detectron2训练自己的数据集_YOLO(v3)PyTorch版 训练自己的数据集

Yolo v3比Frcnn好调试多了……就是数据集准备比较麻烦…… 但是好Debug，linux和win10差别不大……

代码链接（cpu版本）：

这个代码……作者说的太草率了……data怎么准备都没说清……好歹issue里面有大神解答，给了傻瓜版教程，运行他的几个脚本就好了，data文件夹就准备好啦！

data文件准备，按照这个

虽然这个作者是用它来训练coco数据集，但是data整个是个四不像……不用json不用xml用txt……所以训练自己的比较麻烦…… 准备好data，还有修改config/yolov3.cfg文件。

参考链接：

打开yolov3.cfg文件后，搜索yolo，共有三处yolo，下面以一处的修改作为示例。

[convolutional] #紧挨着[yolo]上面的[convolutional]
size=1
stride=1
pad=1
filters=21 #filters=3*(你的class种类数+5）
activation=linear
[yolo]
mask = 6,7,8
anchors = 10,13,  16,30,  33,23,  30,61,  62,45,  59,119,  116,90,  156,198,  373,326
classes=2 #修改classes
num=9
jitter=.3
ignore_thresh = .7
truth_thresh = 1
random=0 #显存大的写1 反之0

除此之外，cfg中的其他参数可以参考这个链接来进行修改，比如可以修改一些数据增强的参数，如果想要接着上次训练的weight继续训练，就参考这个链接进行微调（但是我使用的代码不支持clear操作，只能使用第二种方法）。

然后开始训练吧！个人感觉yolov3学的效果不是很好，frcnn训练了十轮能达到的效果，yolov3可能要80轮左右，开始前几十轮mAP很低就多加几轮试试，issue里面提到这个代码训练100轮，coco也达不到作者所说的mAP……所以……慎重……男票用这个帮我把自己的数据集跑到了92%左右的mAP，效果还是很好的。 准备数据集的那个代码有些bug调不出来，所以还是使用第一个的代码吧。 记录一下用过的脚本之 voc的xml转化为coco的json

