20230204_223042.log

2023-02-04 22:30:42,325 - mmcls - INFO - Environment info:
------------------------------------------------------------
sys.platform: linux
Python: 3.8.16 (default, Jan 17 2023, 23:13:24) [GCC 11.2.0]
CUDA available: True
GPU 0: Tesla V100-SXM2-32GB
CUDA_HOME: /data/apps/cuda/11.1
NVCC: Cuda compilation tools, release 11.1, V11.1.105
GCC: gcc (GCC) 7.3.0
PyTorch: 1.10.0
PyTorch compiling details: PyTorch built with:
  - GCC 7.3
  - C++ Version: 201402
  - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications
  - Intel(R) MKL-DNN v2.2.3 (Git Hash 7336ca9f055cf1bfa13efb658fe15dc9b41f0740)
  - OpenMP 201511 (a.k.a. OpenMP 4.5)
  - LAPACK is enabled (usually provided by MKL)
  - NNPACK is enabled
  - CPU capability usage: AVX512
  - CUDA Runtime 11.3
  - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_37,code=compute_37
  - CuDNN 8.2
  - Magma 2.5.2
  - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.2.0, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -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 -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.10.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, 

TorchVision: 0.11.0
OpenCV: 4.7.0
MMCV: 1.7.0
MMCV Compiler: GCC 7.3
MMCV CUDA Compiler: 11.1
MMClassification: 0.25.0+3d4f80d
------------------------------------------------------------

2023-02-04 22:30:42,328 - mmcls - INFO - Distributed training: False
2023-02-04 22:30:42,433 - mmcls - INFO - Config:
model = dict(
    type='ImageClassifier',
    backbone=dict(
        type='ResNet',
        depth=18,
        num_stages=4,
        out_indices=(3, ),
        style='pytorch'),
    neck=dict(type='GlobalAveragePooling'),
    head=dict(
        type='LinearClsHead',
        num_classes=5,
        in_channels=512,
        loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
        topk=(1, )))
dataset_type = 'ImageNet'
img_norm_cfg = dict(
    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(type='RandomResizedCrop', size=224),
    dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'),
    dict(
        type='Normalize',
        mean=[123.675, 116.28, 103.53],
        std=[58.395, 57.12, 57.375],
        to_rgb=True),
    dict(type='ImageToTensor', keys=['img']),
    dict(type='ToTensor', keys=['gt_label']),
    dict(type='Collect', keys=['img', 'gt_label'])
]
test_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(type='Resize', size=(256, -1)),
    dict(type='CenterCrop', crop_size=224),
    dict(
        type='Normalize',
        mean=[123.675, 116.28, 103.53],
        std=[58.395, 57.12, 57.375],
        to_rgb=True),
    dict(type='ImageToTensor', keys=['img']),
    dict(type='Collect', keys=['img'])
]
data = dict(
    samples_per_gpu=32,
    workers_per_gpu=2,
    train=dict(
        type='ImageNet',
        data_prefix='',
        pipeline=[
            dict(type='LoadImageFromFile'),
            dict(type='RandomResizedCrop', size=224),
            dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'),
            dict(
                type='Normalize',
                mean=[123.675, 116.28, 103.53],
                std=[58.395, 57.12, 57.375],
                to_rgb=True),
            dict(type='ImageToTensor', keys=['img']),
            dict(type='ToTensor', keys=['gt_label']),
            dict(type='Collect', keys=['img', 'gt_label'])
        ],
        ann_file='flower/train.txt',
        classes='flower/classes.txt'),
    val=dict(
        type='ImageNet',
        data_prefix='',
        ann_file='flower/val.txt',
        pipeline=[
            dict(type='LoadImageFromFile'),
            dict(type='Resize', size=(256, -1)),
            dict(type='CenterCrop', crop_size=224),
            dict(
                type='Normalize',
                mean=[123.675, 116.28, 103.53],
                std=[58.395, 57.12, 57.375],
                to_rgb=True),
            dict(type='ImageToTensor', keys=['img']),
            dict(type='Collect', keys=['img'])
        ],
        classes='flower/classes.txt'),
    test=dict(
        type='ImageNet',
        data_prefix='data/imagenet/val',
        ann_file='data/imagenet/meta/val.txt',
        pipeline=[
            dict(type='LoadImageFromFile'),
            dict(type='Resize', size=(256, -1)),
            dict(type='CenterCrop', crop_size=224),
            dict(
                type='Normalize',
                mean=[123.675, 116.28, 103.53],
                std=[58.395, 57.12, 57.375],
                to_rgb=True),
            dict(type='ImageToTensor', keys=['img']),
            dict(type='Collect', keys=['img'])
        ]))
evaluation = dict(interval=1, metric='accuracy')
checkpoint_config = dict(interval=1)
log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')])
dist_params = dict(backend='nccl')
log_level = 'INFO'
load_from = 'checkpoints/resnet18_batch256_imagenet_20200708-34ab8f90.pth'
resume_from = None
workflow = [('train', 1)]
optimizer = dict(type='SGD', lr=0.001, momentum=0.9, weight_decay=0.0001)
optimizer_config = dict(grad_clip=None)
lr_config = dict(policy='step', step=[1])
runner = dict(type='EpochBasedRunner', max_epochs=100)
work_dir = 'work/resnet18_b32_flower'
gpu_ids = [0]

