fasterrcnn训练自己的数据集

要训练自己的数据集，可以按照以下步骤进行：

1. 准备数据集：收集并标注自己的数据集，确保每个样本都有对应的标签信息。

2. 安装相关软件：安装Python、PyTorch、Cuda等必要的软件，以及安装Detectron2或MMDetection等深度学习框架。

3. 配置训练参数：根据自己的数据集和需求，配置训练参数，如学习率、批次大小、迭代次数等。

4. 训练模型：使用自己的数据集训练Fast R-CNN模型，可以选择使用预训练模型或从头开始训练。

5. 评估模型：使用测试集对训练好的模型进行评估，计算模型的准确率、召回率等指标。

6. 预测新数据：使用训练好的模型对新的数据进行预测，得到目标检测结果。

需要注意的是，训练自己的数据集需要一定的计算资源和时间，同时也需要一定的深度学习基础知识。建议在进行训练前先学习相关的深度学习知识和技术。

相关问题

faster rcnn训练自己数据集的代码

Faster R-CNN 是一种基于深度学习的目标检测算法，它可以用于训练自己的数据集。下面是 Faster R-CNN 训练自己数据集的代码示例：

1. 准备训练数据集

首先需要准备训练数据集，包括图像和标注文件。标注文件可以是 VOC 格式或 COCO 格式。

2. 安装依赖库和下载代码

需要安装 TensorFlow 和 Keras，以及下载 Faster R-CNN 的代码。

3. 修改配置文件

修改 Faster R-CNN 的配置文件，包括训练和测试的参数、数据集路径以及模型保存路径等。

4. 训练模型

运行训练代码，使用准备好的数据集进行训练，直到模型收敛或达到预设的训练轮数。

5. 测试模型

使用测试数据集对训练好的模型进行测试，评估模型的准确率和召回率等指标。

6. 模型优化

根据测试结果对模型进行优化，包括调整参数、增加数据集大小等。

参考代码：

以下是 Faster R-CNN 训练自己数据集的代码示例。这里以 TensorFlow 和 Keras 为例，代码中的数据集为 VOC 格式。

# 导入依赖库
import tensorflow as tf
from keras import backend as K
from keras.layers import Input
from keras.models import Model
from keras.optimizers import Adam
from keras.utils import plot_model
from keras.callbacks import TensorBoard, ModelCheckpoint
from keras_frcnn import config
from keras_frcnn import data_generators
from keras_frcnn import losses as losses_fn
from keras_frcnn import roi_helpers
from keras_frcnn import resnet as nn
from keras_frcnn import visualize

# 设置配置文件
config_output_filename = 'config.pickle'
network = 'resnet50'
num_epochs = 1000
output_weight_path = './model_frcnn.hdf5'
input_weight_path = './resnet50_weights_tf_dim_ordering_tf_kernels.h5'
tensorboard_dir = './logs'
train_path = './train.txt'
test_path = './test.txt'
num_rois = 32
horizontal_flips = True
vertical_flips = True
rot_90 = True
output_weight_path = './model_frcnn.hdf5'

# 加载配置文件
config = config.Config()
config_output_filename = 'config.pickle'

# 加载数据集
all_imgs, classes_count, class_mapping = data_generators.get_data(train_path)
test_imgs, _, _ = data_generators.get_data(test_path)

# 计算平均像素值
if 'bg' not in classes_count:
    classes_count['bg'] = 0
    class_mapping['bg'] = len(class_mapping)

config.class_mapping = class_mapping

# 计算平均像素值
C = config.num_channels
mean_pixel = [103.939, 116.779, 123.68]
img_size = (config.im_size, config.im_size)

# 组装模型
input_shape_img = (None, None, C)
img_input = Input(shape=input_shape_img)
roi_input = Input(shape=(num_rois, 4))
shared_layers = nn.nn_base(img_input, trainable=True)

# RPN 网络
num_anchors = len(config.anchor_box_scales) * len(config.anchor_box_ratios)
rpn_layers = nn.rpn(shared_layers, num_anchors)

# RoI 网络
classifier = nn.classifier(shared_layers, roi_input, num_rois, nb_classes=len(classes_count), trainable=True)
model_rpn = Model(img_input, rpn_layers)
model_classifier = Model([img_input, roi_input], classifier)

# 加载权重
model_rpn.load_weights(input_weight_path, by_name=True)
model_classifier.load_weights(input_weight_path, by_name=True)

# 生成训练数据
data_gen_train = data_generators.get_anchor_gt(all_imgs, classes_count, C, K.image_dim_ordering(), mode='train', \
                                               img_size=img_size, \
                                               num_rois=num_rois, \
                                               horizontal_flips=horizontal_flips, \
                                               vertical_flips=vertical_flips, \
                                               rot_90=rot_90)

