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open-mmlab / mmpretrain

OpenMMLab Pre-training Toolbox and Benchmark
https://mmpretrain.readthedocs.io/en/latest/
Apache License 2.0
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Resume training drop developed model metrics #449

Closed lukomski closed 3 years ago

lukomski commented 3 years ago

I have dataset split to training, validation and test. I'm monitoring process of learning by runninng tests and compute kohen-kappa score. In my chart there is obvious where the learning has stopped, and then was resumed, becouse the score is dropping down. Figure_1

The picture shows cohen-cappa score for epochs on test dataset.

What am i doing wrong? Is my configuration correct? Or mayby it is normal effect?

Post related information

  1. For training i use train.py script from tools/. So for resume training i use command ex.

    python tools/train.py configs/pavements/pavements_shufflenet_v2_1x_b64x16_linearlr_bn_nowd.py \
--resume-from ./work_dirs/pavements_shufflenet_v2_1x_b64x16_linearlr_bn_nowd/epoch_884.pth

  2. Configuration printed at begin of train script:

    model = dict(
type='ImageClassifier',
backbone=dict(type='ShuffleNetV2', widen_factor=1.0),
neck=dict(type='GlobalAveragePooling'),
head=dict(
    type='LinearClsHead',
    num_classes=6,
    in_channels=1024,
    loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
    topk=(1, 5)))
dataset_type = 'Pavements'
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, backend='pillow'),
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), backend='pillow'),
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=64,
workers_per_gpu=1,
train=dict(
    type='Pavements',
    data_prefix='../dataset/train',
    pipeline=[
        dict(type='LoadImageFromFile'),
        dict(type='RandomResizedCrop', size=224, backend='pillow'),
        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'])
    ]),
val=dict(
    type='Pavements',
    data_prefix='../dataset/validation',
    pipeline=[
        dict(type='LoadImageFromFile'),
        dict(type='Resize', size=(256, -1), backend='pillow'),
        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'])
    ]),
test=dict(
    type='Pavements',
    data_prefix='../dataset/test',
    pipeline=[
        dict(type='LoadImageFromFile'),
        dict(type='Resize', size=(256, -1), backend='pillow'),
        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')
optimizer = dict(
type='SGD',
lr=0.5,
momentum=0.9,
weight_decay=4e-05,
paramwise_cfg=dict(norm_decay_mult=0))
optimizer_config = dict(grad_clip=None)
lr_config = dict(
policy='poly',
min_lr=0,
by_epoch=False,
warmup='constant',
warmup_iters=5000)
runner = dict(type='EpochBasedRunner', max_epochs=2500)
checkpoint_config = dict(interval=1, create_symlink=False)
log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')])
dist_params = dict(backend='nccl')
log_level = 'INFO'
load_from = None
resume_from = './work_dirs/pavements_shufflenet_v2_1x_b64x16_linearlr_bn_nowd/epoch_884.pth'
workflow = [('train', 1)]
work_dir = './work_dirs/pavements_shufflenet_v2_1x_b64x16_linearlr_bn_nowd'
gpu_ids = range(0, 1)

  3. Configuration file:

    
# model settings
model = dict(
type='ImageClassifier',
backbone=dict(type='ShuffleNetV2', widen_factor=1.0),
neck=dict(type='GlobalAveragePooling'),
head=dict(
    type='LinearClsHead',
    num_classes=6,
    in_channels=1024,
    loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
    topk=(1, 5),
))

dataset settings

dataset_type = 'Pavements' 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, backend='pillow'), dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'), dict(type='Normalize', img_norm_cfg), 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), backend='pillow'), dict(type='CenterCrop', crop_size=224), dict(type='Normalize', img_norm_cfg), dict(type='ImageToTensor', keys=['img']), dict(type='Collect', keys=['img']) ] data = dict( samples_per_gpu=64,#32 workers_per_gpu=1,#2 train=dict( type=dataset_type, data_prefix='../dataset/train', pipeline=train_pipeline), val=dict( type=dataset_type, data_prefix='../dataset/validation', pipeline=test_pipeline), test=dict(

replace data/val with data/test for standard test

    type=dataset_type,
    data_prefix='../dataset/test',
    pipeline=test_pipeline))

evaluation = dict(interval=1, metric='accuracy')

optimizer

optimizer = dict( type='SGD', lr=0.5, momentum=0.9, weight_decay=0.00004, paramwise_cfg=dict(norm_decay_mult=0)) optimizer_config = dict(grad_clip=None)

learning policy

lr_config = dict( policy='poly', min_lr=0, by_epoch=False, warmup='constant', warmup_iters=5000, ) runner = dict(type='EpochBasedRunner', max_epochs=2500)

checkpoint saving

checkpoint_config = dict(interval=1, create_symlink=False)

yapf:disable

log_config = dict( interval=100, hooks=[ dict(type='TextLoggerHook'),

dict(type='TensorboardLoggerHook')

])

yapf:enable

dist_params = dict(backend='nccl') log_level = 'INFO' load_from = None resume_from = None workflow = [('train', 1)]

mzr1996 commented 3 years ago

Hello, so you resumed from the 884 epoch? But in your figure, it looks like no score dropping down at the 884 epoch?

lukomski commented 3 years ago

No, sorry for ambiguity. The picture shows case when i stop and resumed in: 600, 1131 and 1500 epochs.

Here is case, where resume from 884 epoch (orange) is superimposed on previous chart: Figure_2

In each situation the score drop.

