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

[miahh1103]

I train the model on my custom dataset
Here is my config file:

_base_ = [
    'Z:/300_Minh_Anh/train_rtmdet/mmdetection-3.x/configs/rtmdet/rtmdet_s_8xb32-300e_coco.py',
]

experiment_id = 'rtmdet_s_8xb32-300e_coco'

classes = ('NG',)  
num_classes = len(classes)
metainfo = {
    'classes': classes,
    'palette': [(190, 77, 37)]
}

model = dict(
    data_preprocessor=dict(
        type='DetDataPreprocessor',
        mean=[123.675, 102.466, 48.287],
        std=[31.949, 31.084, 20.839],
        bgr_to_rgb=False,
        batch_augments=None,
        pad_size_divisor=32), 
    bbox_head=dict(num_classes=num_classes)
)

image_scale = (512, 672)
batch_size = 4
n_workers = 2
n_gpu = 1
base_lr = 0.004
eta = base_lr * (batch_size * n_gpu / 16)**0.5
n_epochs = 300

albu_train_transforms = [
    dict(
        type='ShiftScaleRotate',
        shift_limit=(-0.0625, 0.0625),
        scale_limit=(0.0, 0.0),
        rotate_limit=(-3.0, 3.0),
        interpolation=1,  
        p=0.5),
    dict(
        type='OneOf',
        transforms=[
            dict(type='Blur', blur_limit=(3, 5), p=1.0),
            dict(type='MedianBlur', blur_limit=(3, 5), p=1.0)
        ],
        p=0.5),
    dict(
        type='RandomBrightnessContrast',
        brightness_limit=[0.0, 0.05],
        contrast_limit=[0.0, 0.05],
        p=0.5),
    dict(
        type='OneOf',
        transforms=[
        dict(
            type='HueSaturationValue',
            hue_shift_limit=2,
            sat_shift_limit=2,
            val_shift_limit=2,
            p=1.0),
        dict(
            type='RGBShift',
            r_shift_limit=(-2, 2),
            g_shift_limit=(-2, 2),
            b_shift_limit=(-2, 2),
            p=1.0),
        ],
        p=0.5),
    dict(type='JpegCompression', quality_lower=98, quality_upper=100, p=0.5),
]
packed_inputs_items = ('img_id', 'img_path', 'img_shape', 'scale_factor', 'ori_shape')
train_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(type='LoadAnnotations', with_bbox=True, with_mask=False, with_seg=False),
    dict(type='Resize', scale=image_scale, keep_ratio=True),
    dict(
        type='Albu',
        transforms=albu_train_transforms,
        bbox_params=dict(
            type='BboxParams',
            format='pascal_voc',
            label_fields=['gt_bboxes_labels', 'gt_ignore_flags'],
            min_visibility=0.0,
            filter_lost_elements=True),
        keymap={
            'img': 'image',
            'gt_bboxes': 'bboxes'
        },
        skip_img_without_anno=True),
    dict(type='RandomFlip', prob=0.5, direction='horizontal'),
    dict(type='PackDetInputs', meta_keys=packed_inputs_items),
]
val_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(type='LoadAnnotations', with_bbox=True, with_mask=False, with_seg=False),
    dict(type='Resize', scale=image_scale, keep_ratio=True),
    dict(type='PackDetInputs', meta_keys=packed_inputs_items),
]
test_pipeline = val_pipeline

dataset_type = 'CocoDataset'
data_root = r'Z://300_Minh_Anh/train_rtmdet/dataset/'  # Ensure this path is correct
backend_args = None

base_dataset_config = {
    "type": dataset_type,
    "metainfo": metainfo,
    "data_root": data_root,
    "ann_file": 'annotations/train_label.json', 
    "backend_args": backend_args
}

train_dataset = dict(
    **base_dataset_config,
    data_prefix=dict(img='train/', ann='annotations/'),
    filter_cfg=dict(filter_empty_gt=True, min_size=32),
    pipeline=train_pipeline
)

val_dataset = dict(
    **base_dataset_config,
    data_prefix=dict(img='dev/', ann='annotations/'), 
    test_mode=True,
    pipeline=val_pipeline
)

test_dataset = dict(
    **base_dataset_config,
    data_prefix=dict(img='test/', ann='annotations/'),  #

    test_mode=True,
    pipeline=test_pipeline
)


train_dataloader = dict(
    batch_size=batch_size,
    num_workers=2,
    persistent_workers=True,
    sampler=dict(type='DefaultSampler', shuffle=True),
    dataset=train_dataset
)

val_dataloader = dict(
    batch_size=1,
    num_workers=2,
    persistent_workers=True,
    drop_last=False,
    sampler=dict(type='DefaultSampler', shuffle=False),
    dataset=val_dataset
)

test_dataloader = dict(
    batch_size=1,
    num_workers=2,
    persistent_workers=True,
    drop_last=False,
    sampler=dict(type='DefaultSampler', shuffle=False),
    dataset=test_dataset
)

val_evaluator = dict(type='CocoMetric', metric=['bbox'], eval_mode='bbox')
test_evaluator = val_evaluator

optimizer = dict(type='AdamW', lr=eta, weight_decay=0.0001)
optim_wrapper = dict(type='OptimWrapper', optimizer=optimizer)

param_scheduler = [
    dict(
        type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=1000),
    dict(
        type='MultiStepLR',
        begin=0,
        end=n_epochs,
        by_epoch=True,
        milestones=[270],
        gamma=0.1)
]

train_cfg = dict(
    type='EpochBasedTrainLoop', max_epochs=n_epochs, val_interval=1)
val_cfg = dict(type='ValLoop')
test_cfg = dict(type='TestLoop')

auto_scale_lr = dict(enable=False, base_batch_size=16)

default_hooks = dict(
    checkpoint=dict(type='CheckpointHook', interval=1, max_keep_ckpts=3),
    logger=dict(type='LoggerHook', interval=50))

vis_backends = [dict(type='LocalVisBackend'), 
                dict(type='TensorboardVisBackend')]
visualizer = dict(
    type='DetLocalVisualizer', vis_backends=vis_backends, name='visualizer')

log_processor = dict(type='LogProcessor', window_size=50, by_epoch=True)

work_dir = 'logs/' + experiment_id 

Here is my error:

Traceback (most recent call last):
  File "mmdetection-3.x/tools/train.py", line 133, in <module>
    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 1728, in train
    self._train_loop = self.build_train_loop(
  File "C:\Users\dmvns00007\.conda\envs\openmmlab\lib\site-packages\mmengine\runner\runner.py", line 1520, in build_train_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 44, in __init__
    super().__init__(runner, dataloader)
  File "C:\Users\dmvns00007\.conda\envs\openmmlab\lib\site-packages\mmengine\runner\base_loop.py", line 26, in __init__
    self.dataloader = runner.build_dataloader(
  File "C:\Users\dmvns00007\.conda\envs\openmmlab\lib\site-packages\mmengine\runner\runner.py", line 1370, in build_dataloader
    dataset = DATASETS.build(dataset_cfg)
  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\mmdetection\mmdet\datasets\base_det_dataset.py", line 51, in __init__
    super().__init__(*args, **kwargs)
TypeError: __init__() got an unexpected keyword argument 'img_prefix'

Please help me to solve the problem. I am just a beginner. Thank you all