TypeError: __init__() got an unexpected keyword argument 'ann_file'

[miahh1103]

hi,I use my own dataset for training，but i got this error:
Traceback (most recent call last):
File "mmdetection-3.x/tools/train.py", line 133, in
main()
File "mmdetection-3.x/tools/train.py", line 129, in main
runner.train()
File "C:\Users\dmvns00007.conda\envs\openmmlab\lib\site-packages\mmengine\runner\runner.py", line 1742, in train self._val_loop = self.build_val_loop(
File "C:\Users\dmvns00007.conda\envs\openmmlab\lib\site-packages\mmengine\runner\runner.py", line 1562, in build_val_loop
loop = LOOPS.build(
File "C:\Users\dmvns00007.conda\envs\openmmlab\lib\site-packages\mmengine\registry\registry.py", line 570, in build
return self.build_func(cfg, *args, **kwargs, registry=self)
File "C:\Users\dmvns00007.conda\envs\openmmlab\lib\site-packages\mmengine\registry\build_functions.py", line 121, in build_from_cfg
obj = obj_cls(**args) # type: ignore
File "C:\Users\dmvns00007.conda\envs\openmmlab\lib\site-packages\mmengine\runner\loops.py", line 348, in init
self.evaluator = runner.build_evaluator(evaluator) # type: ignore
File "C:\Users\dmvns00007.conda\envs\openmmlab\lib\site-packages\mmengine\runner\runner.py", line 1318, in build_evaluator
return Evaluator(evaluator) # type: ignore
File "C:\Users\dmvns00007.conda\envs\openmmlab\lib\site-packages\mmengine\evaluator\evaluator.py", line 25, in init
self.metrics.append(METRICS.build(metric))
File "C:\Users\dmvns00007.conda\envs\openmmlab\lib\site-packages\mmengine\registry\registry.py", line 570, in build
return self.build_func(cfg, *args, **kwargs, registry=self)
File "C:\Users\dmvns00007.conda\envs\openmmlab\lib\site-packages\mmengine\registry\build_functions.py", line 121, in build_from_cfg
obj = obj_cls(**args) # type: ignore
TypeError: init() got an unexpected keyword argument 'ann_file'

This is my config:

_base_ = './rtmdet_s_8xb32-300e_coco.py'

# Load pre-trained weights
load_from = r'Z:\\300_Minh_Anh\\train_rtmdet\\rtmdet_s_8xb32-300e_coco_20220905_161602-387a891e.pth'

# Data root directory
data_root = r'Z:\\300_Minh_Anh\\train_rtmdet\\voc_data'

# Custom dataset metadata
metainfo = {
    'classes': ('NG',),
    'palette': [(220, 20, 60)],
}


# Training pipeline
train_pipeline = [
    dict(type='LoadImageFromFile', backend_args={{_base_.backend_args}}),
    dict(type='LoadAnnotations', with_bbox=True),
    dict(type='Resize', scale=(1536, 1024), keep_ratio=True),
    dict(type='Pad', pad_to_square=True, pad_val=0),
    dict(type='Normalize', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=False),
    dict(type='PackDetInputs')
]

# Validation pipeline
val_pipeline = [
    dict(type='LoadImageFromFile', backend_args={{_base_.backend_args}}),
    dict(type='LoadAnnotations', with_bbox=True),
    dict(type='Resize', scale=(1536, 1024), keep_ratio=True),
    dict(type='Pad', pad_to_square=True, pad_val=0),
    dict(type='Normalize', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=False),
    dict(type='PackDetInputs')
]


# Data loaders
train_dataloader = dict(
    batch_size=2,
    num_workers=2,
    dataset=dict(
        type='VOCDataset',
        data_root=data_root,
        metainfo=metainfo,
        ann_file='ImageSets/train.txt',
        data_prefix=dict(img='JPEGImages/'),
        pipeline=train_pipeline,
        _delete_=True
    )
)

val_dataloader = dict(
    batch_size=2,
    num_workers=2,
    dataset=dict(
        type='VOCDataset',
        data_root=data_root,
        metainfo=metainfo,
        ann_file='ImageSets/dev.txt',
        data_prefix=dict(img='JPEGImages/'),
        pipeline=val_pipeline,
        _delete_=True
    )
)

test_dataloader = dict(
    batch_size=2,
    num_workers=2,
    dataset=dict(
        type='VOCDataset',
        data_root=data_root,
        metainfo=metainfo,
        ann_file='ImageSets/test.txt',
        data_prefix=dict(img='JPEGImages/'),
        _delete_=True
    )
)



# Model configuration
model = dict(
    bbox_head=dict(
        num_classes=1,
        init_cfg=dict(
            type='Pretrained',
            checkpoint=load_from,
            prefix='bbox_head',
            map_location='gpu',
            strict=False
        )
    ))

# Optimizer settings
optim_wrapper = dict(
    type='OptimWrapper',
    optimizer=dict(type='AdamW', lr=0.0001, weight_decay=0.0001)
)

# Learning rate scheduler
param_scheduler = [
    dict(type='LinearLR', start_factor=1.0e-5, by_epoch=False, begin=0, end=10),
    dict(type='CosineAnnealingLR', eta_min=0.0, begin=10, end=300, T_max=300, by_epoch=True),
]
# Validation evaluator
val_evaluator = dict(
    type='VOCMetric',
    ann_file=data_root + '/ImageSets/val.txt',
    metric='bbox',
    format_only=False,
    proposal_nums=(100, 1, 10),
    backend_args=None
)
# Training configuration
train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=100, val_interval=1)

Please help me, thank you all