Drop on blue line - 600, 1131, 1500 epochs Drop on orange line - 884 ( 884 is common for blue and orange)

mzr1996 commented 3 years ago

Can you provide the log file when resume checkpoints？That will help us to reproduce the problem

lukomski commented 3 years ago

Ok, There is log file after resume from 884 epoch (orange):

{"mmcls_version": "0.15.0", "config": "model = dict(\n    type='ImageClassifier',\n    backbone=dict(type='ShuffleNetV2', widen_factor=1.0),\n    neck=dict(type='GlobalAveragePooling'),\n    head=dict(\n        type='LinearClsHead',\n        num_classes=6,\n        in_channels=1024,\n        loss=dict(type='CrossEntropyLoss', loss_weight=1.0),\n        topk=(1, 5)))\ndataset_type = 'Pavements'\nimg_norm_cfg = dict(\n    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)\ntrain_pipeline = [\n    dict(type='LoadImageFromFile'),\n    dict(type='RandomResizedCrop', size=224, backend='pillow'),\n    dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'),\n    dict(\n        type='Normalize',\n        mean=[123.675, 116.28, 103.53],\n        std=[58.395, 57.12, 57.375],\n        to_rgb=True),\n    dict(type='ImageToTensor', keys=['img']),\n    dict(type='ToTensor', keys=['gt_label']),\n    dict(type='Collect', keys=['img', 'gt_label'])\n]\ntest_pipeline = [\n    dict(type='LoadImageFromFile'),\n    dict(type='Resize', size=(256, -1), backend='pillow'),\n    dict(type='CenterCrop', crop_size=224),\n    dict(\n        type='Normalize',\n        mean=[123.675, 116.28, 103.53],\n        std=[58.395, 57.12, 57.375],\n        to_rgb=True),\n    dict(type='ImageToTensor', keys=['img']),\n    dict(type='Collect', keys=['img'])\n]\ndata = dict(\n    samples_per_gpu=64,\n    workers_per_gpu=1,\n    train=dict(\n        type='Pavements',\n        data_prefix='../dataset/train',\n        pipeline=[\n            dict(type='LoadImageFromFile'),\n            dict(type='RandomResizedCrop', size=224, backend='pillow'),\n            dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'),\n            dict(\n                type='Normalize',\n                mean=[123.675, 116.28, 103.53],\n                std=[58.395, 57.12, 57.375],\n                to_rgb=True),\n            dict(type='ImageToTensor', keys=['img']),\n            dict(type='ToTensor', keys=['gt_label']),\n            dict(type='Collect', keys=['img', 'gt_label'])\n        ]),\n    val=dict(\n        type='Pavements',\n        data_prefix='../dataset/validation',\n        pipeline=[\n            dict(type='LoadImageFromFile'),\n            dict(type='Resize', size=(256, -1), backend='pillow'),\n            dict(type='CenterCrop', crop_size=224),\n            dict(\n                type='Normalize',\n                mean=[123.675, 116.28, 103.53],\n                std=[58.395, 57.12, 57.375],\n                to_rgb=True),\n            dict(type='ImageToTensor', keys=['img']),\n            dict(type='Collect', keys=['img'])\n        ]),\n    test=dict(\n        type='Pavements',\n        data_prefix='../dataset/test',\n        pipeline=[\n            dict(type='LoadImageFromFile'),\n            dict(type='Resize', size=(256, -1), backend='pillow'),\n            dict(type='CenterCrop', crop_size=224),\n            dict(\n                type='Normalize',\n                mean=[123.675, 116.28, 103.53],\n                std=[58.395, 57.12, 57.375],\n                to_rgb=True),\n            dict(type='ImageToTensor', keys=['img']),\n            dict(type='Collect', keys=['img'])\n        ]))\nevaluation = dict(interval=1, metric='accuracy')\noptimizer = dict(\n    type='SGD',\n    lr=0.5,\n    momentum=0.9,\n    weight_decay=4e-05,\n    paramwise_cfg=dict(norm_decay_mult=0))\noptimizer_config = dict(grad_clip=None)\nlr_config = dict(\n    policy='poly',\n    min_lr=0,\n    by_epoch=False,\n    warmup='constant',\n    warmup_iters=5000)\nrunner = dict(type='EpochBasedRunner', max_epochs=300)\ncheckpoint_config = dict(interval=1, create_symlink=False)\nlog_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')])\ndist_params = dict(backend='nccl')\nlog_level = 'INFO'\nload_from = None\nresume_from = None\nworkflow = [('train', 1)]\nwork_dir = './work_dirs/pavements_shufflenet_v2_1x_b64x16_linearlr_bn_nowd'\ngpu_ids = range(0, 1)\nseed = None\n", "CLASSES": ["class1", "class2", "class3", "class4", "class5", "class6"], "env_info": "sys.platform: linux\nPython: 3.7.11 (default, Jul  3 2021, 18:01:19) [GCC 7.5.0]\nCUDA available: True\nGPU 0: Tesla T4\nCUDA_HOME: /usr/local/cuda\nNVCC: Build