2023-02-04 22:30:42,434 - mmcls - INFO - Set random seed to 535910259, deterministic: False
2023-02-04 22:30:42,572 - mmcls - INFO - initialize ResNet with init_cfg [{'type': 'Kaiming', 'layer': ['Conv2d']}, {'type': 'Constant', 'val': 1, 'layer': ['_BatchNorm', 'GroupNorm']}]
2023-02-04 22:30:42,699 - mmcls - INFO - initialize LinearClsHead with init_cfg {'type': 'Normal', 'layer': 'Linear', 'std': 0.01}
Name of parameter - Initialization information

backbone.conv1.weight - torch.Size([64, 3, 7, 7]): 
KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 

backbone.bn1.weight - torch.Size([64]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.bn1.bias - torch.Size([64]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.layer1.0.conv1.weight - torch.Size([64, 64, 3, 3]): 
KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 

backbone.layer1.0.bn1.weight - torch.Size([64]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.layer1.0.bn1.bias - torch.Size([64]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.layer1.0.conv2.weight - torch.Size([64, 64, 3, 3]): 
KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 

backbone.layer1.0.bn2.weight - torch.Size([64]): 
Initialized by user-defined `init_weights` in ResNet  

backbone.layer1.0.bn2.bias - torch.Size([64]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.layer1.1.conv1.weight - torch.Size([64, 64, 3, 3]): 
KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 

backbone.layer1.1.bn1.weight - torch.Size([64]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.layer1.1.bn1.bias - torch.Size([64]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.layer1.1.conv2.weight - torch.Size([64, 64, 3, 3]): 
KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 

backbone.layer1.1.bn2.weight - torch.Size([64]): 
Initialized by user-defined `init_weights` in ResNet  

backbone.layer1.1.bn2.bias - torch.Size([64]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.layer2.0.conv1.weight - torch.Size([128, 64, 3, 3]): 
KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 

backbone.layer2.0.bn1.weight - torch.Size([128]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.layer2.0.bn1.bias - torch.Size([128]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.layer2.0.conv2.weight - torch.Size([128, 128, 3, 3]): 
KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 

backbone.layer2.0.bn2.weight - torch.Size([128]): 
Initialized by user-defined `init_weights` in ResNet  

backbone.layer2.0.bn2.bias - torch.Size([128]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.layer2.0.downsample.0.weight - torch.Size([128, 64, 1, 1]): 
KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 

backbone.layer2.0.downsample.1.weight - torch.Size([128]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.layer2.0.downsample.1.bias - torch.Size([128]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.layer2.1.conv1.weight - torch.Size([128, 128, 3, 3]): 
KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 

backbone.layer2.1.bn1.weight - torch.Size([128]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.layer2.1.bn1.bias - torch.Size([128]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.layer2.1.conv2.weight - torch.Size([128, 128, 3, 3]): 
KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 

backbone.layer2.1.bn2.weight - torch.Size([128]): 
Initialized by user-defined `init_weights` in ResNet  

backbone.layer2.1.bn2.bias - torch.Size([128]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.layer3.0.conv1.weight - torch.Size([256, 128, 3, 3]): 
KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 

