faster rcnn 自己的数据集 目标检测 代码 教程

Faster R-CNN是一种常用的目标检测算法，可以用于检测图像中的目标物体。在使用Faster R-CNN进行目标检测时，我们需要自己准备一个数据集，以便训练模型识别我们关心的目标。

首先，我们需要收集一组带有标签的图像，标签可以是目标物体的边界框坐标和类别。收集到的图像应包含我们要检测的目标物体以及背景等其他物体。

接下来，我们需要为数据集标注目标物体的边界框和类别。可以使用标注工具手动标注或者借助一些自动标注工具。将标注结果保存为XML、JSON等格式。

然后，我们需要将数据集划分为训练集和测试集。训练集用于训练模型，测试集用于评估模型的性能。

接着，我们需要根据Faster R-CNN的代码教程进行模型训练。可以参考PyTorch官方提供的代码实现或者一些开源实现代码。在训练模型时，需要配置网络参数、学习率以及其他超参数，并加载准备好的数据集。

在训练过程中，模型将学习如何从图像中检测出我们关心的目标物体。通过调整模型的网络结构和参数，我们可以提高模型的检测精度。

最后，我们可以使用训练好的模型在新的图像上进行目标检测。将测试图像输入到模型中，模型将输出该图像中检测到的目标物体的位置和类别。

总结来说，使用Faster R-CNN进行目标检测，我们需要准备自己的数据集并进行标注，然后根据代码教程进行模型的训练和调优，最后使用训练好的模型进行目标检测。这个过程需要一定的编程能力和对深度学习模型的理解。

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目标检测代码

以下是使用PyTorch实现Faster R-CNN目标检测的示例代码：

import torch
import torchvision
import torchvision.transforms as transforms
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
from torch.autograd import Variable

from collections import OrderedDict
import numpy as np
import cv2
import os

# 定义超参数
learning_rate = 0.001
num_epochs = 5
batch_size = 16

# 定义数据预处理
transform = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))
])

# 加载COCO数据集
train_data = torchvision.datasets.CocoDetection(root='./data', annFile='./data/annotations/instances_train2017.json', transform=transform)
train_loader = DataLoader(train_data, batch_size=batch_size, shuffle=True)

# 定义模型
class FasterRCNN(nn.Module):
    def __init__(self):
        super(FasterRCNN, self).__init__()
        self.features = nn.Sequential(OrderedDict([
            ('conv1', nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1)),
            ('relu1', nn.ReLU(inplace=True)),
            ('conv2', nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1)),
            ('relu2', nn.ReLU(inplace=True)),
            ('conv3', nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1)),
            ('relu3', nn.ReLU(inplace=True)),
            ('conv4', nn.Conv2d(256, 512, kernel_size=3, stride=1, padding=1)),
            ('relu4', nn.ReLU(inplace=True)),
            ('conv5', nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1)),
            ('relu5', nn.ReLU(inplace=True)),
            ('maxpool', nn.MaxPool2d(kernel_size=2, stride=2))
        ]))

        self.roi_pool = nn.AdaptiveMaxPool2d((7, 7))

        self.head = nn.Sequential(OrderedDict([
            ('fc6', nn.Linear(7 * 7 * 512, 4096)),
            ('relu6', nn.ReLU(inplace=True)),
            ('drop6', nn.Dropout()),
            ('fc7', nn.Linear(4096, 4096)),
            ('relu7', nn.ReLU(inplace=True)),
            ('drop7', nn.Dropout())
        ]))

        self.cls_score = nn.Linear(4096, 80)
        self.bbox_pred = nn.Linear(4096, 320)

    def forward(self, x, rois):
        x = self.features(x)
        x = self.roi_pool(x)
        x = self.head(x.view(x.size(0), -1))
        cls_score = self.cls_score(x)
        bbox_pred = self.bbox_pred(x)

        return cls_score, bbox_pred

# 定义损失函数和优化器
model = FasterRCNN()
criterion_cls = nn.CrossEntropyLoss()
criterion_bbox = nn.SmoothL1Loss()
optimizer = optim.SGD(model.parameters(), lr=learning_rate, momentum=0.9)

# 训练模型
for epoch in range(num_epochs):
    for i, (images, targets) in enumerate(train_loader):
        images = Variable(images)
        targets = [{k: Variable(v) for k, v in t.items()} for t in targets]

        # 前向传播
        cls_scores, bbox_preds = model(images, targets)
        cls_scores = torch.cat(cls_scores, dim=0)
        bbox_preds = torch.cat(bbox_preds, dim=0)

        cls_targets = torch.cat([t['labels'] for t in targets])
        bbox_targets = torch.cat([t['boxes'] for t in targets])

        # 计算损失
        loss_cls = criterion_cls(cls_scores, cls_targets)
        loss_bbox = criterion_bbox(bbox_preds, bbox_targets)
        loss = loss_cls + loss_bbox

        # 反向传播和优化
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        if (i + 1) % 100 == 0:
            print("Epoch [{}/{}], Iteration [{}/{}], Loss: {:.4f}"
                  .format(epoch + 1, num_epochs, i + 1, len(train_loader), loss.item()))

# 保存模型
torch.save(model.state_dict(), 'faster_rcnn.pth')

注意：以上代码仅是一个示例，实际使用时可能需要进行适当的修改和调整。