loss_ld: 0.2797
2023/07/13 12:29:41 - mmengine - INFO - Epoch(train) [12][2300/3139] lr: 1.2500e-05 eta: 0:04:31 time: 0.3221 data_time: 0.0036 memory: 721 loss: 1.1490 loss_cls: 0.2497 loss_bbox: 0.4474 loss_dfl: 0.1925 loss_ld: 0.2595
2023/07/13 12:29:58 - mmengine - INFO - Epoch(train) [12][2350/3139] lr: 1.2500e-05 eta: 0:04:15 time: 0.3262 data_time: 0.0044 memory: 739 loss: 1.1888 loss_cls: 0.2471 loss_bbox: 0.4549 loss_dfl: 0.1910 loss_ld: 0.2958
2023/07/13 12:30:14 - mmengine - INFO - Epoch(train) [12][2400/3139] lr: 1.2500e-05 eta: 0:03:59 time: 0.3265 data_time: 0.0054 memory: 720 loss: 1.1587 loss_cls: 0.2422 loss_bbox: 0.4455 loss_dfl: 0.1905 loss_ld: 0.2804
2023/07/13 12:30:30 - mmengine - INFO - Epoch(train) [12][2450/3139] lr: 1.2500e-05 eta: 0:03:43 time: 0.3243 data_time: 0.0047 memory: 722 loss: 1.1986 loss_cls: 0.2504 loss_bbox: 0.4841 loss_dfl: 0.1906 loss_ld: 0.2736
2023/07/13 12:30:37 - mmengine - INFO - Exp name: ld_gfl_r18_r101_fpn_1x_vis_20230713_085933
2023/07/13 12:30:46 - mmengine - INFO - Epoch(train) [12][2500/3139] lr: 1.2500e-05 eta: 0:03:26 time: 0.3265 data_time: 0.0047 memory: 737 loss: 1.1908 loss_cls: 0.2368 loss_bbox: 0.4881 loss_dfl: 0.1916 loss_ld: 0.2744
2023/07/13 12:31:03 - mmengine - INFO - Epoch(train) [12][2550/3139] lr: 1.2500e-05 eta: 0:03:10 time: 0.3277 data_time: 0.0058 memory: 725 loss: 1.1967 loss_cls: 0.2369 loss_bbox: 0.4267 loss_dfl: 0.1824 loss_ld: 0.3507
2023/07/13 12:31:19 - mmengine - INFO - Epoch(train) [12][2600/3139] lr: 1.2500e-05 eta: 0:02:54 time: 0.3254 data_time: 0.0043 memory: 722 loss: 1.2200 loss_cls: 0.2550 loss_bbox: 0.4887 loss_dfl: 0.1984 loss_ld: 0.2779
2023/07/13 12:31:35 - mmengine - INFO - Epoch(train) [12][2650/3139] lr: 1.2500e-05 eta: 0:02:38 time: 0.3233 data_time: 0.0040 memory: 718 loss: 1.1096 loss_cls: 0.2333 loss_bbox: 0.4453 loss_dfl: 0.1860 loss_ld: 0.2451
2023/07/13 12:31:52 - mmengine - INFO - Epoch(train) [12][2700/3139] lr: 1.2500e-05 eta: 0:02:22 time: 0.3262 data_time: 0.0050 memory: 726 loss: 1.1450 loss_cls: 0.2383 loss_bbox: 0.4454 loss_dfl: 0.1886 loss_ld: 0.2728
2023/07/13 12:32:08 - mmengine - INFO - Epoch(train) [12][2750/3139] lr: 1.2500e-05 eta: 0:02:05 time: 0.3236 data_time: 0.0041 memory: 723 loss: 1.1926 loss_cls: 0.2387 loss_bbox: 0.4584 loss_dfl: 0.1871 loss_ld: 0.3084
2023/07/13 12:32:24 - mmengine - INFO - Epoch(train) [12][2800/3139] lr: 1.2500e-05 eta: 0:01:49 time: 0.3224 data_time: 0.0036 memory: 728 loss: 1.1488 loss_cls: 0.2320 loss_bbox: 0.4602 loss_dfl: 0.1892 loss_ld: 0.2674