backbone.layer3.0.bn1.weight - torch.Size([256]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.layer3.0.bn1.bias - torch.Size([256]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.layer3.0.conv2.weight - torch.Size([256, 256, 3, 3]): 
KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 

backbone.layer3.0.bn2.weight - torch.Size([256]): 
Initialized by user-defined `init_weights` in ResNet  

backbone.layer3.0.bn2.bias - torch.Size([256]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.layer3.0.downsample.0.weight - torch.Size([256, 128, 1, 1]): 
KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 

backbone.layer3.0.downsample.1.weight - torch.Size([256]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.layer3.0.downsample.1.bias - torch.Size([256]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.layer3.1.conv1.weight - torch.Size([256, 256, 3, 3]): 
KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 

backbone.layer3.1.bn1.weight - torch.Size([256]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.layer3.1.bn1.bias - torch.Size([256]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.layer3.1.conv2.weight - torch.Size([256, 256, 3, 3]): 
KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 

backbone.layer3.1.bn2.weight - torch.Size([256]): 
Initialized by user-defined `init_weights` in ResNet  

backbone.layer3.1.bn2.bias - torch.Size([256]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.layer4.0.conv1.weight - torch.Size([512, 256, 3, 3]): 
KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 

backbone.layer4.0.bn1.weight - torch.Size([512]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.layer4.0.bn1.bias - torch.Size([512]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.layer4.0.conv2.weight - torch.Size([512, 512, 3, 3]): 
KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 

backbone.layer4.0.bn2.weight - torch.Size([512]): 
Initialized by user-defined `init_weights` in ResNet  

backbone.layer4.0.bn2.bias - torch.Size([512]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.layer4.0.downsample.0.weight - torch.Size([512, 256, 1, 1]): 
KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 

backbone.layer4.0.downsample.1.weight - torch.Size([512]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.layer4.0.downsample.1.bias - torch.Size([512]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.layer4.1.conv1.weight - torch.Size([512, 512, 3, 3]): 
KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 

backbone.layer4.1.bn1.weight - torch.Size([512]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.layer4.1.bn1.bias - torch.Size([512]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.layer4.1.conv2.weight - torch.Size([512, 512, 3, 3]): 
KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 

backbone.layer4.1.bn2.weight - torch.Size([512]): 
Initialized by user-defined `init_weights` in ResNet  

backbone.layer4.1.bn2.bias - torch.Size([512]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

head.fc.weight - torch.Size([5, 512]): 
NormalInit: mean=0, std=0.01, bias=0 

head.fc.bias - torch.Size([5]): 
NormalInit: mean=0, std=0.01, bias=0 
2023-02-04 22:30:50,302 - mmcls - INFO - load checkpoint from local path: checkpoints/resnet18_batch256_imagenet_20200708-34ab8f90.pth
2023-02-04 22:30:50,468 - mmcls - WARNING - The model and loaded state dict do not match exactly