cuda_11.0_bu.TC445_37.28845127_0\nGCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0\nPyTorch: 1.9.0+cu102\nPyTorch compiling details: PyTorch built with:\n  - GCC 7.3\n  - C++ Version: 201402\n  - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications\n  - Intel(R) MKL-DNN v2.1.2 (Git Hash 98be7e8afa711dc9b66c8ff3504129cb82013cdb)\n  - OpenMP 201511 (a.k.a. OpenMP 4.5)\n  - NNPACK is enabled\n  - CPU capability usage: AVX2\n  - CUDA Runtime 10.2\n  - 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_70,code=sm_70\n  - CuDNN 7.6.5\n  - Magma 2.5.2\n  - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=10.2, CUDNN_VERSION=7.6.5, 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 -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=1.9.0, 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, \n\nTorchVision: 0.10.0+cu102\nOpenCV: 4.1.2\nMMCV: 1.3.12\nMMCV Compiler: n/a\nMMCV CUDA Compiler: n/a\nMMClassification: 0.15.0+a41cb2f", "seed": null, "epoch": 884, "iter": 29172, "mmcv_version": "1.3.12", "time": "Fri Sep  3 14:34:18 2021", "hook_msgs": {"last_ckpt": "/content/drive/My Drive/mmclassification/work_dirs/pavements_shufflenet_v2_1x_b64x16_linearlr_bn_nowd/epoch_883.pth"}}
{"mode": "val", "epoch": 885, "iter": 10, "lr": 0.32301, "accuracy_top-1": 53.16667, "accuracy_top-5": 98.0}
{"mode": "val", "epoch": 886, "iter": 10, "lr": 0.32281, "accuracy_top-1": 66.16667, "accuracy_top-5": 99.83333}
{"mode": "val", "epoch": 887, "iter": 10, "lr": 0.32261, "accuracy_top-1": 58.83333, "accuracy_top-5": 98.83333}
{"mode": "val", "epoch": 888, "iter": 10, "lr": 0.32241, "accuracy_top-1": 62.0, "accuracy_top-5": 99.5}
{"mode": "val", "epoch": 889, "iter": 10, "lr": 0.32221, "accuracy_top-1": 66.0, "accuracy_top-5": 99.66667}
{"mode": "val", "epoch": 890, "iter": 10, "lr": 0.32201, "accuracy_top-1": 63.83333, "accuracy_top-5": 99.5}
{"mode": "val", "epoch": 891, "iter": 10, "lr": 0.32181, "accuracy_top-1": 70.16667, "accuracy_top-5": 99.66667}
{"mode": "val", "epoch": 892, "iter": 10, "lr": 0.32161, "accuracy_top-1": 45.16667, "accuracy_top-5": 98.5}
{"mode": "val", "epoch": 893, "iter": 10, "lr": 0.32141, "accuracy_top-1": 55.0, "accuracy_top-5": 98.0}
{"mode": "val", "epoch": 894, "iter": 10, "lr": 0.32121, "accuracy_top-1": 54.5, "accuracy_top-5": 98.83333}
{"mode": "val", "epoch": 895, "iter": 10, "lr": 0.32101, "accuracy_top-1": 56.83333, "accuracy_top-5": 98.83333}
{"mode": "val", "epoch": 896, "iter": 10, "lr": 0.32081, "accuracy_top-1": 59.33333, "accuracy_top-5": 99.16667}
{"mode": "val", "epoch": 897, "iter": 10, "lr": 0.32061, "accuracy_top-1": 66.66667, "accuracy_top-5": 99.66667}
{"mode": "val", "epoch": 898, "iter": 10, "lr": 0.32041, "accuracy_top-1": 66.66667, "accuracy_top-5": 99.5}
{"mode": "val", "epoch": 899, "iter": 10, "lr": 0.32021, "accuracy_top-1": 66.83333, "accuracy_top-5": 99.83333}
{"mode": "val", "epoch": 900, "iter": 10, "lr": 0.32001, "accuracy_top-1": 62.33333, "accuracy_top-5": 99.0}
{"mode": "val", "epoch": 901, "iter": 10, "lr": 0.31981, "accuracy_top-1": 63.0, "accuracy_top-5": 98.83333}
{"mode": "val", "epoch": 902, "iter": 10, "lr": 0.31961, "accuracy_top-1": 60.0, "accuracy_top-5": 99.33333}
{"mode": "val", "epoch": 903, "iter": 10, "lr": 0.31941, "accuracy_top-1": 60.0, "accuracy_top-5": 99.0}
{"mode": "val", "epoch": 904, "iter": 10, "lr": 0.31921, "accuracy_top-1": 62.5, "accuracy_top-5": 99.5}
{"mode": "val", "epoch": 905, "iter": 10, "lr": 0.31901, "accuracy_top-1": 64.0, "accuracy_top-5": 99.5}
{"mode": "val", "epoch": 906, "iter": 10, "lr": 0.31881, "accuracy_top-1": 63.16667, "accuracy_top-5": 99.0}
{"mode": "val", "epoch": 907, "iter": 10, "lr": 0.31861, "accuracy_top-1": 62.66667, "accuracy_top-5": 98.83333}
{"mode": "val", "epoch": 908, "iter": 10, "lr": 0.31841, "accuracy_top-1": 65.0, "accuracy_top-5": 99.5}
{"mode": "val", "epoch": 909, "iter": 10, "lr": 0.31821, "accuracy_top-1": 65.16667, "accuracy_top-5": 99.83333}
{"mode": "val", "epoch": 910, "iter": 10, "lr": 0.31801, "accuracy_top-1": 67.83333, "accuracy_top-5": 99.5}
{"mode": "val", "epoch": 911, "iter": 10, "lr": 0.31781, "accuracy_top-1": 53.16667, "accuracy_top-5": 98.16667}
{"mode": "val", "epoch": 912, "iter": 10, "lr": 0.31761, "accuracy_top-1": 65.83333, "accuracy_top-5": 99.66667}
{"mode": "val", "epoch": 913, "iter": 10, "lr": 0.31741, "accuracy_top-1": 62.66667, "accuracy_top-5": 99.66667}
{"mode": "val", "epoch": 914, "iter": 10, "lr": 0.31721, "accuracy_top-1": 65.33333, "accuracy_top-5": 99.83333}
{"mode": "val", "epoch": 915, "iter": 10, "lr": 0.31701, "accuracy_top-1": 62.0, "accuracy_top-5": 99.33333}
