Other Parts Discussed in Thread: 4430
Tool/software:
i followed this guide https://github.com/TexasInstruments/edgeai-tensorlab/tree/main/edgeai-modelmaker
and used branch r9.1
The model can be trained and compiled normally and no errors are found. However, when I deploy it to a target board, it sometimes does not work. That is, if I use the same datasets, compile it multiple times, and deploy it to the target board, the model compiled at a certain time will not work and will not recognize the object detection at all. This happens when I compile on two different Ubuntu 22.04 machines. , can you tell me the possible reasons? thanks
the trained log：
2024-11-25 16:21:51,120 - mmdet - INFO - Environment info:
------------------------------------------------------------
sys.platform: linux
Python: 3.10.15 (main, Nov 8 2024, 11:46:29) [GCC 11.4.0]
CUDA available: False
GCC: gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
PyTorch: 2.0.1+cpu
PyTorch compiling details: PyTorch built with:
- GCC 9.3
- C++ Version: 201703
- Intel(R) oneAPI Math Kernel Library Version 2022.2-Product Build 20220804 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
- Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, 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_NOCUPTI -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=0, USE_CUDNN=OFF, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=OFF, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF,
TorchVision: 0.15.2+cpu
OpenCV: 4.10.0
MMCV: 1.4.8
MMCV Compiler: GCC 11.4
MMCV CUDA Compiler: not available
MMDetection: 2.22.0+cb8eba4
------------------------------------------------------------
2024-11-25 16:21:51,794 - mmdet - INFO - Distributed training: False
2024-11-25 16:21:52,467 - mmdet - INFO - Config:
dataset_type = 'CocoDataset'
data_root = '/home/mchi/github/edgeai-tensorlab/edgeai-modelmaker/data/projects/1021_3280x2464/dataset'
data = dict(
samples_per_gpu=8,
workers_per_gpu=2,
train=dict(
type='MultiImageMixDataset',
dataset=dict(
type='ModelMakerDataset',
ann_file=
'/home/mchi/github/edgeai-tensorlab/edgeai-modelmaker/data/projects/1021_3280x2464/dataset/annotations/instances_train.json',
img_prefix=
'/home/mchi/github/edgeai-tensorlab/edgeai-modelmaker/data/projects/1021_3280x2464/dataset/train',
pipeline=[
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True)
],
filter_empty_gt=False,
classes=['burr', 'lack', 'split', 'tree', 'vein']),
pipeline=[
dict(type='Mosaic', img_scale=(640, 640), pad_val=114.0),
dict(
type='RandomAffine',
scaling_ratio_range=(0.1, 2),
border=(-320, -320)),
dict(
type='MixUp',
img_scale=(640, 640),
ratio_range=(0.8, 1.6),
pad_val=114.0),
dict(type='YOLOXHSVRandomAug'),
dict(type='RandomFlip', flip_ratio=0.5),
dict(type='Resize', img_scale=(640, 640), keep_ratio=True),
dict(
type='Pad',
pad_to_square=True,
pad_val=dict(img=(114.0, 114.0, 114.0))),
dict(
type='FilterAnnotations',
min_gt_bbox_wh=(1, 1),
keep_empty=False),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
]),
val=dict(
type='ModelMakerDataset',
ann_file=
'/home/mchi/github/edgeai-tensorlab/edgeai-modelmaker/data/projects/1021_3280x2464/dataset/annotations/instances_val.json',
img_prefix=
'/home/mchi/github/edgeai-tensorlab/edgeai-modelmaker/data/projects/1021_3280x2464/dataset/val',
pipeline=[
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(640, 640),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(
type='Pad',
pad_to_square=True,
pad_val=dict(img=(114.0, 114.0, 114.0))),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img'])
])
],
classes=['burr', 'lack', 'split', 'tree', 'vein']),
test=dict(
type='CocoDataset',
ann_file='data/coco/annotations/instances_val2017.json',
img_prefix='data/coco/val2017/',
pipeline=[
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(640, 640),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(
type='Pad',
pad_to_square=True,
pad_val=dict(img=(114.0, 114.0, 114.0))),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img'])
])
]),
persistent_workers=True)
evaluation = dict(interval=1, metric='bbox')
cudnn_benchmark = True
resize_with_scale_factor = True
max_epochs = 300
num_last_epochs = 15
interval = 10
dist_params = dict(backend='nccl')
log_level = 'INFO'
load_from = './data/downloads/pretrained/yolox_s_lite/yolox_s_lite_640x640_20220221_checkpoint.pth'
resume_from = None
workflow = [('train', 1)]
print_model_complexity = True
optimizer = dict(
type='SGD',
lr=0.001,
momentum=0.9,
weight_decay=0.0005,
nesterov=True,
paramwise_cfg=dict(norm_decay_mult=0.0, bias_decay_mult=0.0))
optimizer_config = dict(grad_clip=None)
lr_config = dict(
policy='YOLOX',
warmup='exp',
by_epoch=False,
warmup_by_epoch=True,
warmup_ratio=1,
warmup_iters=1,
num_last_epochs=5,
min_lr_ratio=0.05)
runner = dict(type='EpochBasedRunner', max_epochs=60)
custom_hooks = [
dict(type='YOLOXModeSwitchHook', num_last_epochs=15, priority=48),
dict(type='SyncNormHook', num_last_epochs=15, interval=10, priority=48),
dict(
type='ExpMomentumEMAHook',
resume_from=None,
momentum=0.0001,
priority=49)
]
checkpoint_config = dict(interval=10)
log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')])
img_scale = (640, 640)
input_size = (640, 640)
samples_per_gpu = 16
num_classes_dict = dict(
CocoDataset=80, VOCDataset=20, CityscapesDataset=8, WIDERFaceDataset=1)
dataset_root_dict = dict(
CocoDataset='data/coco/',
VOCDataset='data/VOCdevkit/',
CityscapesDataset='data/cityscapes/',
WIDERFaceDataset='data/WIDERFace/')
num_classes = 80
img_norm_cfg = dict(mean=[0.0, 0.0, 0.0], std=[1.0, 1.0, 1.0], to_rgb=False)
convert_to_lite_model = dict(group_size_dw=None)
quantize = False
initial_learning_rate = 0.01
model = dict(
type='YOLOX',
input_size=(640, 640),
random_size_range=(15, 25),
random_size_interval=10,
backbone=dict(type='CSPDarknet', deepen_factor=0.33, widen_factor=0.5),
neck=dict(
type='YOLOXPAFPN',
in_channels=[128, 256, 512],
out_channels=128,
num_csp_blocks=1),
bbox_head=dict(
type='YOLOXHead', num_classes=5, in_channels=128, feat_channels=128),
train_cfg=dict(assigner=dict(type='SimOTAAssigner', center_radius=2.5)),
test_cfg=dict(score_thr=0.01, nms=dict(type='nms', iou_threshold=0.65)))
train_pipeline = [
dict(type='Mosaic', img_scale=(640, 640), pad_val=114.0),
dict(
type='RandomAffine', scaling_ratio_range=(0.1, 2),
border=(-320, -320)),
dict(
type='MixUp',
img_scale=(640, 640),
ratio_range=(0.8, 1.6),
pad_val=114.0),
dict(type='YOLOXHSVRandomAug'),
dict(type='RandomFlip', flip_ratio=0.5),
dict(type='Resize', img_scale=(640, 640), keep_ratio=True),
dict(
type='Pad',
pad_to_square=True,
pad_val=dict(img=(114.0, 114.0, 114.0))),
dict(type='FilterAnnotations', min_gt_bbox_wh=(1, 1), keep_empty=False),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(640, 640),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(
type='Pad',
pad_to_square=True,
pad_val=dict(img=(114.0, 114.0, 114.0))),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img'])
])
]
work_dir = '/home/mchi/github/edgeai-tensorlab/edgeai-modelmaker/data/projects/1021_3280x2464/run/20241125-162141/yolox_s_lite/training'
total_epochs = 60
export_model = True
auto_resume = False
gpu_ids = [0]
2024-11-25 16:21:52,467 - mmdet - INFO - Set random seed to 51281273, deterministic: False
2024-11-25 16:21:52,656 - mmdet - INFO - initialize CSPDarknet with init_cfg {'type': 'Kaiming', 'layer': 'Conv2d', 'a': 2.23606797749979, 'distribution': 'uniform', 'mode': 'fan_in', 'nonlinearity': 'leaky_relu'}
2024-11-25 16:21:52,686 - mmdet - INFO - initialize YOLOXPAFPN with init_cfg {'type': 'Kaiming', 'layer': 'Conv2d', 'a': 2.23606797749979, 'distribution': 'uniform', 'mode': 'fan_in', 'nonlinearity': 'leaky_relu'}
2024-11-25 16:21:52,716 - mmdet - INFO - initialize YOLOXHead with init_cfg {'type': 'Kaiming', 'layer': 'Conv2d', 'a': 2.23606797749979, 'distribution': 'uniform', 'mode': 'fan_in', 'nonlinearity': 'leaky_relu'}
Name of parameter - Initialization information
backbone.stem.conv.conv.weight - torch.Size([32, 12, 3, 3]):
KaimingInit: a=2.23606797749979, mode=fan_in, nonlinearity=leaky_relu, distribution =uniform, bias=0
backbone.stem.conv.bn.weight - torch.Size([32]):
The value is the same before and after calling `init_weights` of YOLOX
backbone.stem.conv.bn.bias - torch.Size([32]):
The value is the same before and after calling `init_weights` of YOLOX
backbone.stage1.0.conv.weight - torch.Size([64, 32, 3, 3]):
KaimingInit: a=2.23606797749979, mode=fan_in, nonlinearity=leaky_relu, distribution =uniform, bias=0
backbone.stage1.0.bn.weight - torch.Size([64]):
The value is the same before and after calling `init_weights` of YOLOX
backbone.stage1.0.bn.bias - torch.Size([64]):
The value is the same before and after calling `init_weights` of YOLOX
backbone.stage1.1.main_conv.conv.weight - torch.Size([32, 64, 1, 1]):
KaimingInit: a=2.23606797749979, mode=fan_in, nonlinearity=leaky_relu, distribution =uniform, bias=0
backbone.stage1.1.main_conv.bn.weight - torch.Size([32]):
The value is the same before and after calling `init_weights` of YOLOX
backbone.stage1.1.main_conv.bn.bias - torch.Size([32]):
The value is the same before and after calling `init_weights` of YOLOX
backbone.stage1.1.short_conv.conv.weight - torch.Size([32, 64, 1, 1]):
KaimingInit: a=2.23606797749979, mode=fan_in, nonlinearity=leaky_relu, distribution =uniform, bias=0
backbone.stage1.1.short_conv.bn.weight - torch.Size([32]):
The value is the same before and after calling `init_weights` of YOLOX
backbone.stage1.1.short_conv.bn.bias - torch.Size([32]):
The value is the same before and after calling `init_weights` of YOLOX
backbone.stage1.1.final_conv.conv.weight - torch.Size([64, 64, 1, 1]):
KaimingInit: a=2.23606797749979, mode=fan_in, nonlinearity=leaky_relu, distribution =uniform, bias=0
backbone.stage1.1.final_conv.bn.weight - torch.Size([64]):
The value is the same before and after calling `init_weights` of YOLOX
backbone.stage1.1.final_conv.bn.bias - torch.Size([64]):
The value is the same before and after calling `init_weights` of YOLOX
backbone.stage1.1.blocks.0.conv1.conv.weight - torch.Size([32, 32, 1, 1]):
KaimingInit: a=2.23606797749979, mode=fan_in, nonlinearity=leaky_relu, distribution =uniform, bias=0
backbone.stage1.1.blocks.0.conv1.bn.weight - torch.Size([32]):
The value is the same before and after calling `init_weights` of YOLOX
backbone.stage1.1.blocks.0.conv1.bn.bias - torch.Size([32]):
The value is the same before and after calling `init_weights` of YOLOX
backbone.stage1.1.blocks.0.conv2.conv.weight - torch.Size([32, 32, 3, 3]):
KaimingInit: a=2.23606797749979, mode=fan_in, nonlinearity=leaky_relu, distribution =uniform, bias=0
backbone.stage1.1.blocks.0.conv2.bn.weight - torch.Size([32]):
The value is the same before and after calling `init_weights` of YOLOX
backbone.stage1.1.blocks.0.conv2.bn.bias - torch.Size([32]):
The value is the same before and after calling `init_weights` of YOLOX
backbone.stage2.0.conv.weight - torch.Size([128, 64, 3, 3]):
KaimingInit: a=2.23606797749979, mode=fan_in, nonlinearity=leaky_relu, distribution =uniform, bias=0
backbone.stage2.0.bn.weight - torch.Size([128]):
The value is the same before and after calling `init_weights` of YOLOX
backbone.stage2.0.bn.bias - torch.Size([128]):
The value is the same before and after calling `init_weights` of YOLOX
backbone.stage2.1.main_conv.conv.weight - torch.Size([64, 128, 1, 1]):
KaimingInit: a=2.23606797749979, mode=fan_in, nonlinearity=leaky_relu, distribution =uniform, bias=0
backbone.stage2.1.main_conv.bn.weight - torch.Size([64]):
The value is the same before and after calling `init_weights` of YOLOX
backbone.stage2.1.main_conv.bn.bias - torch.Size([64]):
The value is the same before and after calling `init_weights` of YOLOX
backbone.stage2.1.short_conv.conv.weight - torch.Size([64, 128, 1, 1]):
KaimingInit: a=2.23606797749979, mode=fan_in, nonlinearity=leaky_relu, distribution =uniform, bias=0
backbone.stage2.1.short_conv.bn.weight - torch.Size([64]):
The value is the same before and after calling `init_weights` of YOLOX
backbone.stage2.1.short_conv.bn.bias - torch.Size([64]):
The value is the same before and after calling `init_weights` of YOLOX
backbone.stage2.1.final_conv.conv.weight - torch.Size([128, 128, 1, 1]):
KaimingInit: a=2.23606797749979, mode=fan_in, nonlinearity=leaky_relu, distribution =uniform, bias=0
backbone.stage2.1.final_conv.bn.weight - torch.Size([128]):
The value is the same before and after calling `init_weights` of YOLOX
backbone.stage2.1.final_conv.bn.bias - torch.Size([128]):
The value is the same before and after calling `init_weights` of YOLOX
backbone.stage2.1.blocks.0.conv1.conv.weight - torch.Size([64, 64, 1, 1]):
