text: optimizer = dict(
      type='SGD',
      lr=0.01,
      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=5,
      num_last_epochs=15,
      min_lr_ratio=0.05)
  runner = dict(type='EpochBasedRunner', max_epochs=12)
  checkpoint_config = dict(interval=4)
  log_config = dict(interval=50, hooks=[dict(type='TextLoggerHook')])
  custom_hooks = [
      dict(type='YOLOXModeSwitchHook', num_last_epochs=15, priority=48),
      dict(
          type='SyncRandomSizeHook',
          ratio_range=(10, 20),
          img_scale=(320, 320),
          priority=48),
      dict(type='SyncNormHook', num_last_epochs=15, interval=10, priority=48),
      dict(type='ExpMomentumEMAHook', resume_from=None, priority=49)
  ]
  dist_params = dict(backend='nccl')
  log_level = 'INFO'
  load_from = './checkpoints/yolox_tiny_8x8_300e_coco_20210806_234250-4ff3b67e.pth'
  resume_from = None
  workflow = [('train', 1)]
  model = dict(
      type='YOLOX',
      backbone=dict(type='CSPDarknet', deepen_factor=0.33, widen_factor=0.375),
      neck=dict(
          type='YOLOXPAFPN',
          in_channels=[96, 192, 384],
          out_channels=96,
          num_csp_blocks=1),
      bbox_head=dict(
          type='YOLOXHead', num_classes=5, in_channels=96, feat_channels=96),
      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)))
  data_root = 'data/coco/'
  dataset_type = 'CocoDataset'
  img_norm_cfg = dict(
      mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
  img_scale = (320, 320)
  train_pipeline = [
      dict(type='Mosaic', img_scale=(320, 320), pad_val=84),
      dict(
          type='RandomAffine',
          scaling_ratio_range=(0.5, 1.5),
          border=(-320, -320)),
      dict(
          type='PhotoMetricDistortion',
          brightness_delta=32,
          contrast_range=(0.5, 1.5),
          saturation_range=(0.5, 1.5),
          hue_delta=18),
      dict(type='RandomFlip', flip_ratio=0.5),
      dict(type='Resize', keep_ratio=True),
      dict(type='Pad', pad_to_square=True, pad_val=84),
      dict(
          type='Normalize',
          mean=[123.675, 116.28, 103.53],
          std=[58.395, 57.12, 57.375],
          to_rgb=True),
      dict(type='DefaultFormatBundle'),
      dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
  ]
  train_dataset = dict(
      type='MultiImageMixDataset',
      dataset=dict(
          type='CocoDataset',
          ann_file=
          '/home/kkb/mmdetection-master/Datasets/trainData/annotations/instances_train_images.json',
          img_prefix='/home/kkb/mmdetection-master/Datasets/trainData/images/val',
          pipeline=[
              dict(type='LoadImageFromFile', to_float32=True),
              dict(type='LoadAnnotations', with_bbox=True)
          ],
          filter_empty_gt=False),
      pipeline=[
          dict(type='Mosaic', img_scale=(320, 320), pad_val=84),
          dict(
              type='RandomAffine',
              scaling_ratio_range=(0.5, 1.5),
              border=(-320, -320)),
          dict(
              type='PhotoMetricDistortion',
              brightness_delta=32,
              contrast_range=(0.5, 1.5),
              saturation_range=(0.5, 1.5),
              hue_delta=18),
          dict(type='RandomFlip', flip_ratio=0.5),
          dict(type='Resize', keep_ratio=True),
          dict(type='Pad', pad_to_square=True, pad_val=84),
          dict(
              type='Normalize',
              mean=[123.675, 116.28, 103.53],
              std=[58.395, 57.12, 57.375],
              to_rgb=True),
          dict(type='DefaultFormatBundle'),
          dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
      ],
      dynamic_scale=(320, 320))
  test_pipeline = [
      dict(type='LoadImageFromFile'),
      dict(
          type='MultiScaleFlipAug',
          img_scale=(300, 300),
          flip=False,
          transforms=[
              dict(type='Resize', keep_ratio=True),
              dict(type='RandomFlip'),
              dict(type='Pad', size=(300, 300), pad_val=84),
              dict(
                  type='Normalize',