import os
import json
import xml.etree.ElementTree as ET
import numpy as np
import cv2def _isArrayLike(obj):return hasattr(obj, '__iter__') and hasattr(obj, '__len__')class voc2coco:def __init__(self, devkit_path=None, year=None):# self.classes = ('__background__',#                 'aeroplane', 'bicycle', 'bird', 'boat',#                 'bottle', 'bus', 'car', 'cat', 'chair',#                 'cow', 'diningtable', 'dog', 'horse',#                 'motorbike', 'person', 'pottedplant',#                 'sheep', 'sofa', 'train', 'tvmonitor')self.classes = ('none','ll', 'rr') #写你自己的classself.num_classes = len(self.classes)assert 'VOCdevkit' in devkit_path, 'VOC地址不存在: {}'.format(devkit_path)self.data_path = os.path.join(devkit_path, 'VOC' + year)self.annotaions_path = os.path.join(self.data_path, 'Annotations')self.image_set_path = os.path.join(self.data_path, 'ImageSets')self.year = yearself.categories_to_ids_map = self._get_categories_to_ids_map()self.categories_msg = self._categories_msg_generator()def _load_annotation(self, ids=[]):ids = ids if _isArrayLike(ids) else [ids]image_msg = []annotation_msg = []annotation_id = 1for index in ids:filename = '{:0>6}'.format(index)json_file = os.path.join(self.data_path, 'Segmentation_json', filename + '.json')num=0if os.path.exists(json_file):img_file = os.path.join(self.data_path, 'JPEGImages', filename + '.jpg')im = cv2.imread(img_file)width = im.shape[1]height = im.shape[0]seg_data = json.load(open(json_file, 'r'))assert type(seg_data) == type(dict()), 'annotation file format {} not supported'.format(type(seg_data))for shape in seg_data['shapes']:seg_msg = []for point in shape['points']:seg_msg += pointone_ann_msg = {"segmentation": [seg_msg],"area": self._area_computer(shape['points']),"iscrowd": 0,"image_id": int(index),"bbox": self._points_to_mbr(shape['points']),"category_id": self.categories_to_ids_map[shape['label']],"id": annotation_id,"ignore": 0}annotation_msg.append(one_ann_msg)annotation_id += 1else:xml_file = os.path.join(self.annotaions_path, filename + '.xml')tree = ET.parse(xml_file)size = tree.find('size')objs = tree.findall('object')width = size.find('width').textheight = size.find('height').textfor obj in objs:bndbox = obj.find('bndbox')[xmin, xmax, ymin, ymax] = [int(bndbox.find('xmin').text) - 1, int(bndbox.find('xmax').text),int(bndbox.find('ymin').text) - 1, int(bndbox.find('ymax').text)]if xmin < 0:xmin = 0if ymin < 0:ymin = 0bbox = [xmin, xmax, ymin, ymax]one_ann_msg = {"segmentation": self._bbox_to_mask(bbox),"area": self._bbox_area_computer(bbox),"iscrowd": 0,"image_id": int(num),"bbox": [xmin, ymin, xmax - xmin, ymax - ymin],"category_id": self.categories_to_ids_map[obj.find('name').text],"id": annotation_id,"ignore": 0}annotation_msg.append(one_ann_msg)annotation_id += 1one_image_msg = {"file_name": filename + ".jpg","height": int(height),"width": int(width),"id": int(num)}image_msg.append(one_image_msg)num=num+1return image_msg, annotation_msgdef _bbox_to_mask(self, bbox):assert len(bbox) == 4, 'Wrong bndbox!'mask = [bbox[0], bbox[2], bbox[0], bbox[3], bbox[1], bbox[3], bbox[1], bbox[2]]return [mask]def _bbox_area_computer(self, bbox):width = bbox[1] - bbox[0]height = bbox[3] - bbox[2]return width * heightdef _save_json_file(self, filename=None, data=None):json_path = os.path.join(self.data_path, 'cocoformatJson')assert filename is not None, 'lack filename'if os.path.exists(json_path) == False:os.mkdir(json_path)if not filename.endswith('.json'):filename += '.json'assert type(data) == type(dict()), 'data format {} not supported'.format(type(data))with open(os.path.join(json_path, filename), 'w') as f:f.write(json.dumps(data))def _get_categories_to_ids_map(self):return dict(zip(self.classes, range(self.num_classes)))def _get_all_indexs(self):ids = []for root, dirs, files in os.walk(self.annotaions_path, topdown=False):for f in files:if str(f).endswith('.xml'):id = int(str(f).strip('.xml'))ids.append(id)assert ids is not None, 'There is none xml file in {}'.format(self.annotaions_path)return idsdef _get_indexs_by_image_set(self, image_set=None):if image_set is None:return self._get_all_indexs()else:image_set_path = os.path.join(self.image_set_path, 'Main', image_set + '.txt')assert os.path.exists(image_set_path), 'Path does not exist: {}'.format(image_set_path)with open(image_set_path) as f:ids = [x.strip() for x in f.readlines()]return idsdef _points_to_mbr(self, points):assert _isArrayLike(points), 'Points should be array like!'x = [point[0] for point in points]y = [point[1] for point in points]assert len(x) == len(y), 'Wrong point quantity'xmin, xmax, ymin, ymax = min(x), max(x), min(y), max(y)height = ymax - yminwidth = xmax - xminreturn [xmin, ymin, width, height]def _categories_msg_generator(self):categories_msg = []for category in self.classes:if category == 'none':continueone_categories_msg = {"supercategory": "none","id": self.categories_to_ids_map[category],"name": category}categories_msg.append(one_categories_msg)return categories_msgdef _area_computer(self, points):assert _isArrayLike(points), 'Points should be array like!'tmp_contour = []for point in points:tmp_contour.append([point])contour = np.array(tmp_contour, dtype=np.int32)area = cv2.contourArea(contour)return areadef voc_to_coco_converter(self):img_sets = ['trainval', 'test']for img_set in img_sets:ids = self._get_indexs_by_image_set(img_set)img_msg, ann_msg = self._load_annotation(ids)result_json = {"images": img_msg,"type": "instances","annotations": ann_msg,"categories": self.categories_msg}self._save_json_file('voc_' + self.year + '_' + img_set, result_json)def demo():# 转换pascal地址是'./VOC2007/VOCdevkit/VOC2007/ImageSets/Main/trainval.txt'converter = voc2coco('D:Codingpythondata_myselfVOCdevkit2007', '2007')converter.voc_to_coco_converter()if __name__ == "__main__":demo()

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