KaimingInit: a=2.23606797749979, mode=fan_in, nonlinearity=leaky_relu, distribution =uniform, bias=0
backbone.stage2.1.blocks.0.conv1.bn.weight - torch.Size([64]):
The value is the same before and after calling `init_weights` of YOLOX
backbone.stage2.1.blocks.0.conv1.bn.bias - torch.Size([64]):
The value is the same before and after calling `init_weights` of YOLOX
backbone.stage2.1.blocks.0.conv2.conv.weight - torch.Size([64, 64, 3, 3]):
KaimingInit: a=2.23606797749979, mode=fan_in, nonlinearity=leaky_relu, distribution =uniform, bias=0
backbone.stage2.1.blocks.0.conv2.bn.weight - torch.Size([64]):
The value is the same before and after calling `init_weights` of YOLOX
backbone.stage2.1.blocks.0.conv2.bn.bias - torch.Size([64]):
The value is the same before and after calling `init_weights` of YOLOX
backbone.stage2.1.blocks.1.conv1.conv.weight - torch.Size([64, 64, 1, 1]):
KaimingInit: a=2.23606797749979, mode=fan_in, nonlinearity=leaky_relu, distribution =uniform, bias=0
backbone.stage2.1.blocks.1.conv1.bn.weight - torch.Size([64]):
The value is the same before and after calling `init_weights` of YOLOX
backbone.stage2.1.blocks.1.conv1.bn.bias - torch.Size([64]):
The value is the same before and after calling `init_weights` of YOLOX
backbone.stage2.1.blocks.1.conv2.conv.weight - torch.Size([64, 64, 3, 3]):
KaimingInit: a=2.23606797749979, mode=fan_in, nonlinearity=leaky_relu, distribution =uniform, bias=0
backbone.stage2.1.blocks.1.conv2.bn.weight - torch.Size([64]):
The value is the same before and after calling `init_weights` of YOLOX
backbone.stage2.1.blocks.1.conv2.bn.bias - torch.Size([64]):
The value is the same before and after calling `init_weights` of YOLOX
backbone.stage2.1.blocks.2.conv1.conv.weight - torch.Size([64, 64, 1, 1]):
KaimingInit: a=2.23606797749979, mode=fan_in, nonlinearity=leaky_relu, distribution =uniform, bias=0
backbone.stage2.1.blocks.2.conv1.bn.weight - torch.Size([64]):
The value is the same before and after calling `init_weights` of YOLOX
backbone.stage2.1.blocks.2.conv1.bn.bias - torch.Size([64]):
The value is the same before and after calling `init_weights` of YOLOX
backbone.stage2.1.blocks.2.conv2.conv.weight - torch.Size([64, 64, 3, 3]):
KaimingInit: a=2.23606797749979, mode=fan_in, nonlinearity=leaky_relu, distribution =uniform, bias=0
backbone.stage2.1.blocks.2.conv2.bn.weight - torch.Size([64]):
The value is the same before and after calling `init_weights` of YOLOX
backbone.stage2.1.blocks.2.conv2.bn.bias - torch.Size([64]):
The value is the same before and after calling `init_weights` of YOLOX
backbone.stage3.0.conv.weight - torch.Size([256, 128, 3, 3]):
KaimingInit: a=2.23606797749979, mode=fan_in, nonlinearity=leaky_relu, distribution =uniform, bias=0
backbone.stage3.0.bn.weight - torch.Size([256]):
The value is the same before and after calling `init_weights` of YOLOX
backbone.stage3.0.bn.bias - torch.Size([256]):
The value is the same before and after calling `init_weights` of YOLOX
backbone.stage3.1.main_conv.conv.weight - torch.Size([128, 256, 1, 1]):
KaimingInit: a=2.23606797749979, mode=fan_in, nonlinearity=leaky_relu, distribution =uniform, bias=0
backbone.stage3.1.main_conv.bn.weight - torch.Size([128]):
The value is the same before and after calling `init_weights` of YOLOX
backbone.stage3.1.main_conv.bn.bias - torch.Size([128]):
The value is the same before and after calling `init_weights` of YOLOX
backbone.stage3.1.short_conv.conv.weight - torch.Size([128, 256, 1, 1]):
KaimingInit: a=2.23606797749979, mode=fan_in, nonlinearity=leaky_relu, distribution =uniform, bias=0
backbone.stage3.1.short_conv.bn.weight - torch.Size([128]):
The value is the same before and after calling `init_weights` of YOLOX
backbone.stage3.1.short_conv.bn.bias - torch.Size([128]):
The value is the same before and after calling `init_weights` of YOLOX
backbone.stage3.1.final_conv.conv.weight - torch.Size([256, 256, 1, 1]):
KaimingInit: a=2.23606797749979, mode=fan_in, nonlinearity=leaky_relu, distribution =uniform, bias=0
backbone.stage3.1.final_conv.bn.weight - torch.Size([256]):
The value is the same before and after calling `init_weights` of YOLOX
backbone.stage3.1.final_conv.bn.bias - torch.Size([256]):
The value is the same before and after calling `init_weights` of YOLOX
backbone.stage3.1.blocks.0.conv1.conv.weight - torch.Size([128, 128, 1, 1]):
KaimingInit: a=2.23606797749979, mode=fan_in, nonlinearity=leaky_relu, distribution =uniform, bias=0
backbone.stage3.1.blocks.0.conv1.bn.weight - torch.Size([128]):
The value is the same before and after calling `init_weights` of YOLOX
backbone.stage3.1.blocks.0.conv1.bn.bias - torch.Size([128]):
The value is the same before and after calling `init_weights` of YOLOX
backbone.stage3.1.blocks.0.conv2.conv.weight - torch.Size([128, 128, 3, 3]):
KaimingInit: a=2.23606797749979, mode=fan_in, nonlinearity=leaky_relu, distribution =uniform, bias=0
backbone.stage3.1.blocks.0.conv2.bn.weight - torch.Size([128]):
The value is the same before and after calling `init_weights` of YOLOX
backbone.stage3.1.blocks.0.conv2.bn.bias - torch.Size([128]):
The value is the same before and after calling `init_weights` of YOLOX
backbone.stage3.1.blocks.1.conv1.conv.weight - torch.Size([128, 128, 1, 1]):
KaimingInit: a=2.23606797749979, mode=fan_in, nonlinearity=leaky_relu, distribution =uniform, bias=0
backbone.stage3.1.blocks.1.conv1.bn.weight - torch.Size([128]):
The value is the same before and after calling `init_weights` of YOLOX
backbone.stage3.1.blocks.1.conv1.bn.bias - torch.Size([128]):
The value is the same before and after calling `init_weights` of YOLOX
backbone.stage3.1.blocks.1.conv2.conv.weight - torch.Size([128, 128, 3, 3]):
KaimingInit: a=2.23606797749979, mode=fan_in, nonlinearity=leaky_relu, distribution =uniform, bias=0
backbone.stage3.1.blocks.1.conv2.bn.weight - torch.Size([128]):
The value is the same before and after calling `init_weights` of YOLOX
backbone.stage3.1.blocks.1.conv2.bn.bias - torch.Size([128]):
The value is the same before and after calling `init_weights` of YOLOX
backbone.stage3.1.blocks.2.conv1.conv.weight - torch.Size([128, 128, 1, 1]):
KaimingInit: a=2.23606797749979, mode=fan_in, nonlinearity=leaky_relu, distribution =uniform, bias=0
backbone.stage3.1.blocks.2.conv1.bn.weight - torch.Size([128]):
The value is the same before and after calling `init_weights` of YOLOX
backbone.stage3.1.blocks.2.conv1.bn.bias - torch.Size([128]):
The value is the same before and after calling `init_weights` of YOLOX
backbone.stage3.1.blocks.2.conv2.conv.weight - torch.Size([128, 128, 3, 3]):
KaimingInit: a=2.23606797749979, mode=fan_in, nonlinearity=leaky_relu, distribution =uniform, bias=0
backbone.stage3.1.blocks.2.conv2.bn.weight - torch.Size([128]):
The value is the same before and after calling `init_weights` of YOLOX
backbone.stage3.1.blocks.2.conv2.bn.bias - torch.Size([128]):
The value is the same before and after calling `init_weights` of YOLOX
backbone.stage4.0.conv.weight - torch.Size([512, 256, 3, 3]):
KaimingInit: a=2.23606797749979, mode=fan_in, nonlinearity=leaky_relu, distribution =uniform, bias=0
backbone.stage4.0.bn.weight - torch.Size([512]):
The value is the same before and after calling `init_weights` of YOLOX
backbone.stage4.0.bn.bias - torch.Size([512]):
The value is the same before and after calling `init_weights` of YOLOX
backbone.stage4.1.conv1.conv.weight - torch.Size([256, 512, 1, 1]):
KaimingInit: a=2.23606797749979, mode=fan_in, nonlinearity=leaky_relu, distribution =uniform, bias=0
backbone.stage4.1.conv1.bn.weight - torch.Size([256]):
The value is the same before and after calling `init_weights` of YOLOX
backbone.stage4.1.conv1.bn.bias - torch.Size([256]):
The value is the same before and after calling `init_weights` of YOLOX
backbone.stage4.1.conv2.conv.weight - torch.Size([512, 1024, 1, 1]):
KaimingInit: a=2.23606797749979, mode=fan_in, nonlinearity=leaky_relu, distribution =uniform, bias=0
backbone.stage4.1.conv2.bn.weight - torch.Size([512]):
The value is the same before and after calling `init_weights` of YOLOX
backbone.stage4.1.conv2.bn.bias - torch.Size([512]):
The value is the same before and after calling `init_weights` of YOLOX
backbone.stage4.2.main_conv.conv.weight - torch.Size([256, 512, 1, 1]):
KaimingInit: a=2.23606797749979, mode=fan_in, nonlinearity=leaky_relu, distribution =uniform, bias=0
backbone.stage4.2.main_conv.bn.weight - torch.Size([256]):
The value is the same before and after calling `init_weights` of YOLOX
backbone.stage4.2.main_conv.bn.bias - torch.Size([256]):
The value is the same before and after calling `init_weights` of YOLOX
backbone.stage4.2.short_conv.conv.weight - torch.Size([256, 512, 1, 1]):
KaimingInit: a=2.23606797749979, mode=fan_in, nonlinearity=leaky_relu, distribution =uniform, bias=0
backbone.stage4.2.short_conv.bn.weight - torch.Size([256]):
The value is the same before and after calling `init_weights` of YOLOX
backbone.stage4.2.short_conv.bn.bias - torch.Size([256]):
The value is the same before and after calling `init_weights` of YOLOX
backbone.stage4.2.final_conv.conv.weight - torch.Size([512, 512, 1, 1]):
KaimingInit: a=2.23606797749979, mode=fan_in, nonlinearity=leaky_relu, distribution =uniform, bias=0
backbone.stage4.2.final_conv.bn.weight - torch.Size([512]):
The value is the same before and after calling `init_weights` of YOLOX
backbone.stage4.2.final_conv.bn.bias - torch.Size([512]):
The value is the same before and after calling `init_weights` of YOLOX
backbone.stage4.2.blocks.0.conv1.conv.weight - torch.Size([256, 256, 1, 1]):
KaimingInit: a=2.23606797749979, mode=fan_in, nonlinearity=leaky_relu, distribution =uniform, bias=0
backbone.stage4.2.blocks.0.conv1.bn.weight - torch.Size([256]):
The value is the same before and after calling `init_weights` of YOLOX
backbone.stage4.2.blocks.0.conv1.bn.bias - torch.Size([256]):
The value is the same before and after calling `init_weights` of YOLOX
backbone.stage4.2.blocks.0.conv2.conv.weight - torch.Size([256, 256, 3, 3]):
KaimingInit: a=2.23606797749979, mode=fan_in, nonlinearity=leaky_relu, distribution =uniform, bias=0
backbone.stage4.2.blocks.0.conv2.bn.weight - torch.Size([256]):
The value is the same before and after calling `init_weights` of YOLOX
backbone.stage4.2.blocks.0.conv2.bn.bias - torch.Size([256]):
The value is the same before and after calling `init_weights` of YOLOX
neck.reduce_layers.0.conv.weight - torch.Size([256, 512, 1, 1]):
KaimingInit: a=2.23606797749979, mode=fan_in, nonlinearity=leaky_relu, distribution =uniform, bias=0
neck.reduce_layers.0.bn.weight - torch.Size([256]):
The value is the same before and after calling `init_weights` of YOLOX
neck.reduce_layers.0.bn.bias - torch.Size([256]):
The value is the same before and after calling `init_weights` of YOLOX
neck.reduce_layers.1.conv.weight - torch.Size([128, 256, 1, 1]):
KaimingInit: a=2.23606797749979, mode=fan_in, nonlinearity=leaky_relu, distribution =uniform, bias=0
neck.reduce_layers.1.bn.weight - torch.Size([128]):
The value is the same before and after calling `init_weights` of YOLOX
neck.reduce_layers.1.bn.bias - torch.Size([128]):
The value is the same before and after calling `init_weights` of YOLOX
neck.top_down_blocks.0.main_conv.conv.weight - torch.Size([128, 512, 1, 1]):
KaimingInit: a=2.23606797749979, mode=fan_in, nonlinearity=leaky_relu, distribution =uniform, bias=0
neck.top_down_blocks.0.main_conv.bn.weight - torch.Size([128]):
The value is the same before and after calling `init_weights` of YOLOX
neck.top_down_blocks.0.main_conv.bn.bias - torch.Size([128]):
The value is the same before and after calling `init_weights` of YOLOX
neck.top_down_blocks.0.short_conv.conv.weight - torch.Size([128, 512, 1, 1]):
KaimingInit: a=2.23606797749979, mode=fan_in, nonlinearity=leaky_relu, distribution =uniform, bias=0
neck.top_down_blocks.0.short_conv.bn.weight - torch.Size([128]):
The value is the same before and after calling `init_weights` of YOLOX
neck.top_down_blocks.0.short_conv.bn.bias - torch.Size([128]):
The value is the same before and after calling `init_weights` of YOLOX
neck.top_down_blocks.0.final_conv.conv.weight - torch.Size([256, 256, 1, 1]):
KaimingInit: a=2.23606797749979, mode=fan_in, nonlinearity=leaky_relu, distribution =uniform, bias=0
neck.top_down_blocks.0.final_conv.bn.weight - torch.Size([256]):
The value is the same before and after calling `init_weights` of YOLOX
neck.top_down_blocks.0.final_conv.bn.bias - torch.Size([256]):
The value is the same before and after calling `init_weights` of YOLOX
neck.top_down_blocks.0.blocks.0.conv1.conv.weight - torch.Size([128, 128, 1, 1]):
