mmdetection在训练自己数据集时候报错‘ValueError: need at least one array to concatenate’

问题：

mmdetection在训练自己数据集时候报错‘ValueError: need at least one array to concatenate’
在这里插入图片描述

解决方法：

需要修改数据集加载的代码文件，数据集文件在路径configs/base/datasets/coco_detection.py里面，需要增加metainfo这个dict，另外在train_dataloader和val_dataloader这两个dict里面将metainfo传入，使用这个语句传入metainfo = metainfo。
在这里插入图片描述

其中，metainfo这个dict里面classes是自己数据集的类别，palette是对应类别画图时候的颜色，随便设置即可。

代码举例：

# configs/_base_/datasets/coco_detection.py
# dataset settings
dataset_type = 'CocoDataset'
data_root = 'path/mmdetection/datasets/coco2017/'
# Example to use different file client
# Method 1: simply set the data root and let the file I/O module
# automatically infer from prefix (not support LMDB and Memcache yet)
# data_root = 's3://openmmlab/datasets/detection/coco/'
metainfo = dict(classes=('x', 'x1', 'x2', 'x3', 'x4'),palette=[(220, 17, 58), (0, 143, 10), (0, 143, 10), (0, 143, 10), (0, 143, 10)]  # 画图时候的颜色，随便设置即可
)# Method 2: Use `backend_args`, `file_client_args` in versions before 3.0.0rc6
# backend_args = dict(
#     backend='petrel',
#     path_mapping=dict({
#         './data/': 's3://openmmlab/datasets/detection/',
#         'data/': 's3://openmmlab/datasets/detection/'
#     }))
backend_args = None
train_pipeline = [dict(type='LoadImageFromFile', backend_args=backend_args),dict(type='LoadAnnotations', with_bbox=True),dict(type='Resize', scale=(1333, 800), keep_ratio=True),dict(type='RandomFlip', prob=0.5),dict(type='PackDetInputs')
]
test_pipeline = [dict(type='LoadImageFromFile', backend_args=backend_args),dict(type='Resize', scale=(1333, 800), keep_ratio=True),# If you don't have a gt annotation, delete the pipelinedict(type='LoadAnnotations', with_bbox=True),dict(type='PackDetInputs',meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape','scale_factor'))
]
train_dataloader = dict(batch_size=2,num_workers=2,persistent_workers=True,sampler=dict(type='DefaultSampler', shuffle=True),batch_sampler=dict(type='AspectRatioBatchSampler'),dataset=dict(type=dataset_type,data_root=data_root,metainfo = metainfo,ann_file='annotations/instances_train2017.json',data_prefix=dict(img='train2017/'),filter_cfg=dict(filter_empty_gt=True, min_size=32),pipeline=train_pipeline,backend_args=backend_args))
val_dataloader = dict(batch_size=1,num_workers=2,persistent_workers=True,drop_last=False,sampler=dict(type='DefaultSampler', shuffle=False),dataset=dict(type=dataset_type,data_root=data_root,metainfo = metainfo,ann_file='annotations/instances_val2017.json',data_prefix=dict(img='val2017/'),test_mode=True,pipeline=test_pipeline,backend_args=backend_args))
test_dataloader = val_dataloaderval_evaluator = dict(type='CocoMetric',ann_file=data_root + 'annotations/instances_val2017.json',metric='bbox',format_only=False,backend_args=backend_args)
test_evaluator = val_evaluator