                  mean=[123.675, 116.28, 103.53],
                  std=[58.395, 57.12, 57.375],
                  to_rgb=True),
              dict(type='DefaultFormatBundle'),
              dict(type='Collect', keys=['img'])
          ])
  ]
  data = dict(
      samples_per_gpu=8,
      workers_per_gpu=2,
      train=dict(
          type='MultiImageMixDataset',
          dataset=dict(
              type='CocoDataset',
              ann_file=
              '/home/kkb/mmdetection-master/Datasets/trainData/annotations/instances_train_images.json',
              img_prefix=
              '/home/kkb/mmdetection-master/Datasets/trainData/images/train',
              pipeline=[
                  dict(type='LoadImageFromFile', to_float32=True),
                  dict(type='LoadAnnotations', with_bbox=True)
              ],
              filter_empty_gt=False),
          pipeline=[
              dict(type='Mosaic', img_scale=(320, 320), pad_val=84),
              dict(
                  type='RandomAffine',
                  scaling_ratio_range=(0.5, 1.5),
                  border=(-320, -320)),
              dict(
                  type='PhotoMetricDistortion',
                  brightness_delta=32,
                  contrast_range=(0.5, 1.5),
                  saturation_range=(0.5, 1.5),
                  hue_delta=18),
              dict(type='RandomFlip', flip_ratio=0.5),
              dict(type='Resize', keep_ratio=True),
              dict(type='Pad', pad_to_square=True, pad_val=84),
              dict(
                  type='Normalize',
                  mean=[123.675, 116.28, 103.53],
                  std=[58.395, 57.12, 57.375],
                  to_rgb=True),
              dict(type='DefaultFormatBundle'),
              dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
          ],
          dynamic_scale=(320, 320)),
      val=dict(
          type='CocoDataset',
          ann_file=
          '/home/kkb/mmdetection-master/Datasets/trainData/annotations/instances_val_images.json',
          img_prefix='/home/kkb/mmdetection-master/Datasets/trainData/images/val',
          pipeline=[
              dict(type='LoadImageFromFile'),
              dict(
                  type='MultiScaleFlipAug',
                  img_scale=(300, 300),
                  flip=False,
                  transforms=[
                      dict(type='Resize', keep_ratio=True),
                      dict(type='RandomFlip'),
                      dict(type='Pad', size=(300, 300), pad_val=84),
                      dict(
                          type='Normalize',
                          mean=[123.675, 116.28, 103.53],
                          std=[58.395, 57.12, 57.375],
                          to_rgb=True),
                      dict(type='DefaultFormatBundle'),
                      dict(type='Collect', keys=['img'])
                  ])
          ]),
      test=dict(
          type='CocoDataset',
          ann_file=
          '/home/kkb/mmdetection-master/Datasets/trainData/annotations/instances_val_images.json',
          img_prefix='/home/kkb/mmdetection-master/Datasets/trainData/images/val',
          pipeline=[
              dict(type='LoadImageFromFile'),
              dict(
                  type='MultiScaleFlipAug',
                  img_scale=(300, 300),
                  flip=False,
                  transforms=[
                      dict(type='Resize', keep_ratio=True),
                      dict(type='RandomFlip'),
                      dict(type='Pad', size=(300, 300), pad_val=84),
                      dict(
                          type='Normalize',
                          mean=[123.675, 116.28, 103.53],
                          std=[58.395, 57.12, 57.375],
                          to_rgb=True),
                      dict(type='DefaultFormatBundle'),
                      dict(type='Collect', keys=['img'])
                  ])
          ]))
  interval = 10
  evaluation = dict(interval=10, metric='bbox')
  work_dir = './work_dirs/yolox_tiny_8x8_300e_coco'
  gpu_ids = range(0, 1)
  