# 编译模型
optimizer = Adam(lr=1e-5)
model_rpn.compile(optimizer=optimizer, loss=[losses_fn.rpn_loss_cls(num_anchors), losses_fn.rpn_loss_regr(num_anchors)])
model_classifier.compile(optimizer=optimizer, loss=[losses_fn.class_loss_cls, losses_fn.class_loss_regr(len(classes_count) - 1)], metrics={'dense_class_{}'.format(len(classes_count)): 'accuracy'})

# 训练模型
epoch_length = 1000
num_epochs = int(num_epochs)
iter_num = 0

losses = np.zeros((epoch_length, 5))
rpn_accuracy_rpn_monitor = []
rpn_accuracy_for_epoch = []

start_time = time.time()

best_loss = np.Inf

class_mapping_inv = {v: k for k, v in class_mapping.items()}
print('Starting training')

for epoch_num in range(num_epochs):

    progbar = generic_utils.Progbar(epoch_length)
    print('Epoch {}/{}'.format(epoch_num + 1, num_epochs))

    while True:
        try:

            if len(rpn_accuracy_rpn_monitor) == epoch_length and C.verbose:
                mean_overlapping_bboxes = float(sum(rpn_accuracy_rpn_monitor)) / len(rpn_accuracy_rpn_monitor)
                rpn_accuracy_rpn_monitor = []
                print('Average number of overlapping bounding boxes from RPN = {} for {} previous iterations'.format(mean_overlapping_bboxes, epoch_length))
                if mean_overlapping_bboxes == 0:
                    print('RPN is not producing bounding boxes that overlap the ground truth boxes. Check RPN settings or keep training.')

            X, Y, img_data = next(data_gen_train)

            loss_rpn = model_rpn.train_on_batch(X, Y)

            P_rpn = model_rpn.predict_on_batch(X)

            R = roi_helpers.rpn_to_roi(P_rpn[0], P_rpn[1], C.image_dim_ordering(), use_regr=True, overlap_thresh=0.7, max_boxes=300)

            X2, Y1, Y2, IouS = roi_helpers.calc_iou(R, img_data, C, class_mapping)

            if X2 is None:
                rpn_accuracy_rpn_monitor.append(0)
                rpn_accuracy_for_epoch.append(0)
                continue

            # sampling positive/negative samples
            neg_samples = np.where(Y1[0, :, -1] == 1)
            pos_samples = np.where(Y1[0, :, -1] == 0)

            if len(neg_samples) > 0:
                neg_samples = neg_samples[0]
            else:
                neg_samples = []

            if len(pos_samples) > 0:
                pos_samples = pos_samples[0]
            else:
                pos_samples = []

            rpn_accuracy_rpn_monitor.append(len(pos_samples))
            rpn_accuracy_for_epoch.append((len(pos_samples)))

            if C.num_rois > 1:
                if len(pos_samples) < C.num_rois // 2:
                    selected_pos_samples = pos_samples.tolist()
                else:
                    selected_pos_samples = np.random.choice(pos_samples, C.num_rois // 2, replace=False).tolist()
                try:
                    selected_neg_samples = np.random.choice(neg_samples, C.num_rois - len(selected_pos_samples), replace=False).tolist()
                except:
                    selected_neg_samples = np.random.choice(neg_samples, C.num_rois - len(selected_pos_samples), replace=True).tolist()

                sel_samples = selected_pos_samples + selected_neg_samples
            else:
                # in the extreme case where num_rois = 1, we pick a random pos or neg sample
                selected_pos_samples = pos_samples.tolist()
                selected_neg_samples = neg_samples.tolist()
                if np.random.randint(0, 2):
                    sel_samples = random.choice(neg_samples)
                else:
                    sel_samples = random.choice(pos_samples)

            loss_class = model_classifier.train_on_batch([X, X2[:, sel_samples, :]], [Y1[:, sel_samples, :], Y2[:, sel_samples, :]])

            losses[iter_num, 0] = loss_rpn[1]
            losses[iter_num, 1] = loss_rpn[2]

            losses[iter_num, 2] = loss_class[1]
            losses[iter_num, 3] = loss_class[2]
            losses[iter_num, 4] = loss_class[3]

            iter_num += 1

            progbar.update(iter_num, [('rpn_cls', np.mean(losses[:iter_num, 0])), ('rpn_regr', np.mean(losses[:iter_num, 1])),
                                      ('detector_cls', np.mean(losses[:iter_num, 2])),
                                      ('detector_regr', np.mean(losses[:iter_num, 3])),
                                      ('mean_overlapping_bboxes', float(sum(rpn_accuracy_for_epoch)) / len(rpn_accuracy_for_epoch))])

            if iter_num == epoch_length:
                loss_rpn_cls = np.mean(losses[:, 0])
                loss_rpn_regr = np.mean(losses[:, 1])
                loss_class_cls = np.mean(losses[:, 2])
                loss_class_regr = np.mean(losses[:, 3])
                class_acc = np.mean(losses[:, 4])

                mean_overlapping_bboxes = float(sum(rpn_accuracy_for_epoch)) / len(rpn_accuracy_for_epoch)
                rpn_accuracy_for_epoch = []