size mismatch for head.fc.weight: copying a param with shape torch.Size([1000, 512]) from checkpoint, the shape in current model is torch.Size([5, 512]).
size mismatch for head.fc.bias: copying a param with shape torch.Size([1000]) from checkpoint, the shape in current model is torch.Size([5]).
2023-02-04 22:30:50,468 - mmcls - INFO - Start running, host: scv6a1o@g0020, work_dir: /data/home/scv6a1o/mmclassification/work/resnet18_b32_flower
2023-02-04 22:30:50,469 - mmcls - INFO - Hooks will be executed in the following order:
before_run:
(VERY_HIGH   ) StepLrUpdaterHook                  
(NORMAL      ) CheckpointHook                     
(LOW         ) EvalHook                           
(VERY_LOW    ) TextLoggerHook                     
 -------------------- 
before_train_epoch:
(VERY_HIGH   ) StepLrUpdaterHook                  
(LOW         ) IterTimerHook                      
(LOW         ) EvalHook                           
(VERY_LOW    ) TextLoggerHook                     
 -------------------- 
before_train_iter:
(VERY_HIGH   ) StepLrUpdaterHook                  
(LOW         ) IterTimerHook                      
(LOW         ) EvalHook                           
 -------------------- 
after_train_iter:
(ABOVE_NORMAL) OptimizerHook                      
(NORMAL      ) CheckpointHook                     
(LOW         ) IterTimerHook                      
(LOW         ) EvalHook                           
(VERY_LOW    ) TextLoggerHook                     
 -------------------- 
after_train_epoch:
(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                     
 -------------------- 
2023-02-04 22:30:50,469 - mmcls - INFO - workflow: [('train', 1)], max: 100 epochs
2023-02-04 22:30:50,469 - mmcls - INFO - Checkpoints will be saved to /data/home/scv6a1o/mmclassification/work/resnet18_b32_flower by HardDiskBackend.
2023-02-04 22:31:01,171 - mmcls - INFO - Saving checkpoint at 1 epochs
2023-02-04 22:31:03,387 - mmcls - INFO - Epoch(val) [1][18]	accuracy_top-1: 91.2587, accuracy_top-5: 100.0000
2023-02-04 22:31:10,011 - mmcls - INFO - Saving checkpoint at 2 epochs
2023-02-04 22:31:11,601 - mmcls - INFO - Epoch(val) [2][18]	accuracy_top-1: 90.9091, accuracy_top-5: 100.0000
2023-02-04 22:31:18,175 - mmcls - INFO - Saving checkpoint at 3 epochs
2023-02-04 22:31:19,602 - mmcls - INFO - Epoch(val) [3][18]	accuracy_top-1: 92.1329, accuracy_top-5: 100.0000
2023-02-04 22:31:26,311 - mmcls - INFO - Saving checkpoint at 4 epochs
2023-02-04 22:31:27,812 - mmcls - INFO - Epoch(val) [4][18]	accuracy_top-1: 91.9580, accuracy_top-5: 100.0000
2023-02-04 22:31:34,318 - mmcls - INFO - Saving checkpoint at 5 epochs
2023-02-04 22:31:35,790 - mmcls - INFO - Epoch(val) [5][18]	accuracy_top-1: 93.0070, accuracy_top-5: 100.0000
2023-02-04 22:31:42,505 - mmcls - INFO - Saving checkpoint at 6 epochs
2023-02-04 22:31:43,928 - mmcls - INFO - Epoch(val) [6][18]	accuracy_top-1: 92.6573, accuracy_top-5: 100.0000
2023-02-04 22:31:50,598 - mmcls - INFO - Saving checkpoint at 7 epochs
2023-02-04 22:31:52,033 - mmcls - INFO - Epoch(val) [7][18]	accuracy_top-1: 92.1329, accuracy_top-5: 100.0000
2023-02-04 22:31:58,642 - mmcls - INFO - Saving checkpoint at 8 epochs
2023-02-04 22:32:00,101 - mmcls - INFO - Epoch(val) [8][18]	accuracy_top-1: 93.1818, accuracy_top-5: 100.0000
2023-02-04 22:32:06,891 - mmcls - INFO - Saving checkpoint at 9 epochs
2023-02-04 22:32:08,290 - mmcls - INFO - Epoch(val) [9][18]	accuracy_top-1: 93.0070, accuracy_top-5: 100.0000
2023-02-04 22:32:14,925 - mmcls - INFO - Saving checkpoint at 10 epochs
2023-02-04 22:32:16,380 - mmcls - INFO - Epoch(val) [10][18]	accuracy_top-1: 93.0070, accuracy_top-5: 100.0000
2023-02-04 22:32:23,108 - mmcls - INFO - Saving checkpoint at 11 epochs
2023-02-04 22:32:24,638 - mmcls - INFO - Epoch(val) [11][18]	accuracy_top-1: 92.4825, accuracy_top-5: 100.0000
