mmdetection 类别出错问题+训练+测试

mmdetection 是时下比较常用的目标检测工具箱，集成了faster ,cascade,yolo 等性能比较好的检测算法，熟练使用mmdetection进行自定义数据的目标检测就成为了很多AIer的必经之路。

项目仓库地址：https://github.com/open-mmlab/mmdetection

安装，装环境就略过了。

装完之后，想要训练自己的数据集，首先要将自己的数据整理成COCO的格式：

--------data

---------------coco

-----------------------annotations

---------------------------------------train.json,val.json

------------------------images

---------------------------------------......jpg

annotations文件夹下包含了train.json和val.json，images文件夹下则为全部图像数据。

自己的数据标签格式需要整理为json才行，具体的转换方法可以直接去搜程序，也可以自己写。这里给出一部分，可能不适应直接用。

1.xml转coco

import os.path as osp
import xml.etree.ElementTree as ET

import mmcv

from mmdet.core import sea_classes

from glob import glob
from tqdm import tqdm
from PIL import Image

label_ids = {name: i + 1 for i, name in enumerate(sea_classes())}


def get_segmentation(points):
    return [points[0], points[1], points[2] + points[0], points[1],
            points[2] + points[0], points[3] + points[1], points[0], points[3] + points[1]]


def parse_xml(xml_path, img_id, anno_id):
    tree = ET.parse(xml_path)
    # print(str(xml_path))
    root = tree.getroot()
    annotation = []
    for obj in root.findall('object'):
        try:
            name = obj.find('name').text
            # if name in ['lighter','powerbank','zippooil','firecrackers','handcuffs','pressure','slingshot','nailpolish']:
            #    continue

            category_id = label_ids[name]
            bnd_box = obj.find('bndbox')
            xmin = int(bnd_box.find('xmin').text)
            ymin = int(bnd_box.find('ymin').text)
            xmax = int(bnd_box.find('xmax').text)
            ymax = int(bnd_box.find('ymax').text)
            if xmin >= xmax or ymin >= ymax:
                continue
            w = xmax - xmin + 1
            h = ymax - ymin + 1
            area = w * h
            segmentation = get_segmentation([xmin, ymin, w, h])
            annotation.append({
                "segmentation": segmentation,
                "area": area,
                "iscrowd": 0,
                "image_id": img_id,
                "bbox": [xmin, ymin, w, h],
                "category_id": category_id,
                "id": anno_id,
                "ignore": 0})
            anno_id += 1
        except:
            continue
    return annotation, anno_id


def cvt_annotations(img_path, xml_p