{"mode": "val", "epoch": 916, "iter": 10, "lr": 0.31681, "accuracy_top-1": 68.16667, "accuracy_top-5": 99.33333}
{"mode": "val", "epoch": 917, "iter": 10, "lr": 0.31661, "accuracy_top-1": 63.83333, "accuracy_top-5": 99.33333}
{"mode": "val", "epoch": 918, "iter": 10, "lr": 0.31641, "accuracy_top-1": 64.0, "accuracy_top-5": 99.0}
{"mode": "val", "epoch": 919, "iter": 10, "lr": 0.31621, "accuracy_top-1": 65.66667, "accuracy_top-5": 99.66667}
{"mode": "val", "epoch": 920, "iter": 10, "lr": 0.31601, "accuracy_top-1": 66.16667, "accuracy_top-5": 99.5}
{"mode": "val", "epoch": 921, "iter": 10, "lr": 0.31581, "accuracy_top-1": 63.33333, "accuracy_top-5": 99.33333}
{"mode": "val", "epoch": 922, "iter": 10, "lr": 0.31561, "accuracy_top-1": 52.83333, "accuracy_top-5": 97.83333}
{"mode": "val", "epoch": 923, "iter": 10, "lr": 0.31541, "accuracy_top-1": 60.33333, "accuracy_top-5": 99.66667}
{"mode": "val", "epoch": 924, "iter": 10, "lr": 0.31521, "accuracy_top-1": 68.5, "accuracy_top-5": 99.33333}
{"mode": "val", "epoch": 925, "iter": 10, "lr": 0.31501, "accuracy_top-1": 61.5, "accuracy_top-5": 99.5}
{"mode": "val", "epoch": 926, "iter": 10, "lr": 0.31481, "accuracy_top-1": 64.83333, "accuracy_top-5": 99.66667}
{"mode": "val", "epoch": 927, "iter": 10, "lr": 0.31461, "accuracy_top-1": 62.16667, "accuracy_top-5": 98.66667}
{"mode": "val", "epoch": 928, "iter": 10, "lr": 0.31441, "accuracy_top-1": 61.5, "accuracy_top-5": 99.5}
{"mode": "val", "epoch": 929, "iter": 10, "lr": 0.31421, "accuracy_top-1": 64.33333, "accuracy_top-5": 99.0}
{"mode": "val", "epoch": 930, "iter": 10, "lr": 0.31401, "accuracy_top-1": 65.0, "accuracy_top-5": 99.33333}
{"mode": "val", "epoch": 931, "iter": 10, "lr": 0.31381, "accuracy_top-1": 48.33333, "accuracy_top-5": 98.5}
{"mode": "val", "epoch": 932, "iter": 10, "lr": 0.31361, "accuracy_top-1": 67.16667, "accuracy_top-5": 100.0}
{"mode": "val", "epoch": 933, "iter": 10, "lr": 0.31341, "accuracy_top-1": 62.16667, "accuracy_top-5": 99.66667}
{"mode": "val", "epoch": 934, "iter": 10, "lr": 0.31321, "accuracy_top-1": 64.0, "accuracy_top-5": 99.83333}
{"mode": "val", "epoch": 935, "iter": 10, "lr": 0.31301, "accuracy_top-1": 64.83333, "accuracy_top-5": 99.66667}
{"mode": "val", "epoch": 936, "iter": 10, "lr": 0.31281, "accuracy_top-1": 36.66667, "accuracy_top-5": 96.16667}
{"mode": "val", "epoch": 937, "iter": 10, "lr": 0.31261, "accuracy_top-1": 53.33333, "accuracy_top-5": 98.33333}
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{"mode": "val", "epoch": 1393, "iter": 10, "lr": 0.22141, "accuracy_top-1": 69.16667, "accuracy_top-5": 99.83333}
{"mode": "val", "epoch": 1394, "iter": 10, "lr": 0.22121, "accuracy_top-1": 53.66667, "accuracy_top-5": 97.66667}
{"mode": "val", "epoch": 1395, "iter": 10, "lr": 0.22101, "accuracy_top-1": 66.66667, "accuracy_top-5": 99.66667}
{"mode": "val", "epoch": 1396, "iter": 10, "lr": 0.22081, "accuracy_top-1": 56.66667, "accuracy_top-5": 99.16667}
{"mode": "val", "epoch": 1397, "iter": 10, "lr": 0.22061, "accuracy_top-1": 57.83333, "accuracy_top-5": 98.66667}
{"mode": "val", "epoch": 1398, "iter": 10, "lr": 0.22041, "accuracy_top-1": 64.33333, "accuracy_top-5": 99.33333}
{"mode": "val", "epoch": 1399, "iter": 10, "lr": 0.22021, "accuracy_top-1": 64.66667, "accuracy_top-5": 99.83333}
{"mode": "val", "epoch": 1400, "iter": 10, "lr": 0.22001, "accuracy_top-1": 66.66667, "accuracy_top-5": 99.5}
{"mode": "val", "epoch": 1401, "iter": 10, "lr": 0.21981, "accuracy_top-1": 66.83333, "accuracy_top-5": 99.33333}
{"mode": "val", "epoch": 1402, "iter": 10, "lr": 0.21961, "accuracy_top-1": 64.83333, "accuracy_top-5": 99.33333}
{"mode": "val", "epoch": 1403, "iter": 10, "lr": 0.21941, "accuracy_top-1": 62.33333, "accuracy_top-5": 99.33333}
{"mode": "val", "epoch": 1404, "iter": 10, "lr": 0.21921, "accuracy_top-1": 66.33333, "accuracy_top-5": 99.83333}
{"mode": "val", "epoch": 1405, "iter": 10, "lr": 0.21901, "accuracy_top-1": 61.5, "accuracy_top-5": 99.5}
{"mode": "val", "epoch": 1406, "iter": 10, "lr": 0.21881, "accuracy_top-1": 66.0, "accuracy_top-5": 99.5}
{"mode": "val", "epoch": 1407, "iter": 10, "lr": 0.21861, "accuracy_top-1": 66.0, "accuracy_top-5": 99.83333}
{"mode": "val", "epoch": 1408, "iter": 10, "lr": 0.21841, "accuracy_top-1": 61.83333, "accuracy_top-5": 99.16667}
{"mode": "val", "epoch": 1409, "iter": 10, "lr": 0.21821, "accuracy_top-1": 66.5, "accuracy_top-5": 99.0}
{"mode": "val", "epoch": 1410, "iter": 10, "lr": 0.21801, "accuracy_top-1": 65.16667, "accuracy_top-5": 99.33333}

