1 库安装

        pip install timm==1.0.7 thop efficientnet_pytorch==0.7.1 einops grad-cam==1.4.8 dill==0.3.6 albumentations==1.4.11 pytorch_wavelets==1.3.0 tidecv PyWavelets -i https://pypi.tuna.tsinghua.edu.cn/simplepip install -U openmim -i https://pypi.tuna.tsinghua.edu.cn/simplemim install mmengine -i https://pypi.tuna.tsinghua.edu.cn/simplemim install "mmcv==2.1.0" -i https://pypi.tuna.tsinghua.edu.cn/simple

pip install YOLO
pip install ultralytics

*需要注意mmcv库的兼容性。

2 源码下载

GitHub - open-mmlab/mmdetection at v3.0.0

3 数据集准备

我是yolo数据集转coco

使用时需要修改类别和路径。

import json
import os
import shutilimport cv2# info ，license，categories 结构初始化；
# 在train.json,val.json,test.json里面信息是一致的；# info，license暂时用不到
info = {"year": 2024,"version": '1.0',"date_created": 2024 - 6 - 9
}licenses = {"id": 1,"name": "null","url": "null",
}# 自己的标签类别，跟yolo的数据集类别要对应好；
categories = [{"id": 0,"name": 'L',"supercategory": 'lines',},{"id": 1,"name": 'R',"supercategory": 'lines',},{"id": 2,"name": 'I',"supercategory": 'lines',},{"id": 3,"name": 'M',"supercategory": 'lines',},{"id": 4,"name": 'A',"supercategory": 'lines',}]# 初始化train,test、valid 数据字典
# info licenses categories 在train和test里面都是一致的；
train_data = {'info': info, 'licenses': licenses, 'categories': categories, 'images': [], 'annotations': []}
test_data = {'info': info, 'licenses': licenses, 'categories': categories, 'images': [], 'annotations': []}
valid_data = {'info': info, 'licenses': licenses, 'categories': categories, 'images': [], 'annotations': []}# image_path 对应yolov8的图像路径，比如images/train；
# label_path 对应yolov8的label路径，比如labels/train 跟images要对应；
def yolo_covert_coco_format(image_path, label_path):images = []annotations = []for index, img_file in enumerate(os.listdir(image_path)):if img_file.endswith('.jpg'):image_info = {}img = cv2.imread(os.path.join(image_path, img_file))height, width, channel = img.shapeimage_info['id'] = indeximage_info['file_name'] = img_fileimage_info['width'], image_info['height'] = width, heightelse:continueif image_info != {}:images.append(image_info)# 处理label信息-------label_file = os.path.join(label_path, img_file.replace('.jpg', '.txt'))with open(label_file, 'r') as f:for idx, line in enumerate(f.readlines()):info_annotation = {}class_num, xs, ys, ws, hs = line.strip().split(' ')class_id, xc, yc, w, h = int(class_num), float(xs), float(ys), float(ws), float(hs)xmin = (xc - w / 2) * widthymin = (yc - h / 2) * heightxmax = (xc + w / 2) * widthymax = (yc + h / 2) * heightbbox_w = int(width * w)bbox_h = int(height * h)img_copy = img[int(ymin):int(ymax), int(xmin):int(xmax)].copy()info_annotation["category_id"] = class_id  # 类别的idinfo_annotation['bbox'] = [xmin, ymin, bbox_w, bbox_h]  ## bbox的坐标info_annotation['area'] = bbox_h * bbox_w  ###areainfo_annotation['image_id'] = index  # bbox的idinfo_annotation['id'] = index * 100 + idx  # bbox的id# cv2.imwrite(f"./temp/{info_annotation['id']}.jpg", img_copy)info_annotation['segmentation'] = [[xmin, ymin, xmax, ymin, xmax, ymax, xmin, ymax]]  # 四个点的坐标info_annotation['iscrowd'] = 0  # 单例annotations.append(info_annotation)return images, annotations# key == train，test，val
# 对应要生成的json文件，比如instances_train.json，instances_test.json，instances_val.json
# 只是为了不重复写代码。。。。。
def gen_json_file(yolov8_data_path, coco_format_path, key):# json pathjson_path = os.path.join(coco_format_path, f'annotations/instances_{key}.json')dst_path = os.path.join(coco_format_path, f'{key}')if not os.path.exists(os.path.dirname(json_path)):os.makedirs(os.path.dirname(json_path), exist_ok=True)data_path = os.path.join(yolov8_data_path, f'images/{key}')label_path = os.path.join(yolov8_data_path, f'labels/{key}')images, anns = yolo_covert_coco_format(data_path, label_path)if key == 'train':train_data['images'] = imagestrain_data['annotations'] = annswith open(json_path, 'w') as f:json.dump(train_data, f, indent=2)# shutil.copy(data_path,'')elif key == 'test':test_data['images'] = imagestest_data['annotations'] = annswith open(json_path, 'w') as f:json.dump(test_data, f, indent=2)elif key == 'val':valid_data['images'] = imagesvalid_data['annotations'] = annswith open(json_path, 'w') as f:json.dump(valid_data, f, indent=2)else:print(f'key is {key}')print(f'generate {key} json success!')returnif __name__ == '__main__':yolov8_data_path = r''coco_format_path = r''gen_json_file(yolov8_data_path, coco_format_path, key='train')gen_json_file(yolov8_data_path, coco_format_path, key='val')gen_json_file(yolov8_data_path, coco_format_path, key='test')