KaimingInit: a=2.23606797749979, mode=fan_in, nonlinearity=leaky_relu, distribution =uniform, bias=0
neck.top_down_blocks.0.blocks.0.conv1.bn.weight - torch.Size([128]):
The value is the same before and after calling `init_weights` of YOLOX
neck.top_down_blocks.0.blocks.0.conv1.bn.bias - torch.Size([128]):
The value is the same before and after calling `init_weights` of YOLOX
neck.top_down_blocks.0.blocks.0.conv2.conv.weight - torch.Size([128, 128, 3, 3]):
KaimingInit: a=2.23606797749979, mode=fan_in, nonlinearity=leaky_relu, distribution =uniform, bias=0
neck.top_down_blocks.0.blocks.0.conv2.bn.weight - torch.Size([128]):
The value is the same before and after calling `init_weights` of YOLOX
neck.top_down_blocks.0.blocks.0.conv2.bn.bias - torch.Size([128]):
The value is the same before and after calling `init_weights` of YOLOX
neck.top_down_blocks.1.main_conv.conv.weight - torch.Size([64, 256, 1, 1]):
KaimingInit: a=2.23606797749979, mode=fan_in, nonlinearity=leaky_relu, distribution =uniform, bias=0
neck.top_down_blocks.1.main_conv.bn.weight - torch.Size([64]):
The value is the same before and after calling `init_weights` of YOLOX
neck.top_down_blocks.1.main_conv.bn.bias - torch.Size([64]):
The value is the same before and after calling `init_weights` of YOLOX
neck.top_down_blocks.1.short_conv.conv.weight - torch.Size([64, 256, 1, 1]):
KaimingInit: a=2.23606797749979, mode=fan_in, nonlinearity=leaky_relu, distribution =uniform, bias=0
neck.top_down_blocks.1.short_conv.bn.weight - torch.Size([64]):
The value is the same before and after calling `init_weights` of YOLOX
neck.top_down_blocks.1.short_conv.bn.bias - torch.Size([64]):
The value is the same before and after calling `init_weights` of YOLOX
neck.top_down_blocks.1.final_conv.conv.weight - torch.Size([128, 128, 1, 1]):
KaimingInit: a=2.23606797749979, mode=fan_in, nonlinearity=leaky_relu, distribution =uniform, bias=0
neck.top_down_blocks.1.final_conv.bn.weight - torch.Size([128]):
The value is the same before and after calling `init_weights` of YOLOX
neck.top_down_blocks.1.final_conv.bn.bias - torch.Size([128]):
The value is the same before and after calling `init_weights` of YOLOX
neck.top_down_blocks.1.blocks.0.conv1.conv.weight - torch.Size([64, 64, 1, 1]):
KaimingInit: a=2.23606797749979, mode=fan_in, nonlinearity=leaky_relu, distribution =uniform, bias=0
neck.top_down_blocks.1.blocks.0.conv1.bn.weight - torch.Size([64]):
The value is the same before and after calling `init_weights` of YOLOX
neck.top_down_blocks.1.blocks.0.conv1.bn.bias - torch.Size([64]):
The value is the same before and after calling `init_weights` of YOLOX
neck.top_down_blocks.1.blocks.0.conv2.conv.weight - torch.Size([64, 64, 3, 3]):
KaimingInit: a=2.23606797749979, mode=fan_in, nonlinearity=leaky_relu, distribution =uniform, bias=0
neck.top_down_blocks.1.blocks.0.conv2.bn.weight - torch.Size([64]):
The value is the same before and after calling `init_weights` of YOLOX
neck.top_down_blocks.1.blocks.0.conv2.bn.bias - torch.Size([64]):
The value is the same before and after calling `init_weights` of YOLOX
neck.downsamples.0.conv.weight - torch.Size([128, 128, 3, 3]):
KaimingInit: a=2.23606797749979, mode=fan_in, nonlinearity=leaky_relu, distribution =uniform, bias=0
neck.downsamples.0.bn.weight - torch.Size([128]):
The value is the same before and after calling `init_weights` of YOLOX
neck.downsamples.0.bn.bias - torch.Size([128]):
The value is the same before and after calling `init_weights` of YOLOX
neck.downsamples.1.conv.weight - torch.Size([256, 256, 3, 3]):
KaimingInit: a=2.23606797749979, mode=fan_in, nonlinearity=leaky_relu, distribution =uniform, bias=0
neck.downsamples.1.bn.weight - torch.Size([256]):
The value is the same before and after calling `init_weights` of YOLOX
neck.downsamples.1.bn.bias - torch.Size([256]):
The value is the same before and after calling `init_weights` of YOLOX
neck.bottom_up_blocks.0.main_conv.conv.weight - torch.Size([128, 256, 1, 1]):
KaimingInit: a=2.23606797749979, mode=fan_in, nonlinearity=leaky_relu, distribution =uniform, bias=0
neck.bottom_up_blocks.0.main_conv.bn.weight - torch.Size([128]):
The value is the same before and after calling `init_weights` of YOLOX
neck.bottom_up_blocks.0.main_conv.bn.bias - torch.Size([128]):
The value is the same before and after calling `init_weights` of YOLOX
neck.bottom_up_blocks.0.short_conv.conv.weight - torch.Size([128, 256, 1, 1]):
KaimingInit: a=2.23606797749979, mode=fan_in, nonlinearity=leaky_relu, distribution =uniform, bias=0
neck.bottom_up_blocks.0.short_conv.bn.weight - torch.Size([128]):
The value is the same before and after calling `init_weights` of YOLOX
neck.bottom_up_blocks.0.short_conv.bn.bias - torch.Size([128]):
The value is the same before and after calling `init_weights` of YOLOX
neck.bottom_up_blocks.0.final_conv.conv.weight - torch.Size([256, 256, 1, 1]):
KaimingInit: a=2.23606797749979, mode=fan_in, nonlinearity=leaky_relu, distribution =uniform, bias=0
neck.bottom_up_blocks.0.final_conv.bn.weight - torch.Size([256]):
The value is the same before and after calling `init_weights` of YOLOX
neck.bottom_up_blocks.0.final_conv.bn.bias - torch.Size([256]):
The value is the same before and after calling `init_weights` of YOLOX
neck.bottom_up_blocks.0.blocks.0.conv1.conv.weight - torch.Size([128, 128, 1, 1]):
KaimingInit: a=2.23606797749979, mode=fan_in, nonlinearity=leaky_relu, distribution =uniform, bias=0
neck.bottom_up_blocks.0.blocks.0.conv1.bn.weight - torch.Size([128]):
The value is the same before and after calling `init_weights` of YOLOX
neck.bottom_up_blocks.0.blocks.0.conv1.bn.bias - torch.Size([128]):
The value is the same before and after calling `init_weights` of YOLOX
neck.bottom_up_blocks.0.blocks.0.conv2.conv.weight - torch.Size([128, 128, 3, 3]):
KaimingInit: a=2.23606797749979, mode=fan_in, nonlinearity=leaky_relu, distribution =uniform, bias=0
neck.bottom_up_blocks.0.blocks.0.conv2.bn.weight - torch.Size([128]):
The value is the same before and after calling `init_weights` of YOLOX
neck.bottom_up_blocks.0.blocks.0.conv2.bn.bias - torch.Size([128]):
The value is the same before and after calling `init_weights` of YOLOX
neck.bottom_up_blocks.1.main_conv.conv.weight - torch.Size([256, 512, 1, 1]):
KaimingInit: a=2.23606797749979, mode=fan_in, nonlinearity=leaky_relu, distribution =uniform, bias=0
neck.bottom_up_blocks.1.main_conv.bn.weight - torch.Size([256]):
The value is the same before and after calling `init_weights` of YOLOX
neck.bottom_up_blocks.1.main_conv.bn.bias - torch.Size([256]):
The value is the same before and after calling `init_weights` of YOLOX
neck.bottom_up_blocks.1.short_conv.conv.weight - torch.Size([256, 512, 1, 1]):
KaimingInit: a=2.23606797749979, mode=fan_in, nonlinearity=leaky_relu, distribution =uniform, bias=0
neck.bottom_up_blocks.1.short_conv.bn.weight - torch.Size([256]):
The value is the same before and after calling `init_weights` of YOLOX
neck.bottom_up_blocks.1.short_conv.bn.bias - torch.Size([256]):
The value is the same before and after calling `init_weights` of YOLOX
neck.bottom_up_blocks.1.final_conv.conv.weight - torch.Size([512, 512, 1, 1]):
KaimingInit: a=2.23606797749979, mode=fan_in, nonlinearity=leaky_relu, distribution =uniform, bias=0
neck.bottom_up_blocks.1.final_conv.bn.weight - torch.Size([512]):
The value is the same before and after calling `init_weights` of YOLOX
neck.bottom_up_blocks.1.final_conv.bn.bias - torch.Size([512]):
The value is the same before and after calling `init_weights` of YOLOX
neck.bottom_up_blocks.1.blocks.0.conv1.conv.weight - torch.Size([256, 256, 1, 1]):
KaimingInit: a=2.23606797749979, mode=fan_in, nonlinearity=leaky_relu, distribution =uniform, bias=0
neck.bottom_up_blocks.1.blocks.0.conv1.bn.weight - torch.Size([256]):
The value is the same before and after calling `init_weights` of YOLOX
neck.bottom_up_blocks.1.blocks.0.conv1.bn.bias - torch.Size([256]):
The value is the same before and after calling `init_weights` of YOLOX
neck.bottom_up_blocks.1.blocks.0.conv2.conv.weight - torch.Size([256, 256, 3, 3]):
KaimingInit: a=2.23606797749979, mode=fan_in, nonlinearity=leaky_relu, distribution =uniform, bias=0
neck.bottom_up_blocks.1.blocks.0.conv2.bn.weight - torch.Size([256]):
The value is the same before and after calling `init_weights` of YOLOX
neck.bottom_up_blocks.1.blocks.0.conv2.bn.bias - torch.Size([256]):
The value is the same before and after calling `init_weights` of YOLOX
neck.out_convs.0.conv.weight - torch.Size([128, 128, 1, 1]):
KaimingInit: a=2.23606797749979, mode=fan_in, nonlinearity=leaky_relu, distribution =uniform, bias=0
neck.out_convs.0.bn.weight - torch.Size([128]):
The value is the same before and after calling `init_weights` of YOLOX
neck.out_convs.0.bn.bias - torch.Size([128]):
The value is the same before and after calling `init_weights` of YOLOX
neck.out_convs.1.conv.weight - torch.Size([128, 256, 1, 1]):
KaimingInit: a=2.23606797749979, mode=fan_in, nonlinearity=leaky_relu, distribution =uniform, bias=0
neck.out_convs.1.bn.weight - torch.Size([128]):
The value is the same before and after calling `init_weights` of YOLOX
neck.out_convs.1.bn.bias - torch.Size([128]):
The value is the same before and after calling `init_weights` of YOLOX
neck.out_convs.2.conv.weight - torch.Size([128, 512, 1, 1]):
KaimingInit: a=2.23606797749979, mode=fan_in, nonlinearity=leaky_relu, distribution =uniform, bias=0
neck.out_convs.2.bn.weight - torch.Size([128]):
The value is the same before and after calling `init_weights` of YOLOX
neck.out_convs.2.bn.bias - torch.Size([128]):
The value is the same before and after calling `init_weights` of YOLOX
bbox_head.multi_level_cls_convs.0.0.conv.weight - torch.Size([128, 128, 3, 3]):
KaimingInit: a=2.23606797749979, mode=fan_in, nonlinearity=leaky_relu, distribution =uniform, bias=0
bbox_head.multi_level_cls_convs.0.0.bn.weight - torch.Size([128]):
The value is the same before and after calling `init_weights` of YOLOX
bbox_head.multi_level_cls_convs.0.0.bn.bias - torch.Size([128]):
The value is the same before and after calling `init_weights` of YOLOX
bbox_head.multi_level_cls_convs.0.1.conv.weight - torch.Size([128, 128, 3, 3]):
KaimingInit: a=2.23606797749979, mode=fan_in, nonlinearity=leaky_relu, distribution =uniform, bias=0
bbox_head.multi_level_cls_convs.0.1.bn.weight - torch.Size([128]):
The value is the same before and after calling `init_weights` of YOLOX
bbox_head.multi_level_cls_convs.0.1.bn.bias - torch.Size([128]):
The value is the same before and after calling `init_weights` of YOLOX
bbox_head.multi_level_cls_convs.1.0.conv.weight - torch.Size([128, 128, 3, 3]):
KaimingInit: a=2.23606797749979, mode=fan_in, nonlinearity=leaky_relu, distribution =uniform, bias=0
bbox_head.multi_level_cls_convs.1.0.bn.weight - torch.Size([128]):
The value is the same before and after calling `init_weights` of YOLOX
bbox_head.multi_level_cls_convs.1.0.bn.bias - torch.Size([128]):
The value is the same before and after calling `init_weights` of YOLOX
bbox_head.multi_level_cls_convs.1.1.conv.weight - torch.Size([128, 128, 3, 3]):
KaimingInit: a=2.23606797749979, mode=fan_in, nonlinearity=leaky_relu, distribution =uniform, bias=0
bbox_head.multi_level_cls_convs.1.1.bn.weight - torch.Size([128]):
The value is the same before and after calling `init_weights` of YOLOX
bbox_head.multi_level_cls_convs.1.1.bn.bias - torch.Size([128]):
The value is the same before and after calling `init_weights` of YOLOX
bbox_head.multi_level_cls_convs.2.0.conv.weight - torch.Size([128, 128, 3, 3]):
KaimingInit: a=2.23606797749979, mode=fan_in, nonlinearity=leaky_relu, distribution =uniform, bias=0
bbox_head.multi_level_cls_convs.2.0.bn.weight - torch.Size([128]):
The value is the same before and after calling `init_weights` of YOLOX
bbox_head.multi_level_cls_convs.2.0.bn.bias - torch.Size([128]):
The value is the same before and after calling `init_weights` of YOLOX
bbox_head.multi_level_cls_convs.2.1.conv.weight - torch.Size([128, 128, 3, 3]):
KaimingInit: a=2.23606797749979, mode=fan_in, nonlinearity=leaky_relu, distribution =uniform, bias=0
bbox_head.multi_level_cls_convs.2.1.bn.weight - torch.Size([128]):
The value is the same before and after calling `init_weights` of YOLOX
bbox_head.multi_level_cls_convs.2.1.bn.bias - torch.Size([128]):
The value is the same before and after calling `init_weights` of YOLOX
bbox_head.multi_level_reg_convs.0.0.conv.weight - torch.Size([128, 128, 3, 3]):
KaimingInit: a=2.23606797749979, mode=fan_in, nonlinearity=leaky_relu, distribution =uniform, bias=0
bbox_head.multi_level_reg_convs.0.0.bn.weight - torch.Size([128]):