There is log resume from 600 epoch (part of blue):

{"mmcls_version": "0.15.0", "config": "model = dict(\n    type='ImageClassifier',\n    backbone=dict(type='ShuffleNetV2', widen_factor=1.0),\n    neck=dict(type='GlobalAveragePooling'),\n    head=dict(\n        type='LinearClsHead',\n        num_classes=6,\n        in_channels=1024,\n        loss=dict(type='CrossEntropyLoss', loss_weight=1.0),\n        topk=(1, 5)))\ndataset_type = 'Pavements'\nimg_norm_cfg = dict(\n    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)\ntrain_pipeline = [\n    dict(type='LoadImageFromFile'),\n    dict(type='RandomResizedCrop', size=224, backend='pillow'),\n    dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'),\n    dict(\n        type='Normalize',\n        mean=[123.675, 116.28, 103.53],\n        std=[58.395, 57.12, 57.375],\n        to_rgb=True),\n    dict(type='ImageToTensor', keys=['img']),\n    dict(type='ToTensor', keys=['gt_label']),\n    dict(type='Collect', keys=['img', 'gt_label'])\n]\ntest_pipeline = [\n    dict(type='LoadImageFromFile'),\n    dict(type='Resize', size=(256, -1), backend='pillow'),\n    dict(type='CenterCrop', crop_size=224),\n    dict(\n        type='Normalize',\n        mean=[123.675, 116.28, 103.53],\n        std=[58.395, 57.12, 57.375],\n        to_rgb=True),\n    dict(type='ImageToTensor', keys=['img']),\n    dict(type='Collect', keys=['img'])\n]\ndata = dict(\n    samples_per_gpu=64,\n    workers_per_gpu=1,\n    train=dict(\n        type='Pavements',\n        data_prefix='../dataset/train',\n        pipeline=[\n            dict(type='LoadImageFromFile'),\n            dict(type='RandomResizedCrop', size=224, backend='pillow'),\n            dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'),\n            dict(\n                type='Normalize',\n                mean=[123.675, 116.28, 103.53],\n                std=[58.395, 57.12, 57.375],\n                to_rgb=True),\n            dict(type='ImageToTensor', keys=['img']),\n            dict(type='ToTensor', keys=['gt_label']),\n            dict(type='Collect', keys=['img', 'gt_label'])\n        ]),\n    val=dict(\n        type='Pavements',\n        data_prefix='../dataset/validation',\n        pipeline=[\n            dict(type='LoadImageFromFile'),\n            dict(type='Resize', size=(256, -1), backend='pillow'),\n            dict(type='CenterCrop', crop_size=224),\n            dict(\n                type='Normalize',\n                mean=[123.675, 116.28, 103.53],\n                std=[58.395, 57.12, 57.375],\n                to_rgb=True),\n            dict(type='ImageToTensor', keys=['img']),\n            dict(type='Collect', keys=['img'])\n        ]),\n    test=dict(\n        type='Pavements',\n        data_prefix='../dataset/test',\n        pipeline=[\n            dict(type='LoadImageFromFile'),\n            dict(type='Resize', size=(256, -1), backend='pillow'),\n            dict(type='CenterCrop', crop_size=224),\n            dict(\n                type='Normalize',\n                mean=[123.675, 116.28, 103.53],\n                std=[58.395, 57.12, 57.375],\n                to_rgb=True),\n            dict(type='ImageToTensor', keys=['img']),\n            dict(type='Collect', keys=['img'])\n        ]))\nevaluation = dict(interval=1, metric='accuracy')\noptimizer = dict(\n    type='SGD',\n    lr=0.5,\n    momentum=0.9,\n    weight_decay=4e-05,\n    paramwise_cfg=dict(norm_decay_mult=0))\noptimizer_config = dict(grad_clip=None)\nlr_config = dict(\n    policy='poly',\n    min_lr=0,\n    by_epoch=False,\n    warmup='constant',\n    warmup_iters=5000)\nrunner = dict(type='EpochBasedRunner', max_epochs=300)\ncheckpoint_config = dict(interval=1, create_symlink=False)\nlog_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')])\ndist_params = dict(backend='nccl')\nlog_level = 'INFO'\nload_from = None\nresume_from = None\nworkflow = [('train', 1)]\nwork_dir = './work_dirs/pavements_shufflenet_v2_1x_b64x16_linearlr_bn_nowd'\ngpu_ids = range(0, 1)\nseed = None\n", "CLASSES": ["class1", "class2", "class3", "class4", "class5", "class6"], "env_info": "sys.platform: linux\nPython: 3.7.11 (default, Jul  3 2021, 18:01:19) [GCC 7.5.0]\nCUDA available: True\nGPU 0: Tesla T4\nCUDA_HOME: /usr/local/cuda\nNVCC: Build cuda_11.0_bu.TC445_37.28845127_0\nGCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0\nPyTorch: 1.9.0+cu102\nPyTorch compiling details: PyTorch built with:\n  - GCC 7.3\n  - C++ Version: 201402\n  - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications\n  - Intel(R) MKL-DNN v2.1.2 (Git Hash 98be7e8afa711dc9b66c8ff3504129cb82013cdb)\n  - OpenMP 201511 (a.k.a. OpenMP 4.5)\n  - NNPACK is enabled\n  - CPU capability usage: AVX2\n  - CUDA Runtime 10.2\n  - 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_70,code=sm_70\n  - CuDNN 7.6.5\n  - Magma 2.5.2\n  - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=10.2, CUDNN_VERSION=7.6.5, 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 -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=1.9.0, 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, \n\nTorchVision: 0.10.0+cu102\nOpenCV: 4.1.2\nMMCV: 1.3.12\nMMCV Compiler: n/a\nMMCV CUDA Compiler: n/a\nMMClassification: 0.15.0+a41cb2f", "seed": null, "epoch": 600, "iter": 19800, "mmcv_version": "1.3.12", "time": "Fri Sep  3 09:19:45 2021", "hook_msgs": {"last_ckpt": "/content/drive/MyDrive/mmclassification/work_dirs/pavements_shufflenet_v2_1x_b64x16_linearlr_bn_nowd/epoch_599.pth"}}
{"mode": "val", "epoch": 601, "iter": 10, "lr": 0.19952, "accuracy_top-1": 58.5, "accuracy_top-5": 98.66667}
{"mode": "val", "epoch": 602, "iter": 10, "lr": 0.19902, "accuracy_top-1": 62.16667, "accuracy_top-5": 99.83333}
{"mode": "val", "epoch": 603, "iter": 10, "lr": 0.19852, "accuracy_top-1": 57.0, "accuracy_top-5": 99.0}
{"mode": "val", "epoch": 604, "iter": 10, "lr": 0.19802, "accuracy_top-1": 56.16667, "accuracy_top-5": 98.33333}
{"mode": "val", "epoch": 605, "iter": 10, "lr": 0.19752, "accuracy_top-1": 67.66667, "accuracy_top-5": 99.83333}
{"mode": "val", "epoch": 606, "iter": 10, "lr": 0.19702, "accuracy_top-1": 68.5, "accuracy_top-5": 99.5}
{"mode": "val", "epoch": 607, "iter": 10, "lr": 0.19652, "accuracy_top-1": 62.66667, "accuracy_top-5": 100.0}
{"mode": "val", "epoch": 608, "iter": 10, "lr": 0.19602, "accuracy_top-1": 64.33333, "accuracy_top-5": 99.5}
{"mode": "val", "epoch": 609, "iter": 10, "lr": 0.19552, "accuracy_top-1": 67.16667, "accuracy_top-5": 100.0}
{"mode": "val", "epoch": 610, "iter": 10, "lr": 0.19502, "accuracy_top-1": 58.16667, "accuracy_top-5": 98.66667}
{"mode": "val", "epoch": 611, "iter": 10, "lr": 0.19452, "accuracy_top-1": 67.16667, "accuracy_top-5": 99.83333}
{"mode": "val", "epoch": 612, "iter": 10, "lr": 0.19402, "accuracy_top-1": 70.66667, "accuracy_top-5": 100.0}
{"mode": "val", "epoch": 613, "iter": 10, "lr": 0.19352, "accuracy_top-1": 65.16667, "accuracy_top-5": 99.0}
{"mode": "val", "epoch": 614, "iter": 10, "lr": 0.19302, "accuracy_top-1": 63.16667, "accuracy_top-5": 99.83333}
{"mode": "val", "epoch": 615, "iter": 10, "lr": 0.19252, "accuracy_top-1": 65.0, "accuracy_top-5": 99.66667}
{"mode": "val", "epoch": 616, "iter": 10, "lr": 0.19202, "accuracy_top-1": 62.66667, "accuracy_top-5": 99.16667}
{"mode": "val", "epoch": 617, "iter": 10, "lr": 0.19152, "accuracy_top-1": 60.33333, "accuracy_top-5": 99.66667}
{"mode": "val", "epoch": 618, "iter": 10, "lr": 0.19102, "accuracy_top-1": 67.66667, "accuracy_top-5": 100.0}
{"mode": "val", "epoch": 619, "iter": 10, "lr": 0.19052, "accuracy_top-1": 69.5, "accuracy_top-5": 99.66667}
{"mode": "val", "epoch": 620, "iter": 10, "lr": 0.19002, "accuracy_top-1": 62.83333, "accuracy_top-5": 99.16667}
{"mode": "val", "epoch": 621, "iter": 10, "lr": 0.18952, "accuracy_top-1": 65.83333, "accuracy_top-5": 99.33333}
{"mode": "val", "epoch": 622, "iter": 10, "lr": 0.18902, "accuracy_top-1": 65.0, "accuracy_top-5": 99.5}
{"mode": "val", "epoch": 623, "iter": 10, "lr": 0.18852, "accuracy_top-1": 63.0, "accuracy_top-5": 99.16667}
{"mode": "val", "epoch": 624, "iter": 10, "lr": 0.18802, "accuracy_top-1": 64.83333, "accuracy_top-5": 99.83333}
{"mode": "val", "epoch": 625, "iter": 10, "lr": 0.18752, "accuracy_top-1": 63.33333, "accuracy_top-5": 99.5}
{"mode": "val", "epoch": 626, "iter": 10, "lr": 0.18702, "accuracy_top-1": 63.66667, "accuracy_top-5": 99.83333}