2023/07/13 12:32:40 - mmengine - INFO - Epoch(train) [12][2850/3139] lr: 1.2500e-05 eta: 0:01:33 time: 0.3180 data_time: 0.0040 memory: 730 loss: 1.1971 loss_cls: 0.2411 loss_bbox: 0.4715 loss_dfl: 0.1903 loss_ld: 0.2942
2023/07/13 12:32:56 - mmengine - INFO - Epoch(train) [12][2900/3139] lr: 1.2500e-05 eta: 0:01:17 time: 0.3225 data_time: 0.0041 memory: 747 loss: 1.1561 loss_cls: 0.2527 loss_bbox: 0.4643 loss_dfl: 0.1871 loss_ld: 0.2521
2023/07/13 12:33:12 - mmengine - INFO - Epoch(train) [12][2950/3139] lr: 1.2500e-05 eta: 0:01:01 time: 0.3262 data_time: 0.0055 memory: 724 loss: 1.1136 loss_cls: 0.2540 loss_bbox: 0.4172 loss_dfl: 0.1815 loss_ld: 0.2609
2023/07/13 12:33:29 - mmengine - INFO - Epoch(train) [12][3000/3139] lr: 1.2500e-05 eta: 0:00:45 time: 0.3237 data_time: 0.0039 memory: 728 loss: 1.2584 loss_cls: 0.2383 loss_bbox: 0.5114 loss_dfl: 0.2004 loss_ld: 0.3083
2023/07/13 12:33:45 - mmengine - INFO - Epoch(train) [12][3050/3139] lr: 1.2500e-05 eta: 0:00:28 time: 0.3262 data_time: 0.0049 memory: 724 loss: 1.1736 loss_cls: 0.2513 loss_bbox: 0.4469 loss_dfl: 0.1843 loss_ld: 0.2912
2023/07/13 12:34:01 - mmengine - INFO - Epoch(train) [12][3100/3139] lr: 1.2500e-05 eta: 0:00:12 time: 0.3242 data_time: 0.0047 memory: 724 loss: 1.1423 loss_cls: 0.2349 loss_bbox: 0.4564 loss_dfl: 0.1831 loss_ld: 0.2679
2023/07/13 12:34:13 - mmengine - INFO - Exp name: ld_gfl_r18_r101_fpn_1x_vis_20230713_085933
2023/07/13 12:34:13 - mmengine - INFO - Saving checkpoint at 12 epochs
2023/07/13 12:34:21 - mmengine - INFO - Epoch(val) [12][ 50/548] eta: 0:00:38 time: 0.0772 data_time: 0.0022 memory: 717
2023/07/13 12:34:24 - mmengine - INFO - Epoch(val) [12][100/548] eta: 0:00:33 time: 0.0742 data_time: 0.0013 memory: 497
2023/07/13 12:34:28 - mmengine - INFO - Epoch(val) [12][150/548] eta: 0:00:29 time: 0.0739 data_time: 0.0013 memory: 497
2023/07/13 12:34:32 - mmengine - INFO - Epoch(val) [12][200/548] eta: 0:00:26 time: 0.0738 data_time: 0.0013 memory: 497
2023/07/13 12:34:35 - mmengine - INFO - Epoch(val) [12][250/548] eta: 0:00:22 time: 0.0735 data_time: 0.0013 memory: 497
2023/07/13 12:34:39 - mmengine - INFO - Epoch(val) [12][300/548] eta: 0:00:18 time: 0.0738 data_time: 0.0013 memory: 497
2023/07/13 12:34:43 - mmengine - INFO - Epoch(val) [12][350/548] eta: 0:00:14 time: 0.0735 data_time: 0.0013 memory: 497
2023/07/13 12:34:47 - mmengine - INFO - Epoch(val) [12][400/548] eta: 0:00:10 time: 0.0733 data_time: 0.0013 memory: 497
2023/07/13 12:34:50 - mmengine - INFO - Epoch(val) [12][450/548] eta: 0:00:07 time: 0.0739 data_time: 0.0013 memory: 497
2023/07/13 12:34:54 - mmengine - INFO - Epoch(val) [12][500/548] eta: 0:00:03 time: 0.0742 data_time: 0.0013 memory: 497
2023/07/13 12:34:58 - mmengine - INFO - Evaluating bbox...