The value is the same before and after calling `init_weights` of YOLOX
bbox_head.multi_level_reg_convs.0.0.bn.bias - torch.Size([128]):
The value is the same before and after calling `init_weights` of YOLOX
bbox_head.multi_level_reg_convs.0.1.conv.weight - torch.Size([128, 128, 3, 3]):
KaimingInit: a=2.23606797749979, mode=fan_in, nonlinearity=leaky_relu, distribution =uniform, bias=0
bbox_head.multi_level_reg_convs.0.1.bn.weight - torch.Size([128]):
The value is the same before and after calling `init_weights` of YOLOX
bbox_head.multi_level_reg_convs.0.1.bn.bias - torch.Size([128]):
The value is the same before and after calling `init_weights` of YOLOX
bbox_head.multi_level_reg_convs.1.0.conv.weight - torch.Size([128, 128, 3, 3]):
KaimingInit: a=2.23606797749979, mode=fan_in, nonlinearity=leaky_relu, distribution =uniform, bias=0
bbox_head.multi_level_reg_convs.1.0.bn.weight - torch.Size([128]):
The value is the same before and after calling `init_weights` of YOLOX
bbox_head.multi_level_reg_convs.1.0.bn.bias - torch.Size([128]):
The value is the same before and after calling `init_weights` of YOLOX
bbox_head.multi_level_reg_convs.1.1.conv.weight - torch.Size([128, 128, 3, 3]):
KaimingInit: a=2.23606797749979, mode=fan_in, nonlinearity=leaky_relu, distribution =uniform, bias=0
bbox_head.multi_level_reg_convs.1.1.bn.weight - torch.Size([128]):
The value is the same before and after calling `init_weights` of YOLOX
bbox_head.multi_level_reg_convs.1.1.bn.bias - torch.Size([128]):
The value is the same before and after calling `init_weights` of YOLOX
bbox_head.multi_level_reg_convs.2.0.conv.weight - torch.Size([128, 128, 3, 3]):
KaimingInit: a=2.23606797749979, mode=fan_in, nonlinearity=leaky_relu, distribution =uniform, bias=0
bbox_head.multi_level_reg_convs.2.0.bn.weight - torch.Size([128]):
The value is the same before and after calling `init_weights` of YOLOX
bbox_head.multi_level_reg_convs.2.0.bn.bias - torch.Size([128]):
The value is the same before and after calling `init_weights` of YOLOX
bbox_head.multi_level_reg_convs.2.1.conv.weight - torch.Size([128, 128, 3, 3]):
KaimingInit: a=2.23606797749979, mode=fan_in, nonlinearity=leaky_relu, distribution =uniform, bias=0
bbox_head.multi_level_reg_convs.2.1.bn.weight - torch.Size([128]):
The value is the same before and after calling `init_weights` of YOLOX
bbox_head.multi_level_reg_convs.2.1.bn.bias - torch.Size([128]):
The value is the same before and after calling `init_weights` of YOLOX
bbox_head.multi_level_conv_cls.0.weight - torch.Size([5, 128, 1, 1]):
KaimingInit: a=2.23606797749979, mode=fan_in, nonlinearity=leaky_relu, distribution =uniform, bias=0
bbox_head.multi_level_conv_cls.0.bias - torch.Size([5]):
Initialized by user-defined `init_weights` in YOLOXHead
bbox_head.multi_level_conv_cls.1.weight - torch.Size([5, 128, 1, 1]):
KaimingInit: a=2.23606797749979, mode=fan_in, nonlinearity=leaky_relu, distribution =uniform, bias=0
bbox_head.multi_level_conv_cls.1.bias - torch.Size([5]):
Initialized by user-defined `init_weights` in YOLOXHead
bbox_head.multi_level_conv_cls.2.weight - torch.Size([5, 128, 1, 1]):
KaimingInit: a=2.23606797749979, mode=fan_in, nonlinearity=leaky_relu, distribution =uniform, bias=0
bbox_head.multi_level_conv_cls.2.bias - torch.Size([5]):
Initialized by user-defined `init_weights` in YOLOXHead
bbox_head.multi_level_conv_reg.0.weight - torch.Size([4, 128, 1, 1]):
KaimingInit: a=2.23606797749979, mode=fan_in, nonlinearity=leaky_relu, distribution =uniform, bias=0
bbox_head.multi_level_conv_reg.0.bias - torch.Size([4]):
KaimingInit: a=2.23606797749979, mode=fan_in, nonlinearity=leaky_relu, distribution =uniform, bias=0
bbox_head.multi_level_conv_reg.1.weight - torch.Size([4, 128, 1, 1]):
KaimingInit: a=2.23606797749979, mode=fan_in, nonlinearity=leaky_relu, distribution =uniform, bias=0
bbox_head.multi_level_conv_reg.1.bias - torch.Size([4]):
KaimingInit: a=2.23606797749979, mode=fan_in, nonlinearity=leaky_relu, distribution =uniform, bias=0
bbox_head.multi_level_conv_reg.2.weight - torch.Size([4, 128, 1, 1]):
KaimingInit: a=2.23606797749979, mode=fan_in, nonlinearity=leaky_relu, distribution =uniform, bias=0
bbox_head.multi_level_conv_reg.2.bias - torch.Size([4]):
KaimingInit: a=2.23606797749979, mode=fan_in, nonlinearity=leaky_relu, distribution =uniform, bias=0
bbox_head.multi_level_conv_obj.0.weight - torch.Size([1, 128, 1, 1]):
KaimingInit: a=2.23606797749979, mode=fan_in, nonlinearity=leaky_relu, distribution =uniform, bias=0
bbox_head.multi_level_conv_obj.0.bias - torch.Size([1]):
Initialized by user-defined `init_weights` in YOLOXHead
bbox_head.multi_level_conv_obj.1.weight - torch.Size([1, 128, 1, 1]):
KaimingInit: a=2.23606797749979, mode=fan_in, nonlinearity=leaky_relu, distribution =uniform, bias=0
bbox_head.multi_level_conv_obj.1.bias - torch.Size([1]):
Initialized by user-defined `init_weights` in YOLOXHead
bbox_head.multi_level_conv_obj.2.weight - torch.Size([1, 128, 1, 1]):
KaimingInit: a=2.23606797749979, mode=fan_in, nonlinearity=leaky_relu, distribution =uniform, bias=0
bbox_head.multi_level_conv_obj.2.bias - torch.Size([1]):
Initialized by user-defined `init_weights` in YOLOXHead
2024-11-25 16:21:53,414 - mmdet - INFO - =========================================================================================================
Layer (type:depth-idx) Output Shape Param #
=========================================================================================================
YOLOX [1, 5, 80, 80] --
├─CSPDarknet: 1-1 [1, 128, 80, 80] --
│ └─FocusLite: 2-1 [1, 32, 320, 320] --
│ │ └─ConvModule: 3-1 [1, 12, 320, 320] --
│ │ │ └─Conv2d: 4-1 [1, 12, 320, 320] 324
│ │ │ └─BatchNorm2d: 4-2 [1, 12, 320, 320] 24
│ │ └─ConvModule: 3-2 [1, 32, 320, 320] --
│ │ │ └─Conv2d: 4-3 [1, 32, 320, 320] 3,456
│ │ │ └─BatchNorm2d: 4-4 [1, 32, 320, 320] 64
│ │ │ └─ReLU: 4-5 [1, 32, 320, 320] --
│ └─Sequential: 2-2 [1, 64, 160, 160] --
│ │ └─ConvModule: 3-3 [1, 64, 160, 160] --
│ │ │ └─Conv2d: 4-6 [1, 64, 160, 160] 18,432
│ │ │ └─BatchNorm2d: 4-7 [1, 64, 160, 160] 128
│ │ │ └─ReLU: 4-8 [1, 64, 160, 160] --
│ │ └─CSPLayer: 3-4 [1, 64, 160, 160] --
│ │ │ └─ConvModule: 4-9 [1, 32, 160, 160] --
│ │ │ │ └─Conv2d: 5-1 [1, 32, 160, 160] 2,048
│ │ │ │ └─BatchNorm2d: 5-2 [1, 32, 160, 160] 64
│ │ │ │ └─ReLU: 5-3 [1, 32, 160, 160] --
│ │ │ └─ConvModule: 4-10 [1, 32, 160, 160] --
│ │ │ │ └─Conv2d: 5-4 [1, 32, 160, 160] 2,048
│ │ │ │ └─BatchNorm2d: 5-5 [1, 32, 160, 160] 64
│ │ │ │ └─ReLU: 5-6 [1, 32, 160, 160] --
│ │ │ └─Sequential: 4-11 [1, 32, 160, 160] --
│ │ │ │ └─DarknetBottleneck: 5-7 [1, 32, 160, 160] --
│ │ │ │ │ └─ConvModule: 6-1 [1, 32, 160, 160] --
│ │ │ │ │ │ └─Conv2d: 7-1 [1, 32, 160, 160] 1,024
│ │ │ │ │ │ └─BatchNorm2d: 7-2 [1, 32, 160, 160] 64
│ │ │ │ │ │ └─ReLU: 7-3 [1, 32, 160, 160] --
│ │ │ │ │ └─ConvModule: 6-2 [1, 32, 160, 160] --
│ │ │ │ │ │ └─Conv2d: 7-4 [1, 32, 160, 160] 9,216
│ │ │ │ │ │ └─BatchNorm2d: 7-5 [1, 32, 160, 160] 64
│ │ │ │ │ │ └─ReLU: 7-6 [1, 32, 160, 160] --
│ │ │ └─ConvModule: 4-12 [1, 64, 160, 160] --
│ │ │ │ └─Conv2d: 5-8 [1, 64, 160, 160] 4,096
│ │ │ │ └─BatchNorm2d: 5-9 [1, 64, 160, 160] 128
│ │ │ │ └─ReLU: 5-10 [1, 64, 160, 160] --
│ └─Sequential: 2-3 [1, 128, 80, 80] --
│ │ └─ConvModule: 3-5 [1, 128, 80, 80] --
│ │ │ └─Conv2d: 4-13 [1, 128, 80, 80] 73,728
│ │ │ └─BatchNorm2d: 4-14 [1, 128, 80, 80] 256
│ │ │ └─ReLU: 4-15 [1, 128, 80, 80] --
│ │ └─CSPLayer: 3-6 [1, 128, 80, 80] --
│ │ │ └─ConvModule: 4-16 [1, 64, 80, 80] --
│ │ │ │ └─Conv2d: 5-11 [1, 64, 80, 80] 8,192
│ │ │ │ └─BatchNorm2d: 5-12 [1, 64, 80, 80] 128
│ │ │ │ └─ReLU: 5-13 [1, 64, 80, 80] --
│ │ │ └─ConvModule: 4-17 [1, 64, 80, 80] --
│ │ │ │ └─Conv2d: 5-14 [1, 64, 80, 80] 8,192
│ │ │ │ └─BatchNorm2d: 5-15 [1, 64, 80, 80] 128
│ │ │ │ └─ReLU: 5-16 [1, 64, 80, 80] --
│ │ │ └─Sequential: 4-18 [1, 64, 80, 80] --
│ │ │ │ └─DarknetBottleneck: 5-17 [1, 64, 80, 80] --
│ │ │ │ │ └─ConvModule: 6-3 [1, 64, 80, 80] --
│ │ │ │ │ │ └─Conv2d: 7-7 [1, 64, 80, 80] 4,096
│ │ │ │ │ │ └─BatchNorm2d: 7-8 [1, 64, 80, 80] 128
│ │ │ │ │ │ └─ReLU: 7-9 [1, 64, 80, 80] --
│ │ │ │ │ └─ConvModule: 6-4 [1, 64, 80, 80] --
│ │ │ │ │ │ └─Conv2d: 7-10 [1, 64, 80, 80] 36,864
│ │ │ │ │ │ └─BatchNorm2d: 7-11 [1, 64, 80, 80] 128
│ │ │ │ │ │ └─ReLU: 7-12 [1, 64, 80, 80] --
│ │ │ │ └─DarknetBottleneck: 5-18 [1, 64, 80, 80] --
│ │ │ │ │ └─ConvModule: 6-5 [1, 64, 80, 80] --
│ │ │ │ │ │ └─Conv2d: 7-13 [1, 64, 80, 80] 4,096
│ │ │ │ │ │ └─BatchNorm2d: 7-14 [1, 64, 80, 80] 128
│ │ │ │ │ │ └─ReLU: 7-15 [1, 64, 80, 80] --
│ │ │ │ │ └─ConvModule: 6-6 [1, 64, 80, 80] --
│ │ │ │ │ │ └─Conv2d: 7-16 [1, 64, 80, 80] 36,864
│ │ │ │ │ │ └─BatchNorm2d: 7-17 [1, 64, 80, 80] 128
│ │ │ │ │ │ └─ReLU: 7-18 [1, 64, 80, 80] --
│ │ │ │ └─DarknetBottleneck: 5-19 [1, 64, 80, 80] --
│ │ │ │ │ └─ConvModule: 6-7 [1, 64, 80, 80] --
│ │ │ │ │ │ └─Conv2d: 7-19 [1, 64, 80, 80] 4,096
│ │ │ │ │ │ └─BatchNorm2d: 7-20 [1, 64, 80, 80] 128
│ │ │ │ │ │ └─ReLU: 7-21 [1, 64, 80, 80] --
│ │ │ │ │ └─ConvModule: 6-8 [1, 64, 80, 80] --
│ │ │ │ │ │ └─Conv2d: 7-22 [1, 64, 80, 80] 36,864
│ │ │ │ │ │ └─BatchNorm2d: 7-23 [1, 64, 80, 80] 128
│ │ │ │ │ │ └─ReLU: 7-24 [1, 64, 80, 80] --
│ │ │ └─ConvModule: 4-19 [1, 128, 80, 80] --
│ │ │ │ └─Conv2d: 5-20 [1, 128, 80, 80] 16,384
│ │ │ │ └─BatchNorm2d: 5-21 [1, 128, 80, 80] 256
│ │ │ │ └─ReLU: 5-22 [1, 128, 80, 80] --
│ └─Sequential: 2-4 [1, 256, 40, 40] --
│ │ └─ConvModule: 3-7 [1, 256, 40, 40] --
│ │ │ └─Conv2d: 4-20 [1, 256, 40, 40] 294,912
│ │ │ └─BatchNorm2d: 4-21 [1, 256, 40, 40] 512
│ │ │ └─ReLU: 4-22 [1, 256, 40, 40] --
│ │ └─CSPLayer: 3-8 [1, 256, 40, 40] --
│ │ │ └─ConvModule: 4-23 [1, 128, 40, 40] --
│ │ │ │ └─Conv2d: 5-23 [1, 128, 40, 40] 32,768
│ │ │ │ └─BatchNorm2d: 5-24 [1, 128, 40, 40] 256
│ │ │ │ └─ReLU: 5-25 [1, 128, 40, 40] --
│ │ │ └─ConvModule: 4-24 [1, 128, 40, 40] --
│ │ │ │ └─Conv2d: 5-26 [1, 128, 40, 40] 32,768
│ │ │ │ └─BatchNorm2d: 5-27 [1, 128, 40, 40] 256
│ │ │ │ └─ReLU: 5-28 [1, 128, 40, 40] --
│ │ │ └─Sequential: 4-25 [1, 128, 40, 40] --
│ │ │ │ └─DarknetBottleneck: 5-29 [1, 128, 40, 40] --
│ │ │ │ │ └─ConvModule: 6-9 [1, 128, 40, 40] --
│ │ │ │ │ │ └─Conv2d: 7-25 [1, 128, 40, 40] 16,384
│ │ │ │ │ │ └─BatchNorm2d: 7-26 [1, 128, 40, 40] 256
│ │ │ │ │ │ └─ReLU: 7-27 [1, 128, 40, 40] --
│ │ │ │ │ └─ConvModule: 6-10 [1, 128, 40, 40] --
│ │ │ │ │ │ └─Conv2d: 7-28 [1, 128, 40, 40] 147,456
│ │ │ │ │ │ └─BatchNorm2d: 7-29 [1, 128, 40, 40] 256
│ │ │ │ │ │ └─ReLU: 7-30 [1, 128, 40, 40] --
│ │ │ │ └─DarknetBottleneck: 5-30 [1, 128, 40, 40] --
│ │ │ │ │ └─ConvModule: 6-11 [1, 128, 40, 40] --
│ │ │ │ │ │ └─Conv2d: 7-31 [1, 128, 40, 40] 16,384
│ │ │ │ │ │ └─BatchNorm2d: 7-32 [1, 128, 40, 40] 256
│ │ │ │ │ │ └─ReLU: 7-33 [1, 128, 40, 40] --
│ │ │ │ │ └─ConvModule: 6-12 [1, 128, 40, 40] --
│ │ │ │ │ │ └─Conv2d: 7-34 [1, 128, 40, 40] 147,456
│ │ │ │ │ │ └─BatchNorm2d: 7-35 [1, 128, 40, 40] 256
│ │ │ │ │ │ └─ReLU: 7-36 [1, 128, 40, 40] --
│ │ │ │ └─DarknetBottleneck: 5-31 [1, 128, 40, 40] --
│ │ │ │ │ └─ConvModule: 6-13 [1, 128, 40, 40] --
│ │ │ │ │ │ └─Conv2d: 7-37 [1, 128, 40, 40] 16,384
│ │ │ │ │ │ └─BatchNorm2d: 7-38 [1, 128, 40, 40] 256
│ │ │ │ │ │ └─ReLU: 7-39 [1, 128, 40, 40] --
│ │ │ │ │ └─ConvModule: 6-14 [1, 128, 40, 40] --
│ │ │ │ │ │ └─Conv2d: 7-40 [1, 128, 40, 40] 147,456
│ │ │ │ │ │ └─BatchNorm2d: 7-41 [1, 128, 40, 40] 256
│ │ │ │ │ │ └─ReLU: 7-42 [1, 128, 40, 40] --
│ │ │ └─ConvModule: 4-26 [1, 256, 40, 40] --
│ │ │ │ └─Conv2d: 5-32 [1, 256, 40, 40] 65,536
│ │ │ │ └─BatchNorm2d: 5-33 [1, 256, 40, 40] 512
│ │ │ │ └─ReLU: 5-34 [1, 256, 40, 40] --
│ └─Sequential: 2-5 [1, 512, 20, 20] --
│ │ └─ConvModule: 3-9 [1, 512, 20, 20] --
│ │ │ └─Conv2d: 4-27 [1, 512, 20, 20] 1,179,648
│ │ │ └─BatchNorm2d: 4-28 [1, 512, 20, 20] 1,024
│ │ │ └─ReLU: 4-29 [1, 512, 20, 20] --
│ │ └─SPPBottleneck: 3-10 [1, 512, 20, 20] --
│ │ │ └─ConvModule: 4-30 [1, 256, 20, 20] --
│ │ │ │ └─Conv2d: 5-35 [1, 256, 20, 20] 131,072
│ │ │ │ └─BatchNorm2d: 5-36 [1, 256, 20, 20] 512
│ │ │ │ └─ReLU: 5-37 [1, 256, 20, 20] --
│ │ │ └─ModuleList: 4-31 -- --
│ │ │ │ └─SequentialMaxPool2d: 5-38 [1, 256, 20, 20] --
│ │ │ │ │ └─MaxPool2d: 6-15 [1, 256, 20, 20] --
│ │ │ │ │ └─MaxPool2d: 6-16 [1, 256, 20, 20] --
│ │ │ │ └─SequentialMaxPool2d: 5-39 [1, 256, 20, 20] --
│ │ │ │ │ └─MaxPool2d: 6-17 [1, 256, 20, 20] --
│ │ │ │ │ └─MaxPool2d: 6-18 [1, 256, 20, 20] --
│ │ │ │ │ └─MaxPool2d: 6-19 [1, 256, 20, 20] --
│ │ │ │ │ └─MaxPool2d: 6-20 [1, 256, 20, 20] --
│ │ │ │ └─SequentialMaxPool2d: 5-40 [1, 256, 20, 20] --
│ │ │ │ │ └─MaxPool2d: 6-21 [1, 256, 20, 20] --
│ │ │ │ │ └─MaxPool2d: 6-22 [1, 256, 20, 20] --
│ │ │ │ │ └─MaxPool2d: 6-23 [1, 256, 20, 20] --
│ │ │ │ │ └─MaxPool2d: 6-24 [1, 256, 20, 20] --