coco标签可视化工具，可以用于检查数据集是否有问题

import matplotlib.pyplot as plt
import matplotlib.patches as patches
from pycocotools.coco import COCO
import numpy as np
import skimage.io as io
import os
# 定义COCO数据集的路径
dataDir = r''
dataType = 'train2017'
annFile = f'{dataDir}/annotations/instances_{dataType}.json'# 初始化COCO API
coco = COCO(annFile)# 获取一张图片的ID
imgIds = coco.getImgIds()
img = coco.loadImgs(imgIds[np.random.randint(0, len(imgIds))])[0]# 加载并显示图片
I = io.imread(f'{dataDir}/{dataType}/{img["file_name"]}')
plt.imshow(I)
plt.axis('off')# 获取图片中的标注
annIds = coco.getAnnIds(imgIds=img['id'], iscrowd=None)
anns = coco.loadAnns(annIds)# 显示标注和类别标签
for ann in anns:bbox = ann['bbox']category_id = ann['category_id']category_name = coco.loadCats(category_id)[0]['name']  # 获取类别名称# 画出边框rect = patches.Rectangle((bbox[0], bbox[1]), bbox[2], bbox[3], linewidth=2, edgecolor='r', facecolor='none')plt.gca().add_patch(rect)# 在边框上方显示类别名称plt.text(bbox[0], bbox[1] - 10, category_name, color='yellow', fontsize=12, weight='bold', backgroundcolor='black')# 确保输出目录存在output_path = f'./{img["file_name"]}_annotated.jpg'
plt.savefig(output_path, bbox_inches='tight', pad_inches=0.1)
plt.show()

4 代码修改

命令行进入代码文件夹，然后输入指令

pip install -v -e.

根据你想要跑什么模型，以faster rcnn为例。

你需要修改faster-rcnn_r50_fpn_1x.py ,classes_name.py ,coco.py, coco_detection.py 和shedule_1x.py中的数据集参数和训练参数。例如类别名，类别数，数据集路径，epoches和batch等。

这些文件的路径

mmdetection-3.0.0/configs/_base_/schedules/schedule_1x.py

mmdetection-3.0.0/configs/_base_/datasets/coco_detection.py

mmdetection-3.0.0/configs/_base_/models/faster-rcnn_r50_fpn.py

mmdetection-3.0.0/mmdet/datasets/coco.py

5 运行

python tools/train.py  configs/_base_/models/faster-rcnn_r50_fpn.py

或 

python tools/train.py work_dirs/faster-rcnn_r50_fpn_1x_coco/faster-rcnn_r50_fpn_1x_coco.py

后续若需要修改参数可以直接在faster-rcnn_r50_fpn_1x_coco.py中修改。

6 测试

python tools/test.py work_dirs/faster-rcnn_r50_fpn_1x_coco/faster-rcnn_r50_fpn_1x_coco.py work_dirs/faster-rcnn_r50_fpn_1x_coco/epoch_20.pth --show-dir out