{"mode": "val", "epoch": 627, "iter": 10, "lr": 0.18652, "accuracy_top-1": 55.16667, "accuracy_top-5": 99.5}
{"mode": "val", "epoch": 628, "iter": 10, "lr": 0.18602, "accuracy_top-1": 69.33333, "accuracy_top-5": 99.5}
{"mode": "val", "epoch": 629, "iter": 10, "lr": 0.18552, "accuracy_top-1": 64.83333, "accuracy_top-5": 100.0}
{"mode": "val", "epoch": 630, "iter": 10, "lr": 0.18502, "accuracy_top-1": 57.0, "accuracy_top-5": 98.33333}
{"mode": "val", "epoch": 631, "iter": 10, "lr": 0.18452, "accuracy_top-1": 62.5, "accuracy_top-5": 99.66667}
{"mode": "val", "epoch": 632, "iter": 10, "lr": 0.18402, "accuracy_top-1": 67.66667, "accuracy_top-5": 99.66667}
{"mode": "val", "epoch": 633, "iter": 10, "lr": 0.18352, "accuracy_top-1": 67.5, "accuracy_top-5": 99.83333}
{"mode": "val", "epoch": 634, "iter": 10, "lr": 0.18302, "accuracy_top-1": 59.0, "accuracy_top-5": 99.33333}
{"mode": "val", "epoch": 635, "iter": 10, "lr": 0.18252, "accuracy_top-1": 66.5, "accuracy_top-5": 99.83333}
{"mode": "val", "epoch": 636, "iter": 10, "lr": 0.18202, "accuracy_top-1": 68.83333, "accuracy_top-5": 99.83333}
{"mode": "val", "epoch": 637, "iter": 10, "lr": 0.18152, "accuracy_top-1": 68.66667, "accuracy_top-5": 100.0}
{"mode": "val", "epoch": 638, "iter": 10, "lr": 0.18102, "accuracy_top-1": 71.5, "accuracy_top-5": 99.5}
{"mode": "val", "epoch": 639, "iter": 10, "lr": 0.18052, "accuracy_top-1": 68.66667, "accuracy_top-5": 99.83333}
{"mode": "val", "epoch": 640, "iter": 10, "lr": 0.18002, "accuracy_top-1": 55.0, "accuracy_top-5": 98.83333}
{"mode": "val", "epoch": 641, "iter": 10, "lr": 0.17952, "accuracy_top-1": 57.66667, "accuracy_top-5": 99.0}
{"mode": "val", "epoch": 642, "iter": 10, "lr": 0.17902, "accuracy_top-1": 64.66667, "accuracy_top-5": 99.5}
{"mode": "val", "epoch": 643, "iter": 10, "lr": 0.17852, "accuracy_top-1": 67.5, "accuracy_top-5": 99.5}
{"mode": "val", "epoch": 644, "iter": 10, "lr": 0.17802, "accuracy_top-1": 65.66667, "accuracy_top-5": 99.83333}
{"mode": "val", "epoch": 645, "iter": 10, "lr": 0.17752, "accuracy_top-1": 63.83333, "accuracy_top-5": 99.83333}
{"mode": "val", "epoch": 646, "iter": 10, "lr": 0.17702, "accuracy_top-1": 67.83333, "accuracy_top-5": 99.83333}
{"mode": "val", "epoch": 647, "iter": 10, "lr": 0.17652, "accuracy_top-1": 67.83333, "accuracy_top-5": 99.33333}
{"mode": "val", "epoch": 648, "iter": 10, "lr": 0.17602, "accuracy_top-1": 65.16667, "accuracy_top-5": 99.66667}
{"mode": "val", "epoch": 649, "iter": 10, "lr": 0.17552, "accuracy_top-1": 66.83333, "accuracy_top-5": 99.5}
{"mode": "val", "epoch": 650, "iter": 10, "lr": 0.17502, "accuracy_top-1": 67.33333, "accuracy_top-5": 99.66667}
{"mode": "val", "epoch": 651, "iter": 10, "lr": 0.17452, "accuracy_top-1": 64.33333, "accuracy_top-5": 99.66667}
{"mode": "val", "epoch": 652, "iter": 10, "lr": 0.17402, "accuracy_top-1": 69.0, "accuracy_top-5": 99.83333}
{"mode": "val", "epoch": 653, "iter": 10, "lr": 0.17352, "accuracy_top-1": 67.33333, "accuracy_top-5": 99.83333}
{"mode": "val", "epoch": 654, "iter": 10, "lr": 0.17302, "accuracy_top-1": 69.83333, "accuracy_top-5": 100.0}
{"mode": "val", "epoch": 655, "iter": 10, "lr": 0.17252, "accuracy_top-1": 63.33333, "accuracy_top-5": 100.0}
{"mode": "val", "epoch": 656, "iter": 10, "lr": 0.17202, "accuracy_top-1": 68.16667, "accuracy_top-5": 99.83333}
{"mode": "val", "epoch": 657, "iter": 10, "lr": 0.17152, "accuracy_top-1": 69.83333, "accuracy_top-5": 99.66667}
{"mode": "val", "epoch": 658, "iter": 10, "lr": 0.17102, "accuracy_top-1": 64.5, "accuracy_top-5": 99.83333}
{"mode": "val", "epoch": 659, "iter": 10, "lr": 0.17052, "accuracy_top-1": 70.66667, "accuracy_top-5": 99.83333}
{"mode": "val", "epoch": 660, "iter": 10, "lr": 0.17002, "accuracy_top-1": 66.66667, "accuracy_top-5": 99.83333}
{"mode": "val", "epoch": 661, "iter": 10, "lr": 0.16952, "accuracy_top-1": 69.83333, "accuracy_top-5": 99.16667}
{"mode": "val", "epoch": 662, "iter": 10, "lr": 0.16902, "accuracy_top-1": 65.66667, "accuracy_top-5": 99.33333}
{"mode": "val", "epoch": 663, "iter": 10, "lr": 0.16852, "accuracy_top-1": 65.5, "accuracy_top-5": 99.33333}
{"mode": "val", "epoch": 664, "iter": 10, "lr": 0.16802, "accuracy_top-1": 70.33333, "accuracy_top-5": 99.66667}