│ │ │ │ │ └─MaxPool2d: 6-25 [1, 256, 20, 20] --
│ │ │ │ │ └─MaxPool2d: 6-26 [1, 256, 20, 20] --
│ │ │ └─ConvModule: 4-32 [1, 512, 20, 20] --
│ │ │ │ └─Conv2d: 5-41 [1, 512, 20, 20] 524,288
│ │ │ │ └─BatchNorm2d: 5-42 [1, 512, 20, 20] 1,024
│ │ │ │ └─ReLU: 5-43 [1, 512, 20, 20] --
│ │ └─CSPLayer: 3-11 [1, 512, 20, 20] --
│ │ │ └─ConvModule: 4-33 [1, 256, 20, 20] --
│ │ │ │ └─Conv2d: 5-44 [1, 256, 20, 20] 131,072
│ │ │ │ └─BatchNorm2d: 5-45 [1, 256, 20, 20] 512
│ │ │ │ └─ReLU: 5-46 [1, 256, 20, 20] --
│ │ │ └─ConvModule: 4-34 [1, 256, 20, 20] --
│ │ │ │ └─Conv2d: 5-47 [1, 256, 20, 20] 131,072
│ │ │ │ └─BatchNorm2d: 5-48 [1, 256, 20, 20] 512
│ │ │ │ └─ReLU: 5-49 [1, 256, 20, 20] --
│ │ │ └─Sequential: 4-35 [1, 256, 20, 20] --
│ │ │ │ └─DarknetBottleneck: 5-50 [1, 256, 20, 20] --
│ │ │ │ │ └─ConvModule: 6-27 [1, 256, 20, 20] --
│ │ │ │ │ │ └─Conv2d: 7-43 [1, 256, 20, 20] 65,536
│ │ │ │ │ │ └─BatchNorm2d: 7-44 [1, 256, 20, 20] 512
│ │ │ │ │ │ └─ReLU: 7-45 [1, 256, 20, 20] --
│ │ │ │ │ └─ConvModule: 6-28 [1, 256, 20, 20] --
│ │ │ │ │ │ └─Conv2d: 7-46 [1, 256, 20, 20] 589,824
│ │ │ │ │ │ └─BatchNorm2d: 7-47 [1, 256, 20, 20] 512
│ │ │ │ │ │ └─ReLU: 7-48 [1, 256, 20, 20] --
│ │ │ └─ConvModule: 4-36 [1, 512, 20, 20] --
│ │ │ │ └─Conv2d: 5-51 [1, 512, 20, 20] 262,144
│ │ │ │ └─BatchNorm2d: 5-52 [1, 512, 20, 20] 1,024
│ │ │ │ └─ReLU: 5-53 [1, 512, 20, 20] --
├─YOLOXPAFPN: 1-2 [1, 128, 80, 80] --
│ └─ModuleList: 2-9 -- (recursive)
│ │ └─ConvModule: 3-12 [1, 256, 20, 20] --
│ │ │ └─Conv2d: 4-37 [1, 256, 20, 20] 131,072
│ │ │ └─BatchNorm2d: 4-38 [1, 256, 20, 20] 512
│ │ │ └─ReLU: 4-39 [1, 256, 20, 20] --
│ └─Upsample: 2-7 [1, 256, 40, 40] --
│ └─ModuleList: 2-11 -- (recursive)
│ │ └─CSPLayer: 3-13 [1, 256, 40, 40] --
│ │ │ └─ConvModule: 4-40 [1, 128, 40, 40] --
│ │ │ │ └─Conv2d: 5-54 [1, 128, 40, 40] 65,536
│ │ │ │ └─BatchNorm2d: 5-55 [1, 128, 40, 40] 256
│ │ │ │ └─ReLU: 5-56 [1, 128, 40, 40] --
│ │ │ └─ConvModule: 4-41 [1, 128, 40, 40] --
│ │ │ │ └─Conv2d: 5-57 [1, 128, 40, 40] 65,536
│ │ │ │ └─BatchNorm2d: 5-58 [1, 128, 40, 40] 256
│ │ │ │ └─ReLU: 5-59 [1, 128, 40, 40] --
│ │ │ └─Sequential: 4-42 [1, 128, 40, 40] --
│ │ │ │ └─DarknetBottleneck: 5-60 [1, 128, 40, 40] --
│ │ │ │ │ └─ConvModule: 6-29 [1, 128, 40, 40] --
│ │ │ │ │ │ └─Conv2d: 7-49 [1, 128, 40, 40] 16,384
│ │ │ │ │ │ └─BatchNorm2d: 7-50 [1, 128, 40, 40] 256
│ │ │ │ │ │ └─ReLU: 7-51 [1, 128, 40, 40] --
│ │ │ │ │ └─ConvModule: 6-30 [1, 128, 40, 40] --
│ │ │ │ │ │ └─Conv2d: 7-52 [1, 128, 40, 40] 147,456
│ │ │ │ │ │ └─BatchNorm2d: 7-53 [1, 128, 40, 40] 256
│ │ │ │ │ │ └─ReLU: 7-54 [1, 128, 40, 40] --
│ │ │ └─ConvModule: 4-43 [1, 256, 40, 40] --
│ │ │ │ └─Conv2d: 5-61 [1, 256, 40, 40] 65,536
│ │ │ │ └─BatchNorm2d: 5-62 [1, 256, 40, 40] 512
│ │ │ │ └─ReLU: 5-63 [1, 256, 40, 40] --
│ └─ModuleList: 2-9 -- (recursive)
│ │ └─ConvModule: 3-14 [1, 128, 40, 40] --
│ │ │ └─Conv2d: 4-44 [1, 128, 40, 40] 32,768
│ │ │ └─BatchNorm2d: 4-45 [1, 128, 40, 40] 256
│ │ │ └─ReLU: 4-46 [1, 128, 40, 40] --
│ └─Upsample: 2-10 [1, 128, 80, 80] --
│ └─ModuleList: 2-11 -- (recursive)
│ │ └─CSPLayer: 3-15 [1, 128, 80, 80] --
│ │ │ └─ConvModule: 4-47 [1, 64, 80, 80] --
│ │ │ │ └─Conv2d: 5-64 [1, 64, 80, 80] 16,384
│ │ │ │ └─BatchNorm2d: 5-65 [1, 64, 80, 80] 128
│ │ │ │ └─ReLU: 5-66 [1, 64, 80, 80] --
│ │ │ └─ConvModule: 4-48 [1, 64, 80, 80] --
│ │ │ │ └─Conv2d: 5-67 [1, 64, 80, 80] 16,384
│ │ │ │ └─BatchNorm2d: 5-68 [1, 64, 80, 80] 128
│ │ │ │ └─ReLU: 5-69 [1, 64, 80, 80] --
│ │ │ └─Sequential: 4-49 [1, 64, 80, 80] --
│ │ │ │ └─DarknetBottleneck: 5-70 [1, 64, 80, 80] --
│ │ │ │ │ └─ConvModule: 6-31 [1, 64, 80, 80] --
│ │ │ │ │ │ └─Conv2d: 7-55 [1, 64, 80, 80] 4,096
│ │ │ │ │ │ └─BatchNorm2d: 7-56 [1, 64, 80, 80] 128
│ │ │ │ │ │ └─ReLU: 7-57 [1, 64, 80, 80] --
│ │ │ │ │ └─ConvModule: 6-32 [1, 64, 80, 80] --
│ │ │ │ │ │ └─Conv2d: 7-58 [1, 64, 80, 80] 36,864
│ │ │ │ │ │ └─BatchNorm2d: 7-59 [1, 64, 80, 80] 128
│ │ │ │ │ │ └─ReLU: 7-60 [1, 64, 80, 80] --
│ │ │ └─ConvModule: 4-50 [1, 128, 80, 80] --
│ │ │ │ └─Conv2d: 5-71 [1, 128, 80, 80] 16,384
│ │ │ │ └─BatchNorm2d: 5-72 [1, 128, 80, 80] 256
│ │ │ │ └─ReLU: 5-73 [1, 128, 80, 80] --
│ └─ModuleList: 2-14 -- (recursive)
│ │ └─ConvModule: 3-16 [1, 128, 40, 40] --
│ │ │ └─Conv2d: 4-51 [1, 128, 40, 40] 147,456
│ │ │ └─BatchNorm2d: 4-52 [1, 128, 40, 40] 256
│ │ │ └─ReLU: 4-53 [1, 128, 40, 40] --
│ └─ModuleList: 2-15 -- (recursive)
│ │ └─CSPLayer: 3-17 [1, 256, 40, 40] --
│ │ │ └─ConvModule: 4-54 [1, 128, 40, 40] --
│ │ │ │ └─Conv2d: 5-74 [1, 128, 40, 40] 32,768
│ │ │ │ └─BatchNorm2d: 5-75 [1, 128, 40, 40] 256
│ │ │ │ └─ReLU: 5-76 [1, 128, 40, 40] --
│ │ │ └─ConvModule: 4-55 [1, 128, 40, 40] --
│ │ │ │ └─Conv2d: 5-77 [1, 128, 40, 40] 32,768
│ │ │ │ └─BatchNorm2d: 5-78 [1, 128, 40, 40] 256
│ │ │ │ └─ReLU: 5-79 [1, 128, 40, 40] --
│ │ │ └─Sequential: 4-56 [1, 128, 40, 40] --
│ │ │ │ └─DarknetBottleneck: 5-80 [1, 128, 40, 40] --
│ │ │ │ │ └─ConvModule: 6-33 [1, 128, 40, 40] --
│ │ │ │ │ │ └─Conv2d: 7-61 [1, 128, 40, 40] 16,384
│ │ │ │ │ │ └─BatchNorm2d: 7-62 [1, 128, 40, 40] 256
│ │ │ │ │ │ └─ReLU: 7-63 [1, 128, 40, 40] --
│ │ │ │ │ └─ConvModule: 6-34 [1, 128, 40, 40] --
│ │ │ │ │ │ └─Conv2d: 7-64 [1, 128, 40, 40] 147,456
│ │ │ │ │ │ └─BatchNorm2d: 7-65 [1, 128, 40, 40] 256
│ │ │ │ │ │ └─ReLU: 7-66 [1, 128, 40, 40] --
│ │ │ └─ConvModule: 4-57 [1, 256, 40, 40] --
│ │ │ │ └─Conv2d: 5-81 [1, 256, 40, 40] 65,536
│ │ │ │ └─BatchNorm2d: 5-82 [1, 256, 40, 40] 512
│ │ │ │ └─ReLU: 5-83 [1, 256, 40, 40] --
│ └─ModuleList: 2-14 -- (recursive)
│ │ └─ConvModule: 3-18 [1, 256, 20, 20] --
│ │ │ └─Conv2d: 4-58 [1, 256, 20, 20] 589,824
│ │ │ └─BatchNorm2d: 4-59 [1, 256, 20, 20] 512
│ │ │ └─ReLU: 4-60 [1, 256, 20, 20] --
│ └─ModuleList: 2-15 -- (recursive)
│ │ └─CSPLayer: 3-19 [1, 512, 20, 20] --
│ │ │ └─ConvModule: 4-61 [1, 256, 20, 20] --
│ │ │ │ └─Conv2d: 5-84 [1, 256, 20, 20] 131,072
│ │ │ │ └─BatchNorm2d: 5-85 [1, 256, 20, 20] 512
│ │ │ │ └─ReLU: 5-86 [1, 256, 20, 20] --
│ │ │ └─ConvModule: 4-62 [1, 256, 20, 20] --
│ │ │ │ └─Conv2d: 5-87 [1, 256, 20, 20] 131,072
│ │ │ │ └─BatchNorm2d: 5-88 [1, 256, 20, 20] 512
│ │ │ │ └─ReLU: 5-89 [1, 256, 20, 20] --
│ │ │ └─Sequential: 4-63 [1, 256, 20, 20] --
│ │ │ │ └─DarknetBottleneck: 5-90 [1, 256, 20, 20] --
│ │ │ │ │ └─ConvModule: 6-35 [1, 256, 20, 20] --
│ │ │ │ │ │ └─Conv2d: 7-67 [1, 256, 20, 20] 65,536
│ │ │ │ │ │ └─BatchNorm2d: 7-68 [1, 256, 20, 20] 512
│ │ │ │ │ │ └─ReLU: 7-69 [1, 256, 20, 20] --
│ │ │ │ │ └─ConvModule: 6-36 [1, 256, 20, 20] --
│ │ │ │ │ │ └─Conv2d: 7-70 [1, 256, 20, 20] 589,824
│ │ │ │ │ │ └─BatchNorm2d: 7-71 [1, 256, 20, 20] 512
│ │ │ │ │ │ └─ReLU: 7-72 [1, 256, 20, 20] --
│ │ │ └─ConvModule: 4-64 [1, 512, 20, 20] --
│ │ │ │ └─Conv2d: 5-91 [1, 512, 20, 20] 262,144
│ │ │ │ └─BatchNorm2d: 5-92 [1, 512, 20, 20] 1,024
│ │ │ │ └─ReLU: 5-93 [1, 512, 20, 20] --
│ └─ModuleList: 2-16 -- --
│ │ └─ConvModule: 3-20 [1, 128, 80, 80] --
│ │ │ └─Conv2d: 4-65 [1, 128, 80, 80] 16,384
│ │ │ └─BatchNorm2d: 4-66 [1, 128, 80, 80] 256
│ │ │ └─ReLU: 4-67 [1, 128, 80, 80] --
│ │ └─ConvModule: 3-21 [1, 128, 40, 40] --
│ │ │ └─Conv2d: 4-68 [1, 128, 40, 40] 32,768
│ │ │ └─BatchNorm2d: 4-69 [1, 128, 40, 40] 256
│ │ │ └─ReLU: 4-70 [1, 128, 40, 40] --
│ │ └─ConvModule: 3-22 [1, 128, 20, 20] --
│ │ │ └─Conv2d: 4-71 [1, 128, 20, 20] 65,536
│ │ │ └─BatchNorm2d: 4-72 [1, 128, 20, 20] 256
│ │ │ └─ReLU: 4-73 [1, 128, 20, 20] --
├─YOLOXHead: 1-3 [1, 5, 80, 80] --
│ └─ModuleList: 2-27 -- (recursive)
│ │ └─Sequential: 3-23 [1, 128, 80, 80] --
│ │ │ └─ConvModule: 4-74 [1, 128, 80, 80] --
│ │ │ │ └─Conv2d: 5-94 [1, 128, 80, 80] 147,456
│ │ │ │ └─BatchNorm2d: 5-95 [1, 128, 80, 80] 256
│ │ │ │ └─ReLU: 5-96 [1, 128, 80, 80] --
│ │ │ └─ConvModule: 4-75 [1, 128, 80, 80] --
│ │ │ │ └─Conv2d: 5-97 [1, 128, 80, 80] 147,456
│ │ │ │ └─BatchNorm2d: 5-98 [1, 128, 80, 80] 256
│ │ │ │ └─ReLU: 5-99 [1, 128, 80, 80] --
│ └─ModuleList: 2-28 -- (recursive)
│ │ └─Sequential: 3-24 [1, 128, 80, 80] --
│ │ │ └─ConvModule: 4-76 [1, 128, 80, 80] --
│ │ │ │ └─Conv2d: 5-100 [1, 128, 80, 80] 147,456
│ │ │ │ └─BatchNorm2d: 5-101 [1, 128, 80, 80] 256
│ │ │ │ └─ReLU: 5-102 [1, 128, 80, 80] --
│ │ │ └─ConvModule: 4-77 [1, 128, 80, 80] --
│ │ │ │ └─Conv2d: 5-103 [1, 128, 80, 80] 147,456
│ │ │ │ └─BatchNorm2d: 5-104 [1, 128, 80, 80] 256
│ │ │ │ └─ReLU: 5-105 [1, 128, 80, 80] --
│ └─ModuleList: 2-29 -- (recursive)
│ │ └─Conv2d: 3-25 [1, 5, 80, 80] 645
│ └─ModuleList: 2-30 -- (recursive)
│ │ └─Conv2d: 3-26 [1, 4, 80, 80] 516
│ └─ModuleList: 2-31 -- (recursive)
│ │ └─Conv2d: 3-27 [1, 1, 80, 80] 129
│ └─ModuleList: 2-27 -- (recursive)
│ │ └─Sequential: 3-28 [1, 128, 40, 40] --
│ │ │ └─ConvModule: 4-78 [1, 128, 40, 40] --
│ │ │ │ └─Conv2d: 5-106 [1, 128, 40, 40] 147,456
│ │ │ │ └─BatchNorm2d: 5-107 [1, 128, 40, 40] 256
│ │ │ │ └─ReLU: 5-108 [1, 128, 40, 40] --
│ │ │ └─ConvModule: 4-79 [1, 128, 40, 40] --
│ │ │ │ └─Conv2d: 5-109 [1, 128, 40, 40] 147,456
│ │ │ │ └─BatchNorm2d: 5-110 [1, 128, 40, 40] 256
│ │ │ │ └─ReLU: 5-111 [1, 128, 40, 40] --
│ └─ModuleList: 2-28 -- (recursive)
│ │ └─Sequential: 3-29 [1, 128, 40, 40] --
│ │ │ └─ConvModule: 4-80 [1, 128, 40, 40] --
│ │ │ │ └─Conv2d: 5-112 [1, 128, 40, 40] 147,456
│ │ │ │ └─BatchNorm2d: 5-113 [1, 128, 40, 40] 256
│ │ │ │ └─ReLU: 5-114 [1, 128, 40, 40] --
│ │ │ └─ConvModule: 4-81 [1, 128, 40, 40] --
│ │ │ │ └─Conv2d: 5-115 [1, 128, 40, 40] 147,456
│ │ │ │ └─BatchNorm2d: 5-116 [1, 128, 40, 40] 256
│ │ │ │ └─ReLU: 5-117 [1, 128, 40, 40] --
│ └─ModuleList: 2-29 -- (recursive)
│ │ └─Conv2d: 3-30 [1, 5, 40, 40] 645
│ └─ModuleList: 2-30 -- (recursive)
│ │ └─Conv2d: 3-31 [1, 4, 40, 40] 516
│ └─ModuleList: 2-31 -- (recursive)
│ │ └─Conv2d: 3-32 [1, 1, 40, 40] 129
│ └─ModuleList: 2-27 -- (recursive)
│ │ └─Sequential: 3-33 [1, 128, 20, 20] --
│ │ │ └─ConvModule: 4-82 [1, 128, 20, 20] --
│ │ │ │ └─Conv2d: 5-118 [1, 128, 20, 20] 147,456
│ │ │ │ └─BatchNorm2d: 5-119 [1, 128, 20, 20] 256
│ │ │ │ └─ReLU: 5-120 [1, 128, 20, 20] --
│ │ │ └─ConvModule: 4-83 [1, 128, 20, 20] --
│ │ │ │ └─Conv2d: 5-121 [1, 128, 20, 20] 147,456
│ │ │ │ └─BatchNorm2d: 5-122 [1, 128, 20, 20] 256
│ │ │ │ └─ReLU: 5-123 [1, 128, 20, 20] --
│ └─ModuleList: 2-28 -- (recursive)
│ │ └─Sequential: 3-34 [1, 128, 20, 20] --
│ │ │ └─ConvModule: 4-84 [1, 128, 20, 20] --
│ │ │ │ └─Conv2d: 5-124 [1, 128, 20, 20] 147,456
│ │ │ │ └─BatchNorm2d: 5-125 [1, 128, 20, 20] 256
│ │ │ │ └─ReLU: 5-126 [1, 128, 20, 20] --
│ │ │ └─ConvModule: 4-85 [1, 128, 20, 20] --
│ │ │ │ └─Conv2d: 5-127 [1, 128, 20, 20] 147,456
│ │ │ │ └─BatchNorm2d: 5-128 [1, 128, 20, 20] 256
│ │ │ │ └─ReLU: 5-129 [1, 128, 20, 20] --
│ └─ModuleList: 2-29 -- (recursive)
│ │ └─Conv2d: 3-35 [1, 5, 20, 20] 645
│ └─ModuleList: 2-30 -- (recursive)
│ │ └─Conv2d: 3-36 [1, 4, 20, 20] 516
│ └─ModuleList: 2-31 -- (recursive)
│ │ └─Conv2d: 3-37 [1, 1, 20, 20] 129
=========================================================================================================
Total params: 8,939,578
Trainable params: 8,939,578
Non-trainable params: 0
Total mult-adds (G): 13.30
=========================================================================================================
Input size (MB): 4.92
Forward/backward pass size (MB): 497.93
Params size (MB): 35.76
Estimated Total Size (MB): 538.60
=========================================================================================================
2024-11-25 16:21:53,415 - mmdet - INFO -
2024-11-25 16:21:53,445 - mmdet - INFO - load checkpoint from local path: ./data/downloads/pretrained/yolox_s_lite/yolox_s_lite_640x640_20220221_checkpoint.pth
2024-11-25 16:21:56,097 - mmdet - WARNING - The model and loaded state dict do not match exactly
size mismatch for bbox_head.multi_level_conv_cls.0.weight: copying a param with shape torch.Size([80, 128, 1, 1]) from checkpoint, the shape in current model is torch.Size([5, 128, 1, 1]).
size mismatch for bbox_head.multi_level_conv_cls.0.bias: copying a param with shape torch.Size([80]) from checkpoint, the shape in current model is torch.Size([5]).
size mismatch for bbox_head.multi_level_conv_cls.1.weight: copying a param with shape torch.Size([80, 128, 1, 1]) from checkpoint, the shape in current model is torch.Size([5, 128, 1, 1]).
size mismatch for bbox_head.multi_level_conv_cls.1.bias: copying a param with shape torch.Size([80]) from checkpoint, the shape in current model is torch.Size([5]).
size mismatch for bbox_head.multi_level_conv_cls.2.weight: copying a param with shape torch.Size([80, 128, 1, 1]) from checkpoint, the shape in current model is torch.Size([5, 128, 1, 1]).
size mismatch for bbox_head.multi_level_conv_cls.2.bias: copying a param with shape torch.Size([80]) from checkpoint, the shape in current model is torch.Size([5]).
