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（linux/MMdetection2）faster RCNN目标检测：训练自己的coco格式数据集，单/多gpu训练与测试，笔记

news 2026/5/8 12:15:40

前言

如何在anconda创建mmdetection虚拟环境和用pycham为项目配置环境见（linux）mmdetection环境配置gpu+anaconda+pycham+ RTX2080ti 笔记
mmdetection链接 https://github.com/open-mmlab/mmdetection或mirrors / open-mmlab / mmdetection · GitCode
voc转coco参考记录一下：DETR训练自制VOC转COCO数据集的过程

数据集准备

在mmdetection文件夹下新建data文件夹，需要coco数据集格式如下(test测试集可有可无）

coco数据集位置格式 mmdetection ├── data ├── coco: 数据集根目录 ├── train2017 ├── val2017 ├── test2017 └── annotations: 对应标注文件夹 ├── instances_train2017.json ├── instances_val2017.json └── instances_test2017.json

voc转coco

1.先创建instances_train2017.json，instances_val2017.json和instances_test2017.json空文本。可通过新建txt文件再重命名修改文件扩展名创建json格式文本

2.根据对应的txt文件将xml标注文件转json，需要修改PRE_DEFINE_CATEGORIES和数据集路径

# 根据对应的txt文件将xml标注文件转json import sys import os import json import xml.etree.ElementTree as ET START_BOUNDING_BOX_ID = 1 PRE_DEFINE_CATEGORIES = {"person":1, "elephant":2, "lion":3, "giraffe":4} #修改的地方，修改为自己的类别 def get(root, name): vars = root.findall(name) return vars def get_and_check(root, name, length): vars = root.findall(name) if len(vars) == 0: raise NotImplementedError('Can not find %s in %s.' % (name, root.tag)) if length > 0 and len(vars) != length: raise NotImplementedError('The size of %s is supposed to be %d, but is %d.' % (name, length, len(vars))) if length == 1: vars = vars[0] return vars def get_filename_as_int(filename): try: filename = os.path.splitext(filename)[0] return int(filename) except: raise NotImplementedError('Filename %s is supposed to be an integer.' % (filename)) def convert(xml_list, xml_dir, json_file): list_fp = open(xml_list, 'r') json_dict = {"images": [], "type": "instances", "annotations": [], "categories": []} categories = PRE_DEFINE_CATEGORIES bnd_id = START_BOUNDING_BOX_ID for line in list_fp: line = line.strip() line = line + ".xml" print("Processing %s" % (line)) xml_f = os.path.join(xml_dir, line) tree = ET.parse(xml_f) root = tree.getroot() path = get(root, 'path') if len(path) == 1: filename = os.path.basename(path[0].text) elif len(path) == 0: filename = get_and_check(root, 'filename', 1).text else: raise NotImplementedError('%d paths found in %s' % (len(path), line)) ## The filename must be a number image_id = get_filename_as_int(filename) size = get_and_check(root, 'size', 1) width = int(get_and_check(size, 'width', 1).text) height = int(get_and_check(size, 'height', 1).text) image = {'file_name': filename, 'height': height, 'width': width, 'id': image_id} json_dict['images'].append(image) ## Cruuently we do not support segmentation # segmented = get_and_check(root, 'segmented', 1).text # assert segmented == '0' for obj in get(root, 'object'): category = get_and_check(obj, 'name', 1).text if category not in categories: new_id = len(categories) categories[category] = new_id category_id = categories[category] bndbox = get_and_check(obj, 'bndbox', 1) xmin = int(get_and_check(bndbox, 'xmin', 1).text) ymin = int(get_and_check(bndbox, 'ymin', 1).text) xmax = int(get_and_check(bndbox, 'xmax', 1).text) ymax = int(get_and_check(bndbox, 'ymax', 1).text) assert (xmax > xmin+2) assert (ymax > ymin+2) o_width = abs(xmax - xmin) o_height = abs(ymax - ymin) ann = {'area': o_width * o_height, 'iscrowd': 0, 'image_id': image_id, 'bbox': [xmin, ymin, o_width, o_height], 'category_id': category_id, 'id': bnd_id, 'ignore': 0, 'segmentation': []} json_dict['annotations'].append(ann) bnd_id = bnd_id + 1 for cate, cid in categories.items(): cat = {'supercategory': 'none', 'id': cid, 'name': cate} json_dict['categories'].append(cat) json_fp = open(json_file, 'w') json_str = json.dumps(json_dict) json_fp.write(json_str) json_fp.close() list_fp.close() if __name__ == '__main__': # xml_list为xml文件存放的txt文件名 xml_dir为真实xml的存放路径 json_file为存放的json路径 xml_list = '你的数据集路径/code/datasets/BIRDSAI/cocoBIRDSAI/ImageSets/Main/train.txt' xml_dir = '你的数据集路径/code/datasets/BIRDSAI/cocoBIRDSAI/Annotations/' json_dir = '你的数据集路径/annotations/instances_trainl2017.json' # 注意！！！这里instances_trainl2017.json先要自己创建 convert(xml_list, xml_dir, json_dir)