2023/07/13 12:35:13 - mmengine - INFO - bbox_mAP_copypaste: 0.108 0.180 0.116 0.030 0.153 0.302
2023/07/13 12:35:13 - mmengine - INFO - Epoch(val) [12][548/548] coco/bbox_mAP: 0.1080 coco/bbox_mAP_50: 0.1800 coco/bbox_mAP_75: 0.1160 coco/bbox_mAP_s: 0.0300 coco/bbox_mAP_m: 0.1530 coco/bbox_mAP_l: 0.3020 data_time: 0.0014 time: 0.0741
from ld.
HikariTJU
commented on August 28, 2024
I didn't find problems in this log, can you try setting loss_ld=0 in your config and retrain ld_r18-gflv1-r101_fpn_1x, theoritically it should have same accuracy as gfl_r18_fpn_1x
from ld.
melika-sce
commented on August 28, 2024
Thanks for the response, I will give it a try and tell the result
should I set loss_weight=0.0 and let the temperature be 10?
from ld.
HikariTJU
commented on August 28, 2024
Yes, loss_weight=0
from ld.
Related Issues (20)
- I reimplement your model (Teacher: R101, Student: R50, Detection head: Foveabox) and got RuntimeError: shape '[-1, 68]' is invalid for input of size 115200 HOT 5
- Welcome update to OpenMMLab 2.0 HOT 2
- 请问下代码里的教师模型是跟着学生模型一起训练的吗 HOT 2
- If I set output_feature = False , Code don't work HOT 1
- 参考install.md 里面配置的环境无法找到cuda HOT 13
- 简易版本复现问题 HOT 5
- 将DKD与LD结合的问题 HOT 8
- 学生网络与教师网络性能差距较大
- mmpycocotools HOT 1
- 请问下是否可以迁移到centernet呢 HOT 2
- 知识蒸馏 HOT 10
- 请问是使用 ldv2/ld_r50_gflv2_r101_fpn_1x.py 这个文件来复现 Temperature τ in LD 这个实验吗? HOT 1
- kd_loss implementation issue HOT 9
- 关于数据集minicoco HOT 4
- About Adaptive Layer HOT 2
- Can we pass different set of image to teacher and student. HOT 6
- samples_per_gpu HOT 1
- loadcheckpoint出现问题 HOT 2
- Knowledge distillation related
Recommend Projects
-
React
A declarative, efficient, and flexible JavaScript library for building user interfaces.
-
Vue.js
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
-
Typescript
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
-
TensorFlow
An Open Source Machine Learning Framework for Everyone
-
Django
The Web framework for perfectionists with deadlines.
-
Laravel
A PHP framework for web artisans
-
D3
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
-
Recommend Topics
-
javascript
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
-
web
Some thing interesting about web. New door for the world.
-
server
A server is a program made to process requests and deliver data to clients.
-
Machine learning
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
-
Visualization
Some thing interesting about visualization, use data art
-
Game
Some thing interesting about game, make everyone happy.
Recommend Org
-
Facebook
We are working to build community through open source technology. NB: members must have two-factor auth.
-
Microsoft
Open source projects and samples from Microsoft.
-
Google
Google ❤️ Open Source for everyone.
-
Alibaba
Alibaba Open Source for everyone
-
D3
Data-Driven Documents codes.
-
Tencent
China tencent open source team.
from ld.