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 训练自己的数据集

要训练自己的数据集使用Faster R-CNN模型，你需要进行以下步骤：

1. 首先，你需要准备好自己的数据集，并将其转换为Pascal VOC格式。确保你的数据集包含图像和相应的标注文件。

2. 接下来，你需要下载Faster R-CNN模型的预训练权重文件。你可以在models/pascal_voc/ZF/faster_rcnn_alt_opt文件夹下找到5个文件，分别为stage1_rpn_train.pt、stage1_fast_rcnn_train.pt、stage2_rpn_train.pt、stage2_fast_rcnn_train.pt和fast_rcnn_test.pt。\[1\]

3. 在训练之前，你需要修改一些文件的格式和参数。首先，将output/faster_rcnn_alt_opt/voc_2007_trainval中的最终模型ZF_faster_rcnn_final.caffemodel拷贝到data/faster_rcnn_models文件夹中，并删除以前生成的类似的模型文件。\[2\]

4. 接下来，你需要修改stage1_fast_rcnn_train.pt和stage2_fast_rcnn_train.pt文件的参数。将num_class参数设置为2（识别1类+背景1类），将cls_score中的num_output参数设置为2，将bbox_pred中的num_output参数设置为8。这是为了适应你的自定义数据集。\[3\]

5. 最后，你可以开始训练你的自定义数据集。使用修改后的模型文件和数据集进行训练，可以使用相应的训练脚本或命令来启动训练过程。

请注意，以上步骤仅为训练自己的数据集所需的一般步骤。具体的操作可能会因你使用的框架或工具而有所不同。建议你参考相关的文档或教程以获取更详细的指导。
#### 引用[.reference_title]
- *1* *2* *3* [faster rcnn 训练自己的数据](https://blog.csdn.net/hanpengpeng329826/article/details/64905021)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v91^insert_down28v1,239^v3^insert_chatgpt"}} ] [.reference_item]
[ .reference_list ]