{"mode": "val", "epoch": 665, "iter": 10, "lr": 0.16752, "accuracy_top-1": 62.83333, "accuracy_top-5": 99.33333}
{"mode": "val", "epoch": 666, "iter": 10, "lr": 0.16702, "accuracy_top-1": 66.5, "accuracy_top-5": 99.33333}
{"mode": "val", "epoch": 667, "iter": 10, "lr": 0.16652, "accuracy_top-1": 67.16667, "accuracy_top-5": 99.5}
{"mode": "val", "epoch": 668, "iter": 10, "lr": 0.16602, "accuracy_top-1": 63.5, "accuracy_top-5": 99.33333}
{"mode": "val", "epoch": 669, "iter": 10, "lr": 0.16552, "accuracy_top-1": 64.5, "accuracy_top-5": 99.5}
{"mode": "val", "epoch": 670, "iter": 10, "lr": 0.16502, "accuracy_top-1": 68.66667, "accuracy_top-5": 99.66667}
{"mode": "val", "epoch": 671, "iter": 10, "lr": 0.16452, "accuracy_top-1": 63.5, "accuracy_top-5": 100.0}
{"mode": "val", "epoch": 672, "iter": 10, "lr": 0.16402, "accuracy_top-1": 66.83333, "accuracy_top-5": 99.5}
{"mode": "val", "epoch": 673, "iter": 10, "lr": 0.16352, "accuracy_top-1": 69.16667, "accuracy_top-5": 99.83333}
{"mode": "val", "epoch": 674, "iter": 10, "lr": 0.16302, "accuracy_top-1": 63.83333, "accuracy_top-5": 99.5}
{"mode": "val", "epoch": 675, "iter": 10, "lr": 0.16252, "accuracy_top-1": 67.83333, "accuracy_top-5": 99.5}
{"mode": "val", "epoch": 676, "iter": 10, "lr": 0.16202, "accuracy_top-1": 66.5, "accuracy_top-5": 99.5}
{"mode": "val", "epoch": 677, "iter": 10, "lr": 0.16152, "accuracy_top-1": 63.83333, "accuracy_top-5": 99.66667}
{"mode": "val", "epoch": 678, "iter": 10, "lr": 0.16102, "accuracy_top-1": 69.0, "accuracy_top-5": 99.66667}
{"mode": "val", "epoch": 679, "iter": 10, "lr": 0.16052, "accuracy_top-1": 67.16667, "accuracy_top-5": 99.5}
{"mode": "val", "epoch": 680, "iter": 10, "lr": 0.16002, "accuracy_top-1": 66.83333, "accuracy_top-5": 99.5}
{"mode": "val", "epoch": 681, "iter": 10, "lr": 0.15952, "accuracy_top-1": 67.33333, "accuracy_top-5": 99.66667}
{"mode": "val", "epoch": 682, "iter": 10, "lr": 0.15902, "accuracy_top-1": 60.5, "accuracy_top-5": 99.83333}
{"mode": "val", "epoch": 683, "iter": 10, "lr": 0.15852, "accuracy_top-1": 65.33333, "accuracy_top-5": 99.83333}
{"mode": "val", "epoch": 684, "iter": 10, "lr": 0.15802, "accuracy_top-1": 69.16667, "accuracy_top-5": 99.83333}
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{"mode": "val", "epoch": 989, "iter": 10, "lr": 0.00552, "accuracy_top-1": 71.16667, "accuracy_top-5": 99.66667}
{"mode": "val", "epoch": 990, "iter": 10, "lr": 0.00502, "accuracy_top-1": 71.33333, "accuracy_top-5": 99.66667}
{"mode": "val", "epoch": 991, "iter": 10, "lr": 0.00452, "accuracy_top-1": 71.16667, "accuracy_top-5": 99.66667}
{"mode": "val", "epoch": 992, "iter": 10, "lr": 0.00402, "accuracy_top-1": 70.5, "accuracy_top-5": 99.66667}
{"mode": "val", "epoch": 993, "iter": 10, "lr": 0.00352, "accuracy_top-1": 70.16667, "accuracy_top-5": 99.66667}
{"mode": "val", "epoch": 994, "iter": 10, "lr": 0.00302, "accuracy_top-1": 70.0, "accuracy_top-5": 99.66667}
{"mode": "val", "epoch": 995, "iter": 10, "lr": 0.00252, "accuracy_top-1": 70.5, "accuracy_top-5": 99.66667}
{"mode": "val", "epoch": 996, "iter": 10, "lr": 0.00202, "accuracy_top-1": 70.66667, "accuracy_top-5": 99.66667}
{"mode": "val", "epoch": 997, "iter": 10, "lr": 0.00152, "accuracy_top-1": 70.5, "accuracy_top-5": 99.66667}
{"mode": "val", "epoch": 998, "iter": 10, "lr": 0.00102, "accuracy_top-1": 71.16667, "accuracy_top-5": 99.66667}
{"mode": "val", "epoch": 999, "iter": 10, "lr": 0.00052, "accuracy_top-1": 70.5, "accuracy_top-5": 99.66667}
{"mode": "val", "epoch": 1000, "iter": 10, "lr": 2e-05, "accuracy_top-1": 71.33333, "accuracy_top-5": 99.66667}
mzr1996 commented 3 years ago

Hello, here is a question, when you resume from the 884 epoch, the learning rate is 0.32301, but when you resume from the 600 epoch, the learning rate is 0.19952, while the learning rate of 884 epoch is 0.05802. So are they from two different training config files?

lukomski commented 3 years ago

You are right. I didn't know that changing max_epochs variable in config makes such influence on process of learning. That was the reason. When i don't change anything in config file during learning process the results are as expected.

In the following example learning was reasumed from epochs: 916, 1076, 2792, 4677, 4995

Figure_3