2023-02-04 22:32:31,343 - mmcls - INFO - Saving checkpoint at 12 epochs
2023-02-04 22:32:32,858 - mmcls - INFO - Epoch(val) [12][18]	accuracy_top-1: 93.8811, accuracy_top-5: 100.0000
2023-02-04 22:32:39,501 - mmcls - INFO - Saving checkpoint at 13 epochs
2023-02-04 22:32:40,998 - mmcls - INFO - Epoch(val) [13][18]	accuracy_top-1: 93.3566, accuracy_top-5: 100.0000
2023-02-04 22:32:47,637 - mmcls - INFO - Saving checkpoint at 14 epochs
2023-02-04 22:32:49,074 - mmcls - INFO - Epoch(val) [14][18]	accuracy_top-1: 93.5315, accuracy_top-5: 100.0000
2023-02-04 22:32:56,028 - mmcls - INFO - Saving checkpoint at 15 epochs
2023-02-04 22:32:57,491 - mmcls - INFO - Epoch(val) [15][18]	accuracy_top-1: 93.3566, accuracy_top-5: 100.0000
2023-02-04 22:33:04,054 - mmcls - INFO - Saving checkpoint at 16 epochs
2023-02-04 22:33:05,628 - mmcls - INFO - Epoch(val) [16][18]	accuracy_top-1: 93.5315, accuracy_top-5: 100.0000
2023-02-04 22:33:12,327 - mmcls - INFO - Saving checkpoint at 17 epochs
2023-02-04 22:33:13,755 - mmcls - INFO - Epoch(val) [17][18]	accuracy_top-1: 93.0070, accuracy_top-5: 100.0000
2023-02-04 22:33:20,519 - mmcls - INFO - Saving checkpoint at 18 epochs
2023-02-04 22:33:21,963 - mmcls - INFO - Epoch(val) [18][18]	accuracy_top-1: 94.2308, accuracy_top-5: 100.0000
2023-02-04 22:33:28,793 - mmcls - INFO - Saving checkpoint at 19 epochs
2023-02-04 22:33:30,264 - mmcls - INFO - Epoch(val) [19][18]	accuracy_top-1: 94.0559, accuracy_top-5: 100.0000
2023-02-04 22:33:37,038 - mmcls - INFO - Saving checkpoint at 20 epochs
2023-02-04 22:33:38,478 - mmcls - INFO - Epoch(val) [20][18]	accuracy_top-1: 94.2308, accuracy_top-5: 100.0000
2023-02-04 22:33:45,028 - mmcls - INFO - Saving checkpoint at 21 epochs
2023-02-04 22:33:46,499 - mmcls - INFO - Epoch(val) [21][18]	accuracy_top-1: 93.7063, accuracy_top-5: 100.0000
2023-02-04 22:33:53,307 - mmcls - INFO - Saving checkpoint at 22 epochs
2023-02-04 22:33:54,756 - mmcls - INFO - Epoch(val) [22][18]	accuracy_top-1: 94.4056, accuracy_top-5: 100.0000
2023-02-04 22:34:01,518 - mmcls - INFO - Saving checkpoint at 23 epochs
2023-02-04 22:34:03,017 - mmcls - INFO - Epoch(val) [23][18]	accuracy_top-1: 94.0559, accuracy_top-5: 100.0000
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2023-02-04 22:34:27,543 - mmcls - INFO - Epoch(val) [26][18]	accuracy_top-1: 94.2308, accuracy_top-5: 100.0000
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2023-02-04 22:34:35,673 - mmcls - INFO - Epoch(val) [27][18]	accuracy_top-1: 93.7063, accuracy_top-5: 100.0000
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2023-02-04 22:34:43,739 - mmcls - INFO - Epoch(val) [28][18]	accuracy_top-1: 94.2308, accuracy_top-5: 100.0000
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2023-02-04 22:34:51,828 - mmcls - INFO - Epoch(val) [29][18]	accuracy_top-1: 93.8811, accuracy_top-5: 100.0000
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2023-02-04 22:35:08,095 - mmcls - INFO - Epoch(val) [31][18]	accuracy_top-1: 93.7063, accuracy_top-5: 100.0000
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2023-02-04 22:35:24,244 - mmcls - INFO - Epoch(val) [33][18]	accuracy_top-1: 94.4056, accuracy_top-5: 100.0000
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2023-02-04 22:35:32,316 - mmcls - INFO - Epoch(val) [34][18]	accuracy_top-1: 94.9301, accuracy_top-5: 100.0000
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2023-02-04 22:35:40,304 - mmcls - INFO - Epoch(val) [35][18]	accuracy_top-1: 94.4056, accuracy_top-5: 100.0000
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2023-02-04 22:35:48,300 - mmcls - INFO - Epoch(val) [36][18]	accuracy_top-1: 94.5804, accuracy_top-5: 100.0000
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2023-02-04 22:35:56,623 - mmcls - INFO - Epoch(val) [37][18]	accuracy_top-1: 94.5804, accuracy_top-5: 100.0000
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