unexpected key in source state_dict: ema_backbone_stem_conv_in_conv_weight, ema_backbone_stem_conv_in_bn_weight, ema_backbone_stem_conv_in_bn_bias, ema_backbone_stem_conv_in_bn_running_mean, ema_backbone_stem_conv_in_bn_running_var, ema_backbone_stem_conv_in_bn_num_batches_tracked, ema_backbone_stem_conv_conv_weight, ema_backbone_stem_conv_bn_weight, ema_backbone_stem_conv_bn_bias, ema_backbone_stem_conv_bn_running_mean, ema_backbone_stem_conv_bn_running_var, ema_backbone_stem_conv_bn_num_batches_tracked, ema_backbone_stage1_0_conv_weight, ema_backbone_stage1_0_bn_weight, ema_backbone_stage1_0_bn_bias, ema_backbone_stage1_0_bn_running_mean, ema_backbone_stage1_0_bn_running_var, ema_backbone_stage1_0_bn_num_batches_tracked, ema_backbone_stage1_1_main_conv_conv_weight, ema_backbone_stage1_1_main_conv_bn_weight, ema_backbone_stage1_1_main_conv_bn_bias, ema_backbone_stage1_1_main_conv_bn_running_mean, ema_backbone_stage1_1_main_conv_bn_running_var, ema_backbone_stage1_1_main_conv_bn_num_batches_tracked, ema_backbone_stage1_1_short_conv_conv_weight, ema_backbone_stage1_1_short_conv_bn_weight, ema_backbone_stage1_1_short_conv_bn_bias, ema_backbone_stage1_1_short_conv_bn_running_mean, ema_backbone_stage1_1_short_conv_bn_running_var, ema_backbone_stage1_1_short_conv_bn_num_batches_tracked, ema_backbone_stage1_1_final_conv_conv_weight, ema_backbone_stage1_1_final_conv_bn_weight, ema_backbone_stage1_1_final_conv_bn_bias, ema_backbone_stage1_1_final_conv_bn_running_mean, ema_backbone_stage1_1_final_conv_bn_running_var, ema_backbone_stage1_1_final_conv_bn_num_batches_tracked, ema_backbone_stage1_1_blocks_0_conv1_conv_weight, ema_backbone_stage1_1_blocks_0_conv1_bn_weight, ema_backbone_stage1_1_blocks_0_conv1_bn_bias, ema_backbone_stage1_1_blocks_0_conv1_bn_running_mean, ema_backbone_stage1_1_blocks_0_conv1_bn_running_var, ema_backbone_stage1_1_blocks_0_conv1_bn_num_batches_tracked, ema_backbone_stage1_1_blocks_0_conv2_conv_weight, ema_backbone_stage1_1_blocks_0_conv2_bn_weight, ema_backbone_stage1_1_blocks_0_conv2_bn_bias, ema_backbone_stage1_1_blocks_0_conv2_bn_running_mean, ema_backbone_stage1_1_blocks_0_conv2_bn_running_var, ema_backbone_stage1_1_blocks_0_conv2_bn_num_batches_tracked, ema_backbone_stage2_0_conv_weight, ema_backbone_stage2_0_bn_weight, ema_backbone_stage2_0_bn_bias, ema_backbone_stage2_0_bn_running_mean, ema_backbone_stage2_0_bn_running_var, ema_backbone_stage2_0_bn_num_batches_tracked, ema_backbone_stage2_1_main_conv_conv_weight, ema_backbone_stage2_1_main_conv_bn_weight, ema_backbone_stage2_1_main_conv_bn_bias, ema_backbone_stage2_1_main_conv_bn_running_mean, ema_backbone_stage2_1_main_conv_bn_running_var, ema_backbone_stage2_1_main_conv_bn_num_batches_tracked, ema_backbone_stage2_1_short_conv_conv_weight, ema_backbone_stage2_1_short_conv_bn_weight, ema_backbone_stage2_1_short_conv_bn_bias, ema_backbone_stage2_1_short_conv_bn_running_mean, ema_backbone_stage2_1_short_conv_bn_running_var, ema_backbone_stage2_1_short_conv_bn_num_batches_tracked, ema_backbone_stage2_1_final_conv_conv_weight, ema_backbone_stage2_1_final_conv_bn_weight, ema_backbone_stage2_1_final_conv_bn_bias, ema_backbone_stage2_1_final_conv_bn_running_mean, ema_backbone_stage2_1_final_conv_bn_running_var, ema_backbone_stage2_1_final_conv_bn_num_batches_tracked, ema_backbone_stage2_1_blocks_0_conv1_conv_weight, ema_backbone_stage2_1_blocks_0_conv1_bn_weight, ema_backbone_stage2_1_blocks_0_conv1_bn_bias, ema_backbone_stage2_1_blocks_0_conv1_bn_running_mean, ema_backbone_stage2_1_blocks_0_conv1_bn_running_var, ema_backbone_stage2_1_blocks_0_conv1_bn_num_batches_tracked, ema_backbone_stage2_1_blocks_0_conv2_conv_weight, ema_backbone_stage2_1_blocks_0_conv2_bn_weight, ema_backbone_stage2_1_blocks_0_conv2_bn_bias, ema_backbone_stage2_1_blocks_0_conv2_bn_running_mean, ema_backbone_stage2_1_blocks_0_conv2_bn_running_var, ema_backbone_stage2_1_blocks_0_conv2_bn_num_batches_tracked, ema_backbone_stage2_1_blocks_1_conv1_conv_weight, ema_backbone_stage2_1_blocks_1_conv1_bn_weight, ema_backbone_stage2_1_blocks_1_conv1_bn_bias, ema_backbone_stage2_1_blocks_1_conv1_bn_running_mean, ema_backbone_stage2_1_blocks_1_conv1_bn_running_var, ema_backbone_stage2_1_blocks_1_conv1_bn_num_batches_tracked, ema_backbone_stage2_1_blocks_1_conv2_conv_weight, ema_backbone_stage2_1_blocks_1_conv2_bn_weight, ema_backbone_stage2_1_blocks_1_conv2_bn_bias, ema_backbone_stage2_1_blocks_1_conv2_bn_running_mean, ema_backbone_stage2_1_blocks_1_conv2_bn_running_var, ema_backbone_stage2_1_blocks_1_conv2_bn_num_batches_tracked, ema_backbone_stage2_1_blocks_2_conv1_conv_weight, ema_backbone_stage2_1_blocks_2_conv1_bn_weight, ema_backbone_stage2_1_blocks_2_conv1_bn_bias, ema_backbone_stage2_1_blocks_2_conv1_bn_running_mean, ema_backbone_stage2_1_blocks_2_conv1_bn_running_var, ema_backbone_stage2_1_blocks_2_conv1_bn_num_batches_tracked, ema_backbone_stage2_1_blocks_2_conv2_conv_weight, ema_backbone_stage2_1_blocks_2_conv2_bn_weight, ema_backbone_stage2_1_blocks_2_conv2_bn_bias, ema_backbone_stage2_1_blocks_2_conv2_bn_running_mean, ema_backbone_stage2_1_blocks_2_conv2_bn_running_var, ema_backbone_stage2_1_blocks_2_conv2_bn_num_batches_tracked, ema_backbone_stage3_0_conv_weight, ema_backbone_stage3_0_bn_weight, ema_backbone_stage3_0_bn_bias, ema_backbone_stage3_0_bn_running_mean, ema_backbone_stage3_0_bn_running_var, ema_backbone_stage3_0_bn_num_batches_tracked, ema_backbone_stage3_1_main_conv_conv_weight, ema_backbone_stage3_1_main_conv_bn_weight, ema_backbone_stage3_1_main_conv_bn_bias, ema_backbone_stage3_1_main_conv_bn_running_mean, ema_backbone_stage3_1_main_conv_bn_running_var, ema_backbone_stage3_1_main_conv_bn_num_batches_tracked, ema_backbone_stage3_1_short_conv_conv_weight, ema_backbone_stage3_1_short_conv_bn_weight, ema_backbone_stage3_1_short_conv_bn_bias, ema_backbone_stage3_1_short_conv_bn_running_mean, ema_backbone_stage3_1_short_conv_bn_running_var, ema_backbone_stage3_1_short_conv_bn_num_batches_tracked, ema_backbone_stage3_1_final_conv_conv_weight, ema_backbone_stage3_1_final_conv_bn_weight, ema_backbone_stage3_1_final_conv_bn_bias, ema_backbone_stage3_1_final_conv_bn_running_mean, ema_backbone_stage3_1_final_conv_bn_running_var, ema_backbone_stage3_1_final_conv_bn_num_batches_tracked, ema_backbone_stage3_1_blocks_0_conv1_conv_weight, ema_backbone_stage3_1_blocks_0_conv1_bn_weight, ema_backbone_stage3_1_blocks_0_conv1_bn_bias, ema_backbone_stage3_1_blocks_0_conv1_bn_running_mean, ema_backbone_stage3_1_blocks_0_conv1_bn_running_var, ema_backbone_stage3_1_blocks_0_conv1_bn_num_batches_tracked, ema_backbone_stage3_1_blocks_0_conv2_conv_weight, ema_backbone_stage3_1_blocks_0_conv2_bn_weight, ema_backbone_stage3_1_blocks_0_conv2_bn_bias, ema_backbone_stage3_1_blocks_0_conv2_bn_running_mean, ema_backbone_stage3_1_blocks_0_conv2_bn_running_var, ema_backbone_stage3_1_blocks_0_conv2_bn_num_batches_tracked, ema_backbone_stage3_1_blocks_1_conv1_conv_weight, ema_backbone_stage3_1_blocks_1_conv1_bn_weight, ema_backbone_stage3_1_blocks_1_conv1_bn_bias, ema_backbone_stage3_1_blocks_1_conv1_bn_running_mean, ema_backbone_stage3_1_blocks_1_conv1_bn_running_var, ema_backbone_stage3_1_blocks_1_conv1_bn_num_batches_tracked, ema_backbone_stage3_1_blocks_1_conv2_conv_weight, ema_backbone_stage3_1_blocks_1_conv2_bn_weight, ema_backbone_stage3_1_blocks_1_conv2_bn_bias, ema_backbone_stage3_1_blocks_1_conv2_bn_running_mean, ema_backbone_stage3_1_blocks_1_conv2_bn_running_var, ema_backbone_stage3_1_blocks_1_conv2_bn_num_batches_tracked, ema_backbone_stage3_1_blocks_2_conv1_conv_weight, ema_backbone_stage3_1_blocks_2_conv1_bn_weight, ema_backbone_stage3_1_blocks_2_conv1_bn_bias, ema_backbone_stage3_1_blocks_2_conv1_bn_running_mean, ema_backbone_stage3_1_blocks_2_conv1_bn_running_var, ema_backbone_stage3_1_blocks_2_conv1_bn_num_batches_tracked, ema_backbone_stage3_1_blocks_2_conv2_conv_weight, 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2024-11-25 16:21:56,102 - mmdet - INFO - Start running, host: mchi@ubuntu22, work_dir: /home/mchi/github/edgeai-tensorlab/edgeai-modelmaker/data/projects/1021_3280x2464/run/20241125-162141/yolox_s_lite/training
2024-11-25 16:21:56,102 - mmdet - INFO - Hooks will be executed in the following order:
before_run:
(VERY_HIGH ) YOLOXLrUpdaterHook
(49 ) ExpMomentumEMAHook
(NORMAL ) CheckpointHook
(LOW ) EvalHook
(VERY_LOW ) TextLoggerHook
--------------------
before_train_epoch:
(VERY_HIGH ) YOLOXLrUpdaterHook
(48 ) YOLOXModeSwitchHook
(48 ) SyncNormHook
(49 ) ExpMomentumEMAHook
(LOW ) IterTimerHook
(LOW ) EvalHook
(VERY_LOW ) TextLoggerHook
--------------------
before_train_iter:
(VERY_HIGH ) YOLOXLrUpdaterHook
(LOW ) IterTimerHook
(LOW ) EvalHook
--------------------
after_train_iter:
(ABOVE_NORMAL) OptimizerHook
(49 ) ExpMomentumEMAHook
(NORMAL ) CheckpointHook
(LOW ) IterTimerHook
(LOW ) EvalHook
(VERY_LOW ) TextLoggerHook
--------------------
after_train_epoch:
(48 ) SyncNormHook
(49 ) ExpMomentumEMAHook
(NORMAL ) CheckpointHook
(LOW ) EvalHook
(VERY_LOW ) TextLoggerHook
--------------------
before_val_epoch:
(LOW ) IterTimerHook
(VERY_LOW ) TextLoggerHook
--------------------
before_val_iter:
(LOW ) IterTimerHook
--------------------
after_val_iter:
(LOW ) IterTimerHook
--------------------
after_val_epoch:
(VERY_LOW ) TextLoggerHook
--------------------
after_run:
(VERY_LOW ) TextLoggerHook
--------------------
2024-11-25 16:21:56,103 - mmdet - INFO - workflow: [('train', 1)], max: 60 epochs
2024-11-25 16:21:56,120 - mmdet - INFO - Checkpoints will be saved to /home/mchi/github/edgeai-tensorlab/edgeai-modelmaker/data/projects/1021_3280x2464/run/20241125-162141/yolox_s_lite/training by HardDiskBackend.
2024-11-25 16:22:19,129 - mmdet - INFO - Evaluating bbox...
2024-11-25 16:22:19,174 - mmdet - INFO -
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.001
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.000
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.000
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.008
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.008
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.008
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.011
2024-11-25 16:22:19,174 - mmdet - INFO - Exp name: yolox_s_lite.py
2024-11-25 16:22:19,174 - mmdet - INFO - Epoch(val) [1][12] bbox_mAP: 0.0000, bbox_mAP_50: 0.0010, bbox_mAP_75: 0.0000, bbox_mAP_s: 0.0000, bbox_mAP_m: 0.0000, bbox_mAP_l: 0.0000, bbox_mAP_copypaste: 0.000 0.001 0.000 0.000 0.000 0.000
2024-11-25 16:22:44,614 - mmdet - INFO - Evaluating bbox...
2024-11-25 16:22:44,668 - mmdet - INFO -
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.000
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.000
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.000
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.000
2024-11-25 16:22:44,669 - mmdet - INFO - Exp name: yolox_s_lite.py
2024-11-25 16:22:44,669 - mmdet - INFO - Epoch(val) [2][12] bbox_mAP: 0.0000, bbox_mAP_50: 0.0000, bbox_mAP_75: 0.0000, bbox_mAP_s: 0.0000, bbox_mAP_m: 0.0000, bbox_mAP_l: 0.0000, bbox_mAP_copypaste: 0.000 0.000 0.000 0.000 0.000 0.000
2024-11-25 16:23:06,317 - mmdet - INFO - Evaluating bbox...
2024-11-25 16:23:06,491 - mmdet - INFO -
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.006
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.001
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.000
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.003
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.051
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.054
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.054
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.078
2024-11-25 16:23:06,492 - mmdet - INFO - Exp name: yolox_s_lite.py
2024-11-25 16:23:06,492 - mmdet - INFO - Epoch(val) [3][12] bbox_mAP: 0.0020, bbox_mAP_50: 0.0060, bbox_mAP_75: 0.0010, bbox_mAP_s: 0.0000, bbox_mAP_m: 0.0000, bbox_mAP_l: 0.0030, bbox_mAP_copypaste: 0.002 0.006 0.001 0.000 0.000 0.003
2024-11-25 16:23:23,938 - mmdet - INFO - Evaluating bbox...
2024-11-25 16:23:24,183 - mmdet - INFO -
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.008
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.000
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.000
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.002
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.069
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.077
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.077
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.111
2024-11-25 16:23:24,185 - mmdet - INFO - Exp name: yolox_s_lite.py
2024-11-25 16:23:24,185 - mmdet - INFO - Epoch(val) [4][12] bbox_mAP: 0.0020, bbox_mAP_50: 0.0080, bbox_mAP_75: 0.0000, bbox_mAP_s: 0.0000, bbox_mAP_m: 0.0000, bbox_mAP_l: 0.0020, bbox_mAP_copypaste: 0.002 0.008 0.000 0.000 0.000 0.002
2024-11-25 16:23:39,517 - mmdet - INFO - Evaluating bbox...
2024-11-25 16:23:39,667 - mmdet - INFO -
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.010
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.049
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.000
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.000
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.014
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.100
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.108
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.108
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.156
2024-11-25 16:23:39,668 - mmdet - INFO - Exp name: yolox_s_lite.py
2024-11-25 16:23:39,668 - mmdet - INFO - Epoch(val) [5][12] bbox_mAP: 0.0100, bbox_mAP_50: 0.0490, bbox_mAP_75: 0.0000, bbox_mAP_s: 0.0000, bbox_mAP_m: 0.0000, bbox_mAP_l: 0.0140, bbox_mAP_copypaste: 0.010 0.049 0.000 0.000 0.000 0.014
2024-11-25 16:24:13,703 - mmdet - INFO - Evaluating bbox...
2024-11-25 16:24:14,454 - mmdet - INFO -
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.003
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.022
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.000
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.000
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.004
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.067
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.069
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.069
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.100
2024-11-25 16:24:14,458 - mmdet - INFO - Exp name: yolox_s_lite.py
2024-11-25 16:24:14,458 - mmdet - INFO - Epoch(val) [6][12] bbox_mAP: 0.0030, bbox_mAP_50: 0.0220, bbox_mAP_75: 0.0000, bbox_mAP_s: 0.0000, bbox_mAP_m: 0.0000, bbox_mAP_l: 0.0040, bbox_mAP_copypaste: 0.003 0.022 0.000 0.000 0.000 0.004
2024-11-25 16:24:46,333 - mmdet - INFO - Evaluating bbox...
2024-11-25 16:24:46,645 - mmdet - INFO -
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.008
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.063
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.000
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.000
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.011
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.085
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.092
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.095
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.137
2024-11-25 16:24:46,647 - mmdet - INFO - Exp name: yolox_s_lite.py
2024-11-25 16:24:46,647 - mmdet - INFO - Epoch(val) [7][12] bbox_mAP: 0.0080, bbox_mAP_50: 0.0630, bbox_mAP_75: 0.0000, bbox_mAP_s: 0.0000, bbox_mAP_m: 0.0000, bbox_mAP_l: 0.0110, bbox_mAP_copypaste: 0.008 0.063 0.000 0.000 0.000 0.011
2024-11-25 16:25:15,324 - mmdet - INFO - Evaluating bbox...
2024-11-25 16:25:15,705 - mmdet - INFO -
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.010
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.071
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.000
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.000
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.013
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.079
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.082
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.082
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.119
2024-11-25 16:25:15,708 - mmdet - INFO - Exp name: yolox_s_lite.py
2024-11-25 16:25:15,708 - mmdet - INFO - Epoch(val) [8][12] bbox_mAP: 0.0100, bbox_mAP_50: 0.0710, bbox_mAP_75: 0.0000, bbox_mAP_s: 0.0000, bbox_mAP_m: 0.0000, bbox_mAP_l: 0.0130, bbox_mAP_copypaste: 0.010 0.071 0.000 0.000 0.000 0.013
2024-11-25 16:25:42,918 - mmdet - INFO - Evaluating bbox...
2024-11-25 16:25:43,362 - mmdet - INFO -
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.003
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.022
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.000
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.000
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.005
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.118
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.164
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.164
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.237
2024-11-25 16:25:43,364 - mmdet - INFO - Exp name: yolox_s_lite.py
2024-11-25 16:25:43,364 - mmdet - INFO - Epoch(val) [9][12] bbox_mAP: 0.0030, bbox_mAP_50: 0.0220, bbox_mAP_75: 0.0000, bbox_mAP_s: 0.0000, bbox_mAP_m: 0.0000, bbox_mAP_l: 0.0050, bbox_mAP_copypaste: 0.003 0.022 0.000 0.000 0.000 0.005
2024-11-25 16:26:07,550 - mmdet - INFO - Saving checkpoint at 10 epochs
2024-11-25 16:26:09,462 - mmdet - INFO - Evaluating bbox...
2024-11-25 16:26:09,701 - mmdet - INFO -
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.007
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.039
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.001
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.001
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.011
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.154
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.213
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.213
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.055
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.285
2024-11-25 16:26:09,703 - mmdet - INFO - Exp name: yolox_s_lite.py
2024-11-25 16:26:09,703 - mmdet - INFO - Epoch(val) [10][12] bbox_mAP: 0.0070, bbox_mAP_50: 0.0390, bbox_mAP_75: 0.0010, bbox_mAP_s: 0.0000, bbox_mAP_m: 0.0010, bbox_mAP_l: 0.0110, bbox_mAP_copypaste: 0.007 0.039 0.001 0.000 0.001 0.011
2024-11-25 16:26:41,620 - mmdet - INFO - Evaluating bbox...
2024-11-25 16:26:41,823 - mmdet - INFO -
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.026
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.133
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.004
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.003
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.038
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.203
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.233
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.233
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.064
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.311
2024-11-25 16:26:41,825 - mmdet - INFO - Exp name: yolox_s_lite.py
2024-11-25 16:26:41,825 - mmdet - INFO - Epoch(val) [11][12] bbox_mAP: 0.0260, bbox_mAP_50: 0.1330, bbox_mAP_75: 0.0040, bbox_mAP_s: 0.0000, bbox_mAP_m: 0.0030, bbox_mAP_l: 0.0380, bbox_mAP_copypaste: 0.026 0.133 0.004 0.000 0.003 0.038
2024-11-25 16:27:08,445 - mmdet - INFO - Evaluating bbox...
2024-11-25 16:27:08,683 - mmdet - INFO -
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.011
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.054
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.000
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.001
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.017
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.138
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.177
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.179
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.055
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.237
2024-11-25 16:27:08,685 - mmdet - INFO - Exp name: yolox_s_lite.py
2024-11-25 16:27:08,685 - mmdet - INFO - Epoch(val) [12][12] bbox_mAP: 0.0110, bbox_mAP_50: 0.0540, bbox_mAP_75: 0.0000, bbox_mAP_s: 0.0000, bbox_mAP_m: 0.0010, bbox_mAP_l: 0.0170, bbox_mAP_copypaste: 0.011 0.054 0.000 0.000 0.001 0.017
2024-11-25 16:27:23,572 - mmdet - INFO - Evaluating bbox...
2024-11-25 16:27:23,847 - mmdet - INFO -
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.006
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.034
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.000
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.002
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.010
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.156
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.192
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.192
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.064
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.252
2024-11-25 16:27:23,849 - mmdet - INFO - Exp name: yolox_s_lite.py
2024-11-25 16:27:23,849 - mmdet - INFO - Epoch(val) [13][12] bbox_mAP: 0.0060, bbox_mAP_50: 0.0340, bbox_mAP_75: 0.0000, bbox_mAP_s: 0.0000, bbox_mAP_m: 0.0020, bbox_mAP_l: 0.0100, bbox_mAP_copypaste: 0.006 0.034 0.000 0.000 0.002 0.010
2024-11-25 16:27:47,913 - mmdet - INFO - Evaluating bbox...
