| auto_scale_lr = dict(base_batch_size=96) | |
| custom_hooks = [ | |
| dict(momentum=0.0001, priority='ABOVE_NORMAL', type='EMAHook'), | |
| ] | |
| data_preprocessor = dict( | |
| mean=[ | |
| 123.675, | |
| 116.28, | |
| 103.53, | |
| ], | |
| num_classes=2, | |
| std=[ | |
| 58.395, | |
| 57.12, | |
| 57.375, | |
| ], | |
| to_rgb=True) | |
| dataset_type = 'CustomDataset' | |
| default_hooks = dict( | |
| checkpoint=dict(interval=2, type='CheckpointHook'), | |
| logger=dict(interval=100, type='LoggerHook'), | |
| param_scheduler=dict(type='ParamSchedulerHook'), | |
| sampler_seed=dict(type='DistSamplerSeedHook'), | |
| timer=dict(type='IterTimerHook'), | |
| visualization=dict( | |
| enable=True, | |
| interval=1, | |
| out_dir=None, | |
| type='VisualizationHook', | |
| wait_time=2)) | |
| default_scope = 'mmpretrain' | |
| env_cfg = dict( | |
| cudnn_benchmark=False, | |
| dist_cfg=dict(backend='nccl'), | |
| mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) | |
| launcher = 'none' | |
| load_from = './ConvNeXt_v2-v2_ep90.pth' | |
| log_level = 'INFO' | |
| model = dict( | |
| backbone=dict( | |
| arch='tiny', | |
| drop_path_rate=0.5, | |
| layer_scale_init_value=0.0, | |
| type='ConvNeXt', | |
| use_grn=True), | |
| head=dict( | |
| in_channels=768, | |
| init_cfg=None, | |
| loss=dict(label_smooth_val=0.2, type='LabelSmoothLoss'), | |
| num_classes=2, | |
| type='LinearClsHead'), | |
| init_cfg=dict( | |
| bias=0.0, layer=[ | |
| 'Conv2d', | |
| 'Linear', | |
| ], std=0.02, type='TruncNormal'), | |
| train_cfg=dict(augments=[ | |
| dict(alpha=0.8, type='Mixup'), | |
| dict(alpha=1.0, type='CutMix'), | |
| ]), | |
| type='ImageClassifier') | |
| optim_wrapper = dict( | |
| accumulative_counts=3, | |
| clip_grad=None, | |
| loss_scale='dynamic', | |
| optimizer=dict( | |
| betas=( | |
| 0.9, | |
| 0.999, | |
| ), | |
| eps=1e-08, | |
| lr=0.00032, | |
| type='AdamW', | |
| weight_decay=0.05), | |
| paramwise_cfg=dict( | |
| bias_decay_mult=0.0, | |
| custom_keys=dict({ | |
| '.absolute_pos_embed': dict(decay_mult=0.0), | |
| '.relative_position_bias_table': dict(decay_mult=0.0) | |
| }), | |
| flat_decay_mult=0.0, | |
| norm_decay_mult=0.0), | |
| type='AmpOptimWrapper') | |
| param_scheduler = [ | |
| dict( | |
| by_epoch=True, | |
| convert_to_iter_based=True, | |
| end=2, | |
| start_factor=0.001, | |
| type='LinearLR'), | |
| dict(begin=2, by_epoch=True, eta_min=8e-05, type='CosineAnnealingLR'), | |
| ] | |
| randomness = dict(deterministic=False, seed=None) | |
| resume = False | |
| test_cfg = dict() | |
| test_dataloader = dict( | |
| batch_size=16, | |
| collate_fn=dict(type='default_collate'), | |
| dataset=dict( | |
| data_root='./testimgs', | |
| pipeline=[ | |
| dict(type='LoadImageFromFile'), | |
| dict( | |
| backend='pillow', | |
| interpolation='bicubic', | |
| scale=384, | |
| type='Resize'), | |
| dict(type='PackInputs'), | |
| ], | |
| type='CustomDataset'), | |
| num_workers=5, | |
| persistent_workers=True, | |
| pin_memory=True, | |
| sampler=dict(shuffle=False, type='DefaultSampler')) | |
| test_evaluator = dict(topk=(1, ), type='Accuracy') | |
| test_pipeline = [ | |
| dict(type='LoadImageFromFile'), | |
| dict(backend='pillow', interpolation='bicubic', scale=384, type='Resize'), | |
| dict(type='PackInputs'), | |
| ] | |
| train_cfg = dict(by_epoch=True, max_epochs=120, val_interval=1) | |
| train_dataloader = dict( | |
| batch_size=32, | |
| collate_fn=dict(type='default_collate'), | |
| dataset=dict( | |
| data_root='./procset', | |
| pipeline=[ | |
| dict(type='LoadImageFromFile'), | |
| dict( | |
| backend='pillow', | |
| interpolation='bicubic', | |
| scale=384, | |
| type='RandomResizedCrop'), | |
| dict(direction='horizontal', prob=0.5, type='RandomFlip'), | |
| dict(type='PackInputs'), | |
| ], | |
| type='CustomDataset'), | |
| num_workers=5, | |
| persistent_workers=True, | |
| pin_memory=True, | |
| sampler=dict(shuffle=True, type='DefaultSampler')) | |
| train_pipeline = [ | |
| dict(type='LoadImageFromFile'), | |
| dict( | |
| backend='pillow', | |
| interpolation='bicubic', | |
| scale=384, | |
| type='RandomResizedCrop'), | |
| dict(direction='horizontal', prob=0.5, type='RandomFlip'), | |
| dict(type='PackInputs'), | |
| ] | |
| val_cfg = dict() | |
| val_dataloader = dict( | |
| batch_size=16, | |
| collate_fn=dict(type='default_collate'), | |
| dataset=dict( | |
| data_root='./valset', | |
| pipeline=[ | |
| dict(type='LoadImageFromFile'), | |
| dict( | |
| backend='pillow', | |
| interpolation='bicubic', | |
| scale=384, | |
| type='Resize'), | |
| dict(type='PackInputs'), | |
| ], | |
| type='CustomDataset'), | |
| num_workers=5, | |
| persistent_workers=True, | |
| pin_memory=True, | |
| sampler=dict(shuffle=False, type='DefaultSampler')) | |
| val_evaluator = dict(topk=(1, ), type='Accuracy') | |
| vis_backends = [ | |
| dict(type='LocalVisBackend'), | |
| ] | |
| visualizer = dict( | |
| type='UniversalVisualizer', vis_backends=[ | |
| dict(type='LocalVisBackend'), | |
| ]) | |
| work_dir = './work_dirs\\convnext-v2-tiny_32xb32_in1k-384px' | |