无脑016——使用mmdetection训练自己的数据集,使用labelme标注的coco格式

标注的数据集在上一篇文章介绍过了,先使用labelme软件标注好,然后使用代码,生成一个汇总的json文件
http://t.csdnimg.cn/oKqDD

一.我的环境如下:

(mmdet300rc5_torch171) PS G:\bsh\mmdet300rc5_torch171> python
Python 3.8.18 (default, Sep 11 2023, 13:39:12) [MSC v.1916 64 bit (AMD64)] :: Anaconda, Inc. on win32
Type "help", "copyright", "credits" or "license" for more information.
>>> import torch
>>> import torchvision
>>> torch.__version__
'1.7.1'
>>> torchvision.__version__
'0.8.2'
>>> torch.version.cuda
'10.1'
>>> torch.cuda.get_device_name(0) 
'NVIDIA GeForce GTX 1060'
>>> quit()
(mmdet300rc5_torch171) PS G:\bsh\mmdet300rc5_torch171> mim list
Package    Version    Source
---------  ---------  ----------------------------------------------------------------------------
mmcv       2.0.0rc4   https://github.com/open-mmlab/mmcv
mmdet      3.2.0      f:\app\miniconda\envs\mmdet_torch171\lib\site-packages\mmdet-3.2.0-py3.8.egg
mmengine   0.9.0      https://github.com/open-mmlab/mmengine

二、接下来将数据集导入mmdetection进行训练

1、数据集文件夹格式:

G:\bsh\mmdet300rc5_torch171\data\flame_coco\annotations\instances_train.json
G:\bsh\mmdet300rc5_torch171\data\flame_coco\annotations\instances_val.json
G:\bsh\mmdet300rc5_torch171\data\flame_coco\images\train
G:\bsh\mmdet300rc5_torch171\data\flame_coco\images\val

2、修改configs\rtmdet\rtmdet_l_8xb32-300e_coco.py的

num_classes=2batch_size=12num_workers=1(这个必须改为1,windows不支持多线程训练)、
max_epochs=30base_lr=0.004interval=10pin_memory=False,(这个必须改为False,windows不支持多线程训练)

3、修改mmdet\datasets\coco.py中的classes

@DATASETS.register_module()
class CocoDataset(BaseDetDataset):
    """Dataset for COCO."""

    METAINFO = {
        'classes':
        ('ok2', 'ok10'),
        # palette is a list of color tuples, which is used for visualization.
        'palette':
        [(220, 20, 60), (119, 11, 32), (0, 0, 142), (0, 0, 230), (106, 0, 228),
         (0, 60, 100), (0, .......................

4、修改mmdet/evaluation/functional/class_names.py

def coco_classes():
    return [
        'ok2', 'ok10'
    ]

5、修改configs/base/datasets/coco_detection.py的路径

# dataset settings
dataset_type = 'CocoDataset'
data_root = 'data/flame_coco/'

# file_client_args = dict(
#     backend='petrel',
#     path_mapping=dict({
#         './data/': 's3://openmmlab/datasets/detection/',
#         'data/': 's3://openmmlab/datasets/detection/'
#     }))
file_client_args = dict(backend='disk')

train_pipeline = [
    dict(type='LoadImageFromFile', file_client_args=file_client_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', file_client_args=file_client_args),
    dict(type='Resize', scale=(1333, 800), keep_ratio=True),
    # If you don't have a gt annotation, delete the pipeline
    dict(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,
        ann_file='annotations/instances_train.json',
        data_prefix=dict(img='images/train/'),
        filter_cfg=dict(filter_empty_gt=True, min_size=32),
        pipeline=train_pipeline))
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,
        ann_file='annotations/instances_val.json',
        data_prefix=dict(img='images/val/'),
        test_mode=True,
        pipeline=test_pipeline))
test_dataloader = val_dataloader

val_evaluator = dict(
    type='CocoMetric',
    ann_file=data_root + 'annotations/instances_val.json',
    metric='bbox',
    format_only=False)
test_evaluator = val_evaluator


6、运行代码开始训练

python tools/train.py configs\rtmdet\rtmdet_s_8xb32-300e_coco.py

7、运行推理代码:

python demo/image_demo.py demo/00000.bmp configs/rtmdet/rtmdet_s_8xb32-300e_coco.py --weights work_dirs/rtmdet_s_8xb32-300e_coco/epoch_300.pth --show

你可能感兴趣的:(python)