2024-11-25 16:27:47,961 - mmdet - INFO -
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.048
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.200
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.005
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.017
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.065
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.267
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.267
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.267
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.055
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.363
2024-11-25 16:27:47,962 - mmdet - INFO - Exp name: yolox_s_lite.py
2024-11-25 16:27:47,962 - mmdet - INFO - Epoch(val) [14][12] bbox_mAP: 0.0480, bbox_mAP_50: 0.2000, bbox_mAP_75: 0.0050, bbox_mAP_s: 0.0000, bbox_mAP_m: 0.0170, bbox_mAP_l: 0.0650, bbox_mAP_copypaste: 0.048 0.200 0.005 0.000 0.017 0.065
2024-11-25 16:28:16,299 - mmdet - INFO - Evaluating bbox...
2024-11-25 16:28:16,383 - mmdet - INFO -
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.039
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.187
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.002
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.023
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.055
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.208
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.208
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.208
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.045
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.281
2024-11-25 16:28:16,384 - mmdet - INFO - Exp name: yolox_s_lite.py
2024-11-25 16:28:16,384 - mmdet - INFO - Epoch(val) [15][12] bbox_mAP: 0.0390, bbox_mAP_50: 0.1870, bbox_mAP_75: 0.0020, bbox_mAP_s: 0.0000, bbox_mAP_m: 0.0230, bbox_mAP_l: 0.0550, bbox_mAP_copypaste: 0.039 0.187 0.002 0.000 0.023 0.055
2024-11-25 16:28:49,252 - mmdet - INFO - Evaluating bbox...
2024-11-25 16:28:49,316 - mmdet - INFO -
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.024
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.140
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.000
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.012
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.033
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.185
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.185
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.185
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.064
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.241
2024-11-25 16:28:49,317 - mmdet - INFO - Exp name: yolox_s_lite.py
2024-11-25 16:28:49,317 - mmdet - INFO - Epoch(val) [16][12] bbox_mAP: 0.0240, bbox_mAP_50: 0.1400, bbox_mAP_75: 0.0000, bbox_mAP_s: 0.0000, bbox_mAP_m: 0.0120, bbox_mAP_l: 0.0330, bbox_mAP_copypaste: 0.024 0.140 0.000 0.000 0.012 0.033
2024-11-25 16:29:19,318 - mmdet - INFO - Evaluating bbox...
2024-11-25 16:29:19,449 - mmdet - INFO -
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.043
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.219
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.001
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.013
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.068
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.251
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.264
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.264
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.118
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.333
2024-11-25 16:29:19,450 - mmdet - INFO - Exp name: yolox_s_lite.py
2024-11-25 16:29:19,451 - mmdet - INFO - Epoch(val) [17][12] bbox_mAP: 0.0430, bbox_mAP_50: 0.2190, bbox_mAP_75: 0.0010, bbox_mAP_s: 0.0000, bbox_mAP_m: 0.0130, bbox_mAP_l: 0.0680, bbox_mAP_copypaste: 0.043 0.219 0.001 0.000 0.013 0.068
2024-11-25 16:29:44,961 - mmdet - INFO - Evaluating bbox...
2024-11-25 16:29:45,089 - mmdet - INFO -
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.072
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.266
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.003
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.007
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.102
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.264
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.272
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.272
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.055
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.370
2024-11-25 16:29:45,090 - mmdet - INFO - Exp name: yolox_s_lite.py
2024-11-25 16:29:45,090 - mmdet - INFO - Epoch(val) [18][12] bbox_mAP: 0.0720, bbox_mAP_50: 0.2660, bbox_mAP_75: 0.0030, bbox_mAP_s: 0.0000, bbox_mAP_m: 0.0070, bbox_mAP_l: 0.1020, bbox_mAP_copypaste: 0.072 0.266 0.003 0.000 0.007 0.102
2024-11-25 16:30:04,811 - mmdet - INFO - Evaluating bbox...
2024-11-25 16:30:05,049 - mmdet - INFO -
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.022
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.093
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.003
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.006
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.035
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.259
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.287
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.287
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.045
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.396
2024-11-25 16:30:05,051 - mmdet - INFO - Exp name: yolox_s_lite.py
2024-11-25 16:30:05,051 - mmdet - INFO - Epoch(val) [19][12] bbox_mAP: 0.0220, bbox_mAP_50: 0.0930, bbox_mAP_75: 0.0030, bbox_mAP_s: 0.0000, bbox_mAP_m: 0.0060, bbox_mAP_l: 0.0350, bbox_mAP_copypaste: 0.022 0.093 0.003 0.000 0.006 0.035
2024-11-25 16:30:20,493 - mmdet - INFO - Saving checkpoint at 20 epochs
2024-11-25 16:30:22,277 - mmdet - INFO - Evaluating bbox...
2024-11-25 16:30:22,472 - mmdet - INFO -
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.015
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.061
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.001
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.011
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.024
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.226
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.277
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.279
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.073
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.374
2024-11-25 16:30:22,473 - mmdet - INFO - Exp name: yolox_s_lite.py
2024-11-25 16:30:22,473 - mmdet - INFO - Epoch(val) [20][12] bbox_mAP: 0.0150, bbox_mAP_50: 0.0610, bbox_mAP_75: 0.0010, bbox_mAP_s: 0.0000, bbox_mAP_m: 0.0110, bbox_mAP_l: 0.0240, bbox_mAP_copypaste: 0.015 0.061 0.001 0.000 0.011 0.024
2024-11-25 16:30:42,988 - mmdet - INFO - Evaluating bbox...
2024-11-25 16:30:43,096 - mmdet - INFO -
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.042
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.197
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.002
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.011
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.065
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.272
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.274
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.274
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.109
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.352
2024-11-25 16:30:43,096 - mmdet - INFO - Exp name: yolox_s_lite.py
2024-11-25 16:30:43,097 - mmdet - INFO - Epoch(val) [21][12] bbox_mAP: 0.0420, bbox_mAP_50: 0.1970, bbox_mAP_75: 0.0020, bbox_mAP_s: 0.0000, bbox_mAP_m: 0.0110, bbox_mAP_l: 0.0650, bbox_mAP_copypaste: 0.042 0.197 0.002 0.000 0.011 0.065
2024-11-25 16:31:02,508 - mmdet - INFO - Evaluating bbox...
2024-11-25 16:31:02,712 - mmdet - INFO -
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.025
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.129
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.001
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.000
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.037
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.164
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.190
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.190
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.009
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.270
2024-11-25 16:31:02,713 - mmdet - INFO - Exp name: yolox_s_lite.py
2024-11-25 16:31:02,713 - mmdet - INFO - Epoch(val) [22][12] bbox_mAP: 0.0250, bbox_mAP_50: 0.1290, bbox_mAP_75: 0.0010, bbox_mAP_s: 0.0000, bbox_mAP_m: 0.0000, bbox_mAP_l: 0.0370, bbox_mAP_copypaste: 0.025 0.129 0.001 0.000 0.000 0.037
2024-11-25 16:31:20,233 - mmdet - INFO - Evaluating bbox...
2024-11-25 16:31:20,365 - mmdet - INFO -
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.061
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.218
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.023
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.022
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.088
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.241
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.254
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.254
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.109
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.322
2024-11-25 16:31:20,366 - mmdet - INFO - Exp name: yolox_s_lite.py
2024-11-25 16:31:20,366 - mmdet - INFO - Epoch(val) [23][12] bbox_mAP: 0.0610, bbox_mAP_50: 0.2180, bbox_mAP_75: 0.0230, bbox_mAP_s: 0.0000, bbox_mAP_m: 0.0220, bbox_mAP_l: 0.0880, bbox_mAP_copypaste: 0.061 0.218 0.023 0.000 0.022 0.088
2024-11-25 16:31:41,916 - mmdet - INFO - Evaluating bbox...
2024-11-25 16:31:42,048 - mmdet - INFO -
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.112
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.359
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.042
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.041
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.147
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.285
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.297
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.297
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.064
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.404
2024-11-25 16:31:42,049 - mmdet - INFO - Exp name: yolox_s_lite.py
2024-11-25 16:31:42,049 - mmdet - INFO - Epoch(val) [24][12] bbox_mAP: 0.1120, bbox_mAP_50: 0.3590, bbox_mAP_75: 0.0420, bbox_mAP_s: 0.0000, bbox_mAP_m: 0.0410, bbox_mAP_l: 0.1470, bbox_mAP_copypaste: 0.112 0.359 0.042 0.000 0.041 0.147
2024-11-25 16:32:04,155 - mmdet - INFO - Evaluating bbox...
2024-11-25 16:32:04,278 - mmdet - INFO -
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.097
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.273
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.041
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.028
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.131
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.233
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.233
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.233
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.055
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.315
2024-11-25 16:32:04,279 - mmdet - INFO - Exp name: yolox_s_lite.py
2024-11-25 16:32:04,280 - mmdet - INFO - Epoch(val) [25][12] bbox_mAP: 0.0970, bbox_mAP_50: 0.2730, bbox_mAP_75: 0.0410, bbox_mAP_s: 0.0000, bbox_mAP_m: 0.0280, bbox_mAP_l: 0.1310, bbox_mAP_copypaste: 0.097 0.273 0.041 0.000 0.028 0.131
2024-11-25 16:32:21,399 - mmdet - INFO - Evaluating bbox...
2024-11-25 16:32:21,622 - mmdet - INFO -
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.072
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.298
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.003
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.047
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.090
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.259
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.262
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.262
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.109
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.333
2024-11-25 16:32:21,623 - mmdet - INFO - Exp name: yolox_s_lite.py
2024-11-25 16:32:21,624 - mmdet - INFO - Epoch(val) [26][12] bbox_mAP: 0.0720, bbox_mAP_50: 0.2980, bbox_mAP_75: 0.0030, bbox_mAP_s: 0.0000, bbox_mAP_m: 0.0470, bbox_mAP_l: 0.0900, bbox_mAP_copypaste: 0.072 0.298 0.003 0.000 0.047 0.090
2024-11-25 16:32:42,458 - mmdet - INFO - Evaluating bbox...
2024-11-25 16:32:42,575 - mmdet - INFO -
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.071
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.323
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.006
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.035
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.099
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.277
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.287
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.287
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.173
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.344
2024-11-25 16:32:42,576 - mmdet - INFO - Exp name: yolox_s_lite.py
2024-11-25 16:32:42,576 - mmdet - INFO - Epoch(val) [27][12] bbox_mAP: 0.0710, bbox_mAP_50: 0.3230, bbox_mAP_75: 0.0060, bbox_mAP_s: 0.0000, bbox_mAP_m: 0.0350, bbox_mAP_l: 0.0990, bbox_mAP_copypaste: 0.071 0.323 0.006 0.000 0.035 0.099
2024-11-25 16:33:21,450 - mmdet - INFO - Evaluating bbox...
2024-11-25 16:33:21,520 - mmdet - INFO -
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.071
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.307
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.009
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.027
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.096
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.233
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.233
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.233
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.045
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.319
2024-11-25 16:33:21,521 - mmdet - INFO - Exp name: yolox_s_lite.py
2024-11-25 16:33:21,521 - mmdet - INFO - Epoch(val) [28][12] bbox_mAP: 0.0710, bbox_mAP_50: 0.3070, bbox_mAP_75: 0.0090, bbox_mAP_s: 0.0000, bbox_mAP_m: 0.0270, bbox_mAP_l: 0.0960, bbox_mAP_copypaste: 0.071 0.307 0.009 0.000 0.027 0.096
2024-11-25 16:33:43,761 - mmdet - INFO - Evaluating bbox...
2024-11-25 16:33:43,872 - mmdet - INFO -
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.095
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.400
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.030
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.034
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.128
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.264
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.264
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.264
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.045
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.363
2024-11-25 16:33:43,873 - mmdet - INFO - Exp name: yolox_s_lite.py
2024-11-25 16:33:43,873 - mmdet - INFO - Epoch(val) [29][12] bbox_mAP: 0.0950, bbox_mAP_50: 0.4000, bbox_mAP_75: 0.0300, bbox_mAP_s: 0.0000, bbox_mAP_m: 0.0340, bbox_mAP_l: 0.1280, bbox_mAP_copypaste: 0.095 0.400 0.030 0.000 0.034 0.128
2024-11-25 16:33:59,760 - mmdet - INFO - Saving checkpoint at 30 epochs
2024-11-25 16:34:01,698 - mmdet - INFO - Evaluating bbox...
2024-11-25 16:34:01,842 - mmdet - INFO -
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.098
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.360
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.025
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.053
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.152
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.295
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.300
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.300
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.145
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.374
2024-11-25 16:34:01,844 - mmdet - INFO - Exp name: yolox_s_lite.py
2024-11-25 16:34:01,844 - mmdet - INFO - Epoch(val) [30][12] bbox_mAP: 0.0980, bbox_mAP_50: 0.3600, bbox_mAP_75: 0.0250, bbox_mAP_s: 0.0000, bbox_mAP_m: 0.0530, bbox_mAP_l: 0.1520, bbox_mAP_copypaste: 0.098 0.360 0.025 0.000 0.053 0.152
2024-11-25 16:34:21,024 - mmdet - INFO - Evaluating bbox...
2024-11-25 16:34:21,256 - mmdet - INFO -
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.078
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.352
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.004
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.031
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.129
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.310
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.315
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.315
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.173
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.385
2024-11-25 16:34:21,258 - mmdet - INFO - Exp name: yolox_s_lite.py
2024-11-25 16:34:21,258 - mmdet - INFO - Epoch(val) [31][12] bbox_mAP: 0.0780, bbox_mAP_50: 0.3520, bbox_mAP_75: 0.0040, bbox_mAP_s: 0.0000, bbox_mAP_m: 0.0310, bbox_mAP_l: 0.1290, bbox_mAP_copypaste: 0.078 0.352 0.004 0.000 0.031 0.129
2024-11-25 16:34:46,245 - mmdet - INFO - Evaluating bbox...
2024-11-25 16:34:46,354 - mmdet - INFO -
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.061
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.234
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.004
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.034
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.098
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.292
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.305
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.305
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.200
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.359
2024-11-25 16:34:46,355 - mmdet - INFO - Exp name: yolox_s_lite.py
2024-11-25 16:34:46,355 - mmdet - INFO - Epoch(val) [32][12] bbox_mAP: 0.0610, bbox_mAP_50: 0.2340, bbox_mAP_75: 0.0040, bbox_mAP_s: 0.0000, bbox_mAP_m: 0.0340, bbox_mAP_l: 0.0980, bbox_mAP_copypaste: 0.061 0.234 0.004 0.000 0.034 0.098
2024-11-25 16:35:03,893 - mmdet - INFO - Evaluating bbox...
2024-11-25 16:35:03,988 - mmdet - INFO -
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.083
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.384
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.007
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.022
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.148
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.269
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.282
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.282
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.118
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.359
2024-11-25 16:35:03,989 - mmdet - INFO - Exp name: yolox_s_lite.py
2024-11-25 16:35:03,989 - mmdet - INFO - Epoch(val) [33][12] bbox_mAP: 0.0830, bbox_mAP_50: 0.3840, bbox_mAP_75: 0.0070, bbox_mAP_s: 0.0000, bbox_mAP_m: 0.0220, bbox_mAP_l: 0.1480, bbox_mAP_copypaste: 0.083 0.384 0.007 0.000 0.022 0.148
2024-11-25 16:35:30,271 - mmdet - INFO - Evaluating bbox...
2024-11-25 16:35:30,312 - mmdet - INFO -
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.096
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.326
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.007
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.016
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.159
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.313
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.313
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.313
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.136
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.396
2024-11-25 16:35:30,312 - mmdet - INFO - Exp name: yolox_s_lite.py
2024-11-25 16:35:30,312 - mmdet - INFO - Epoch(val) [34][12] bbox_mAP: 0.0960, bbox_mAP_50: 0.3260, bbox_mAP_75: 0.0070, bbox_mAP_s: 0.0000, bbox_mAP_m: 0.0160, bbox_mAP_l: 0.1590, bbox_mAP_copypaste: 0.096 0.326 0.007 0.000 0.016 0.159
2024-11-25 16:36:00,513 - mmdet - INFO - Evaluating bbox...
2024-11-25 16:36:00,553 - mmdet - INFO -
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.082
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.397
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.005
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.024
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.114
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.267
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.267
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.267
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.100
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.344
2024-11-25 16:36:00,553 - mmdet - INFO - Exp name: yolox_s_lite.py
2024-11-25 16:36:00,553 - mmdet - INFO - Epoch(val) [35][12] bbox_mAP: 0.0820, bbox_mAP_50: 0.3970, bbox_mAP_75: 0.0050, bbox_mAP_s: 0.0000, bbox_mAP_m: 0.0240, bbox_mAP_l: 0.1140, bbox_mAP_copypaste: 0.082 0.397 0.005 0.000 0.024 0.114
2024-11-25 16:36:26,753 - mmdet - INFO - Evaluating bbox...
2024-11-25 16:36:26,834 - mmdet - INFO -
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.069
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.324
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.000
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.018
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.096
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.205
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.205
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.205
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.045
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.278
2024-11-25 16:36:26,835 - mmdet - INFO - Exp name: yolox_s_lite.py
2024-11-25 16:36:26,835 - mmdet - INFO - Epoch(val) [36][12] bbox_mAP: 0.0690, bbox_mAP_50: 0.3240, bbox_mAP_75: 0.0000, bbox_mAP_s: 0.0000, bbox_mAP_m: 0.0180, bbox_mAP_l: 0.0960, bbox_mAP_copypaste: 0.069 0.324 0.000 0.000 0.018 0.096
2024-11-25 16:36:56,195 - mmdet - INFO - Evaluating bbox...
2024-11-25 16:36:56,273 - mmdet - INFO -
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.122
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.500
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.005
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.028
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.170
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.267
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.267
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.267
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.073
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.356
2024-11-25 16:36:56,274 - mmdet - INFO - Exp name: yolox_s_lite.py
2024-11-25 16:36:56,274 - mmdet - INFO - Epoch(val) [37][12] bbox_mAP: 0.1220, bbox_mAP_50: 0.5000, bbox_mAP_75: 0.0050, bbox_mAP_s: 0.0000, bbox_mAP_m: 0.0280, bbox_mAP_l: 0.1700, bbox_mAP_copypaste: 0.122 0.500 0.005 0.000 0.028 0.170
2024-11-25 16:37:31,155 - mmdet - INFO - Evaluating bbox...