                if C.verbose:
                    print('Mean number of bounding boxes from RPN overlapping ground truth boxes: {}'.format(mean_overlapping_bboxes))
                    print('Classifier accuracy for bounding boxes from RPN: {}'.format(class_acc))
                    print('Loss RPN classifier: {}'.format(loss_rpn_cls))
                    print('Loss RPN regression: {}'.format(loss_rpn_regr))
                    print('Loss Detector classifier: {}'.format(loss_class_cls))
                    print('Loss Detector regression: {}'.format(loss_class_regr))
                    print('Elapsed time: {}'.format(time.time() - start_time))

                curr_loss = loss_rpn_cls + loss_rpn_regr + loss_class_cls + loss_class_regr

                iter_num = 0
                start_time = time.time()

                if curr_loss < best_loss:
                    if C.verbose:
                        print('Total loss decreased from {} to {}, saving weights'.format(best_loss, curr_loss))
                    best_loss = curr_loss
                    model_rpn.save_weights(output_weight_path)
                    model_classifier.save_weights(output_weight_path)

                break

        except Exception as e:
            print('Exception: {}'.format(e))
            continue

print('Training complete, exiting.')

这是一个简单的 Faster R-CNN 训练自己数据集的示例代码，可以根据自己的数据集和需求进行修改和优化。

faster rcnn训练自己数据集。txt

### 使用Faster R-CNN训练自定义数据集

#### 准备工作
为了使用Faster R-CNN模型训练自定义数据集，需先准备好数据集并将其转换成适合该模型处理的格式。通常情况下，这涉及到创建类似于PASCAL VOC标准结构的数据集目录[^3]。

对于`.txt`格式标注文件而言，每一行代表一个对象实例，其中包含了类别标签以及边界框坐标(xmin, ymin, xmax, ymax)，这些信息应当被解析出来用于后续操作。

#### 数据预处理
构建符合要求的数据集结构之后，下一步是对图像及其对应的标注信息做进一步整理：

- **读取图片路径**：遍历`JPEGImages`文件夹获取所有待处理样本；
- **加载标注详情**：依据相应规则解析位于`Annotations`下的XML文档或是直接从指定位置读入简单的文本记录；

确保每张输入图片都有唯一ID关联至具体的矩形框描述列表，以便于后续步骤中的匹配查找过程顺利开展。

#### 实现细节
下面给出一段基于PyTorch框架实现上述逻辑的关键代码片段作为参考：

import os
from xml.etree import ElementTree as ET
import torch.utils.data as data


class CustomDataset(data.Dataset):
    def __init__(self, root_dir='path/to/VOC2007', set_type='train'):
        self.root = root_dir
        image_set_file = os.path.join(self.root, 'ImageSets/Main/{}.txt'.format(set_type))
        
        with open(image_set_file) as f:
            ids = [line.strip() for line in f.readlines()]
            
        self.ids = ids
        
    def _parse_voc_xml(self, node):
        voc_dict = {}
        children = list(node)
        if not children:
            return node.text
        for child in children:
            item_list = []
            class_name = child.tag.replace('-', '_')
            value = self._parse_voc_xml(child)
            if isinstance(value, dict):                
                item = {class_name: value}
            elif type(value) is str and len(value.split())>1 :
                item={class_name:value.split()}
            else:
                item = {class_name: value}               
            item_list.append(item)

            if class_name in voc_dict.keys():
                if type(voc_dict[class_name]) is list:
                    voc_dict[class_name].extend(item_list)
                else:
                    voc_dict[class_name] = [voc_dict[class_name]]
                    voc_dict[class_name].extend(item_list)
            else:
                voc_dict.update({class_name:item})
                    
        return voc_dict
    
    def parse_annotation(self,id_):
        anno_path=os.path.join(self.root,'Annotations','{}.xml'.format(id_))    
        tree=ET.parse(anno_path)
        objs=self._parse_voc_xml(tree.getroot())
        boxes=[]
        labels=[]

        for obj in objs['object']:
            bbox=obj['bndbox']
            box=[float(bbox[tag])for tag in ['xmin','ymin','xmax','ymax']]
            label=int(obj['name']) # 假设名称已经被映射成了整数索引
            
            boxes.append(box)
            labels.append(label)
            
        return {'boxes':torch.tensor(boxes,dtype=torch.float32),
               'labels':torch.tensor(labels)}

此部分展示了如何继承`data.Dataset`类来定制化自己的数据源接口，并实现了基本的功能函数用以完成对单个样本的信息抽取任务。注意这里假设已经完成了类别名到数值型编码之间的映射关系建立，在实际应用当中还需要额外考虑这一点。

#### 模型配置与训练
当一切准备就绪后，则可参照官方教程或者其他开源项目资源来进行具体建模环节的操作了。比如设置超参数、初始化权重、定义损失计算方式等等。由于这部分内容较为复杂且依赖特定环境搭建情况，因此建议参阅相关资料深入学习[^4]。