3. 根据对应的txt文件将jpg图像文件转移到对应文件夹，需要新建train2017，val2017和test2017文件夹

#根据txt名称列表文件转移图像等数据文件 import shutil file = open('你的数据集路径/ImageSets/Main/train.txt', 'r') number_list = file.readlines() for i in range(len(number_list)): number_list[i] = number_list[i].strip() print(number_list) src_path = '你的数据集路径/JPEGImages/'#图像路径 target_path = '你的数据集路径/train2017/' while True: try: for number in number_list: shutil.move(src_path + number + '.jpg', target_path + number + '.jpg') # 文件名 except: break

数据集训练与测试

1. 如果你划分了测试集，并且想使用测试集，需要修改以下三个文件
configs/_base_/datasets文件夹下的coco_detection.py、coco_instance.py和coco_instance_semantic.py最后几行
test=dict(
type=dataset_type,
ann_file=data_root + 'annotations/instances_val2017.json',
img_prefix=data_root + 'val2017/',
改为
test=dict(
type=dataset_type,
ann_file=data_root +'annotations/instances_test2017.json',
img_prefix=data_root +'test2017/',

2. 如果你的显卡不是很好，更改训练时图像尺寸以提高训练速度
configs/_base_/datasets文件夹下的coco_detection.py、coco_instance.py和coco_instance_semantic.py中
所有的img_scale=(1333, 800)--> img_scale=(600, 400)#根据自己需要修改大小

3. 修改目标类别class_names.py和coco.py文件
mmdet/core/evaluation/class_names.py文件
def coco_classes():
return [
'person', 'elephant', 'lion', 'giraffe'
]#你自己的目标类别

mmdet/datasets/coco.py文件
class CocoDataset(CustomDataset):
CLASSES = ('person', 'elephant', 'lion', 'giraffe')#你自己的目标类别

4.修改目标类别数量 num_class=
configs/_base_/models/faster_rcnn_r50_fpn.py文件
num_classes=80-->num_classes=4

5. 训练参数修改，主要是configs/_base_/schedules/schedule_1x.py文件
学习率lr 最大迭代数量max_epochs，根据需要修改

6. 修改完成后最好重新编译python setup.py develop再运行

7.训练指令

多GPUbash ./tools/dist_train.sh configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py2（2是GPU数量根据你自己的显卡数修改）
单GPUpython tools/train.py configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py

8. 训练完成后数据存放在work_dirs/faster_rcnn_r50_fpn_1x_coco/文件夹中
latest.pth文件为最终的网络模型，faster_rcnn_r50_fpn_1x_coco.py为模型和数据集参数文件。用于接下来的测试集测试

9. 测试评估
多GPU./tools/dist_test.sh ./work_dirs/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco.py ./work_dirs/faster_rcnn_r50_fpn_1x_coco/latest.pth 2 --eval bbox --options "classwise=True"
单GPUpython 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/latest.pth --eval bbox --options "classwise=True"

--eval bbox表示评估mAP

--options "classwise=True"表示评估每个类别的AP

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