2024-11-25 16:37:31,215 - mmdet - INFO -
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.116
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.403
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.039
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.018
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.164
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.262
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.262
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.262
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.073
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.348
2024-11-25 16:37:31,216 - mmdet - INFO - Exp name: yolox_s_lite.py
2024-11-25 16:37:31,216 - mmdet - INFO - Epoch(val) [38][12] bbox_mAP: 0.1160, bbox_mAP_50: 0.4030, bbox_mAP_75: 0.0390, bbox_mAP_s: 0.0000, bbox_mAP_m: 0.0180, bbox_mAP_l: 0.1640, bbox_mAP_copypaste: 0.116 0.403 0.039 0.000 0.018 0.164
2024-11-25 16:38:03,173 - mmdet - INFO - Evaluating bbox...
2024-11-25 16:38:03,260 - mmdet - INFO -
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.097
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.457
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.022
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.040
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.132
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.313
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.313
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.313
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.145
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.393
2024-11-25 16:38:03,261 - mmdet - INFO - Exp name: yolox_s_lite.py
2024-11-25 16:38:03,261 - mmdet - INFO - Epoch(val) [39][12] bbox_mAP: 0.0970, bbox_mAP_50: 0.4570, bbox_mAP_75: 0.0220, bbox_mAP_s: 0.0000, bbox_mAP_m: 0.0400, bbox_mAP_l: 0.1320, bbox_mAP_copypaste: 0.097 0.457 0.022 0.000 0.040 0.132
2024-11-25 16:38:32,880 - mmdet - INFO - Saving checkpoint at 40 epochs
2024-11-25 16:38:34,743 - mmdet - INFO - Evaluating bbox...
2024-11-25 16:38:34,836 - mmdet - INFO -
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.115
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.443
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.009
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.011
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.173
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.285
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.285
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.285
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.073
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.381
2024-11-25 16:38:34,837 - mmdet - INFO - Exp name: yolox_s_lite.py
2024-11-25 16:38:34,837 - mmdet - INFO - Epoch(val) [40][12] bbox_mAP: 0.1150, bbox_mAP_50: 0.4430, bbox_mAP_75: 0.0090, bbox_mAP_s: 0.0000, bbox_mAP_m: 0.0110, bbox_mAP_l: 0.1730, bbox_mAP_copypaste: 0.115 0.443 0.009 0.000 0.011 0.173
2024-11-25 16:39:05,323 - mmdet - INFO - Evaluating bbox...
2024-11-25 16:39:05,415 - mmdet - INFO -
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.117
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.432
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.002
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.024
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.166
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.282
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.282
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.282
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.100
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.367
2024-11-25 16:39:05,415 - mmdet - INFO - Exp name: yolox_s_lite.py
2024-11-25 16:39:05,416 - mmdet - INFO - Epoch(val) [41][12] bbox_mAP: 0.1170, bbox_mAP_50: 0.4320, bbox_mAP_75: 0.0020, bbox_mAP_s: 0.0000, bbox_mAP_m: 0.0240, bbox_mAP_l: 0.1660, bbox_mAP_copypaste: 0.117 0.432 0.002 0.000 0.024 0.166
2024-11-25 16:39:35,404 - mmdet - INFO - Evaluating bbox...
2024-11-25 16:39:35,509 - mmdet - INFO -
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.115
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.467
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.013
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.021
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.165
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.246
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.246
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.246
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.027
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.344
2024-11-25 16:39:35,510 - mmdet - INFO - Exp name: yolox_s_lite.py
2024-11-25 16:39:35,510 - mmdet - INFO - Epoch(val) [42][12] bbox_mAP: 0.1150, bbox_mAP_50: 0.4670, bbox_mAP_75: 0.0130, bbox_mAP_s: 0.0000, bbox_mAP_m: 0.0210, bbox_mAP_l: 0.1650, bbox_mAP_copypaste: 0.115 0.467 0.013 0.000 0.021 0.165
2024-11-25 16:40:08,153 - mmdet - INFO - Evaluating bbox...
2024-11-25 16:40:08,250 - mmdet - INFO -
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.097
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.414
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.004
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.021
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.144
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.256
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.259
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.259
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.036
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.359
2024-11-25 16:40:08,251 - mmdet - INFO - Exp name: yolox_s_lite.py
2024-11-25 16:40:08,251 - mmdet - INFO - Epoch(val) [43][12] bbox_mAP: 0.0970, bbox_mAP_50: 0.4140, bbox_mAP_75: 0.0040, bbox_mAP_s: 0.0000, bbox_mAP_m: 0.0210, bbox_mAP_l: 0.1440, bbox_mAP_copypaste: 0.097 0.414 0.004 0.000 0.021 0.144
2024-11-25 16:40:35,325 - mmdet - INFO - Evaluating bbox...
2024-11-25 16:40:35,420 - mmdet - INFO -
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.097
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.391
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.013
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.004
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.147
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.259
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.264
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.264
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.073
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.352
2024-11-25 16:40:35,421 - mmdet - INFO - Exp name: yolox_s_lite.py
2024-11-25 16:40:35,421 - mmdet - INFO - Epoch(val) [44][12] bbox_mAP: 0.0970, bbox_mAP_50: 0.3910, bbox_mAP_75: 0.0130, bbox_mAP_s: 0.0000, bbox_mAP_m: 0.0040, bbox_mAP_l: 0.1470, bbox_mAP_copypaste: 0.097 0.391 0.013 0.000 0.004 0.147
2024-11-25 16:40:35,427 - mmdet - INFO - No mosaic and mixup aug now!
2024-11-25 16:40:35,446 - mmdet - INFO - Add additional L1 loss now!
2024-11-25 16:41:00,250 - mmdet - INFO - Evaluating bbox...
2024-11-25 16:41:00,303 - mmdet - INFO -
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.125
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.496
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.010
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.037
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.191
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.321
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.321
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.321
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.155
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.400
2024-11-25 16:41:00,303 - mmdet - INFO - Exp name: yolox_s_lite.py
2024-11-25 16:41:00,303 - mmdet - INFO - Epoch(val) [45][12] bbox_mAP: 0.1250, bbox_mAP_50: 0.4960, bbox_mAP_75: 0.0100, bbox_mAP_s: 0.0000, bbox_mAP_m: 0.0370, bbox_mAP_l: 0.1910, bbox_mAP_copypaste: 0.125 0.496 0.010 0.000 0.037 0.191
2024-11-25 16:41:15,804 - mmdet - INFO - Evaluating bbox...
2024-11-25 16:41:15,849 - mmdet - INFO -
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.151
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.450
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.070
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.095
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.207
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.433
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.433
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.433
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.336
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.489
2024-11-25 16:41:15,850 - mmdet - INFO - Exp name: yolox_s_lite.py
2024-11-25 16:41:15,850 - mmdet - INFO - Epoch(val) [46][12] bbox_mAP: 0.1510, bbox_mAP_50: 0.4500, bbox_mAP_75: 0.0700, bbox_mAP_s: 0.0000, bbox_mAP_m: 0.0950, bbox_mAP_l: 0.2070, bbox_mAP_copypaste: 0.151 0.450 0.070 0.000 0.095 0.207
2024-11-25 16:41:34,399 - mmdet - INFO - Evaluating bbox...
2024-11-25 16:41:34,437 - mmdet - INFO -
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.158
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.475
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.076
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.085
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.214
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.415
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.415
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.415
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.318
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.470
2024-11-25 16:41:34,437 - mmdet - INFO - Exp name: yolox_s_lite.py
2024-11-25 16:41:34,437 - mmdet - INFO - Epoch(val) [47][12] bbox_mAP: 0.1580, bbox_mAP_50: 0.4750, bbox_mAP_75: 0.0760, bbox_mAP_s: 0.0000, bbox_mAP_m: 0.0850, bbox_mAP_l: 0.2140, bbox_mAP_copypaste: 0.158 0.475 0.076 0.000 0.085 0.214
2024-11-25 16:41:59,647 - mmdet - INFO - Evaluating bbox...
2024-11-25 16:41:59,678 - mmdet - INFO -
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.205
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.589
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.108
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.082
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.284
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.423
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.423
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.423
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.282
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.496
2024-11-25 16:41:59,679 - mmdet - INFO - Exp name: yolox_s_lite.py
2024-11-25 16:41:59,679 - mmdet - INFO - Epoch(val) [48][12] bbox_mAP: 0.2050, bbox_mAP_50: 0.5890, bbox_mAP_75: 0.1080, bbox_mAP_s: 0.0000, bbox_mAP_m: 0.0820, bbox_mAP_l: 0.2840, bbox_mAP_copypaste: 0.205 0.589 0.108 0.000 0.082 0.284
2024-11-25 16:42:22,000 - mmdet - INFO - Evaluating bbox...
2024-11-25 16:42:22,030 - mmdet - INFO -
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.200
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.646
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.099
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.075
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.274
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.382
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.382
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.382
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.227
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.459
2024-11-25 16:42:22,031 - mmdet - INFO - Exp name: yolox_s_lite.py
2024-11-25 16:42:22,031 - mmdet - INFO - Epoch(val) [49][12] bbox_mAP: 0.2000, bbox_mAP_50: 0.6460, bbox_mAP_75: 0.0990, bbox_mAP_s: 0.0000, bbox_mAP_m: 0.0750, bbox_mAP_l: 0.2740, bbox_mAP_copypaste: 0.200 0.646 0.099 0.000 0.075 0.274
2024-11-25 16:42:40,298 - mmdet - INFO - Saving checkpoint at 50 epochs
2024-11-25 16:42:42,145 - mmdet - INFO - Evaluating bbox...
2024-11-25 16:42:42,175 - mmdet - INFO -
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.204
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.585
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.099
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.076
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.278
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.403
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.403
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.403
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.255
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.478
2024-11-25 16:42:42,176 - mmdet - INFO - Exp name: yolox_s_lite.py
2024-11-25 16:42:42,176 - mmdet - INFO - Epoch(val) [50][12] bbox_mAP: 0.2040, bbox_mAP_50: 0.5850, bbox_mAP_75: 0.0990, bbox_mAP_s: 0.0000, bbox_mAP_m: 0.0760, bbox_mAP_l: 0.2780, bbox_mAP_copypaste: 0.204 0.585 0.099 0.000 0.076 0.278
2024-11-25 16:42:59,862 - mmdet - INFO - Evaluating bbox...
2024-11-25 16:42:59,889 - mmdet - INFO -
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.235
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.640
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.132
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.083
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.318
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.423
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.423
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.423
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.264
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.504
2024-11-25 16:42:59,890 - mmdet - INFO - Exp name: yolox_s_lite.py
2024-11-25 16:42:59,890 - mmdet - INFO - Epoch(val) [51][12] bbox_mAP: 0.2350, bbox_mAP_50: 0.6400, bbox_mAP_75: 0.1320, bbox_mAP_s: 0.0000, bbox_mAP_m: 0.0830, bbox_mAP_l: 0.3180, bbox_mAP_copypaste: 0.235 0.640 0.132 0.000 0.083 0.318
2024-11-25 16:43:19,825 - mmdet - INFO - Evaluating bbox...
2024-11-25 16:43:19,850 - mmdet - INFO -
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.216
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.567
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.111
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.074
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.294
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.413
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.413
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.413
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.273
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.485
2024-11-25 16:43:19,851 - mmdet - INFO - Exp name: yolox_s_lite.py
2024-11-25 16:43:19,851 - mmdet - INFO - Epoch(val) [52][12] bbox_mAP: 0.2160, bbox_mAP_50: 0.5670, bbox_mAP_75: 0.1110, bbox_mAP_s: 0.0000, bbox_mAP_m: 0.0740, bbox_mAP_l: 0.2940, bbox_mAP_copypaste: 0.216 0.567 0.111 0.000 0.074 0.294
2024-11-25 16:43:45,711 - mmdet - INFO - Evaluating bbox...
2024-11-25 16:43:45,735 - mmdet - INFO -
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.232
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.653
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.136
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.081
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.318
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.405
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.405
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.405
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.191
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.507
2024-11-25 16:43:45,736 - mmdet - INFO - Exp name: yolox_s_lite.py
2024-11-25 16:43:45,736 - mmdet - INFO - Epoch(val) [53][12] bbox_mAP: 0.2320, bbox_mAP_50: 0.6530, bbox_mAP_75: 0.1360, bbox_mAP_s: 0.0000, bbox_mAP_m: 0.0810, bbox_mAP_l: 0.3180, bbox_mAP_copypaste: 0.232 0.653 0.136 0.000 0.081 0.318
2024-11-25 16:44:12,978 - mmdet - INFO - Evaluating bbox...
2024-11-25 16:44:13,001 - mmdet - INFO -
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.222
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.655
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.108
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.088
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.298
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.392
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.392
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.392
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.218
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.478
2024-11-25 16:44:13,001 - mmdet - INFO - Exp name: yolox_s_lite.py
2024-11-25 16:44:13,001 - mmdet - INFO - Epoch(val) [54][12] bbox_mAP: 0.2220, bbox_mAP_50: 0.6550, bbox_mAP_75: 0.1080, bbox_mAP_s: 0.0000, bbox_mAP_m: 0.0880, bbox_mAP_l: 0.2980, bbox_mAP_copypaste: 0.222 0.655 0.108 0.000 0.088 0.298
2024-11-25 16:44:40,354 - mmdet - INFO - Evaluating bbox...
2024-11-25 16:44:40,383 - mmdet - INFO -
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.225
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.650
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.104
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.079
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.303
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.392
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.392
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.392
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.200
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.485
2024-11-25 16:44:40,383 - mmdet - INFO - Exp name: yolox_s_lite.py
2024-11-25 16:44:40,383 - mmdet - INFO - Epoch(val) [55][12] bbox_mAP: 0.2250, bbox_mAP_50: 0.6500, bbox_mAP_75: 0.1040, bbox_mAP_s: 0.0000, bbox_mAP_m: 0.0790, bbox_mAP_l: 0.3030, bbox_mAP_copypaste: 0.225 0.650 0.104 0.000 0.079 0.303
2024-11-25 16:44:59,186 - mmdet - INFO - Evaluating bbox...
2024-11-25 16:44:59,211 - mmdet - INFO -
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.216
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.646
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.115
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.080
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.291
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.377
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.377
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.377
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.218
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.456
2024-11-25 16:44:59,212 - mmdet - INFO - Exp name: yolox_s_lite.py
2024-11-25 16:44:59,212 - mmdet - INFO - Epoch(val) [56][12] bbox_mAP: 0.2160, bbox_mAP_50: 0.6460, bbox_mAP_75: 0.1150, bbox_mAP_s: 0.0000, bbox_mAP_m: 0.0800, bbox_mAP_l: 0.2910, bbox_mAP_copypaste: 0.216 0.646 0.115 0.000 0.080 0.291
2024-11-25 16:45:20,408 - mmdet - INFO - Evaluating bbox...
2024-11-25 16:45:20,439 - mmdet - INFO -
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.219
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.669
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.075
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.092
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.294
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.379
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.379
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.379
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.245
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.448
2024-11-25 16:45:20,440 - mmdet - INFO - Exp name: yolox_s_lite.py
2024-11-25 16:45:20,440 - mmdet - INFO - Epoch(val) [57][12] bbox_mAP: 0.2190, bbox_mAP_50: 0.6690, bbox_mAP_75: 0.0750, bbox_mAP_s: 0.0000, bbox_mAP_m: 0.0920, bbox_mAP_l: 0.2940, bbox_mAP_copypaste: 0.219 0.669 0.075 0.000 0.092 0.294
2024-11-25 16:45:48,193 - mmdet - INFO - Evaluating bbox...
2024-11-25 16:45:48,218 - mmdet - INFO -
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.234
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.668
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.098
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.071
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.326
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.369
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.369
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.369
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.218
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.444
2024-11-25 16:45:48,219 - mmdet - INFO - Exp name: yolox_s_lite.py
2024-11-25 16:45:48,219 - mmdet - INFO - Epoch(val) [58][12] bbox_mAP: 0.2340, bbox_mAP_50: 0.6680, bbox_mAP_75: 0.0980, bbox_mAP_s: 0.0000, bbox_mAP_m: 0.0710, bbox_mAP_l: 0.3260, bbox_mAP_copypaste: 0.234 0.668 0.098 0.000 0.071 0.326
2024-11-25 16:46:14,833 - mmdet - INFO - Evaluating bbox...
2024-11-25 16:46:14,858 - mmdet - INFO -
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.238
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.696
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.092
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.080
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.330
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.382
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.382
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.382
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.236
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.456
2024-11-25 16:46:14,859 - mmdet - INFO - Exp name: yolox_s_lite.py
2024-11-25 16:46:14,859 - mmdet - INFO - Epoch(val) [59][12] bbox_mAP: 0.2380, bbox_mAP_50: 0.6960, bbox_mAP_75: 0.0920, bbox_mAP_s: 0.0000, bbox_mAP_m: 0.0800, bbox_mAP_l: 0.3300, bbox_mAP_copypaste: 0.238 0.696 0.092 0.000 0.080 0.330
2024-11-25 16:46:39,681 - mmdet - INFO - Saving checkpoint at 60 epochs
2024-11-25 16:46:41,406 - mmdet - INFO - Evaluating bbox...
2024-11-25 16:46:41,431 - mmdet - INFO -
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.217
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.682
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.091
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.075
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.297
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.379
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.379
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.379
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.236
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.452
2024-11-25 16:46:41,432 - mmdet - INFO - Exp name: yolox_s_lite.py
2024-11-25 16:46:41,432 - mmdet - INFO - Epoch(val) [60][12] bbox_mAP: 0.2170, bbox_mAP_50: 0.6820, bbox_mAP_75: 0.0910, bbox_mAP_s: 0.0000, bbox_mAP_m: 0.0750, bbox_mAP_l: 0.2970, bbox_mAP_copypaste: 0.217 0.682 0.091 0.000 0.075 0.297
SUCCESS: ModelMaker - Training completed.
