=len(label_name_to_value)label_name_to_value[label_name]=label_value lbl,_=utils.shapes_to_label(img.shape,data["shapes"],label_name_to_value)label_names=[None]*(max(label_name_to_value.values())+1)forname,valueinlabel_name_to_value.items():label_names[value]=name lbl_viz=imgviz....
from keras.utils.np_utilsimportto_categorical from keras.layersimportDropout,Flatten from keras.layers.convolutionalimportConv2D,MaxPooling2Dimportcv2 from sklearn.model_selectionimporttrain_test_split from keras.preprocessing.imageimportImageDataGenerator 加载数据 Python Pandas包帮助我们处理数据集。我们首先将...
(-1, 28, 28, 1) / 255.0 # 把训练集和测试集的标签转为独热编码 y_train = tf.keras.utils.to_categorical(y_train, num_classes=10) y_test = tf.keras.utils.to_categorical(y_test, num_classes=10) # 定义顺序模型 model = Sequential() # 第一个卷积层 # input_shape 输入数据 # ...
batch_size = 512 train_loader = torch.utils.data.DataLoader( torchvision.datasets.MNIST('mnist_data', train=True, download=True, transform=torchvision.transforms.Compose([ torchvision.transforms.ToTensor(), torchvision.transforms.Normalize( (0.1307,), (0.3081,)) ])), batch_size=batch_size, shuffl...
from mrcnn import utils import mrcnn.model as modellib from mrcnn import visualize # Import COCO config sys.path.append(os.path.join(ROOT_DIR, "samples/coco/")) # To find local version import coco %matplotlib inline # Directory to save logs and trained model ...
from CTPN.utils.text_connector.text_connect_cfg import Config as TextLineCfg def ctpn_text_...
使用预训练词向量:utils.py的main函数可以提取词表对应的预训练词向量。 数据集、词表及对应的预训练词向量,已经打包好,详见THUCNews文件夹。 效果 完整项目和数据集代码获取地址: 关注微信公众号 datayx 然后回复NLP实战即可获取。 Python环境及安装相应依赖包 ...
import mrcnn.utils from mrcnn.model import MaskRCNN from pathlib import Path # Configuration that will be used by the Mask-RCNN library class MaskRCNNConfig(mrcnn.config.Config): NAME = "coco_pretrained_model_config" IMAGES_PER_GPU = 1 ...
setup.py安装完成后,到python_root/Lib/site-packages/utils中可以找到两个文件cython_bbox.pyd和cython_nms.pyd,把这两个文件复制到fast_rcnn/lib/utils中。 5:在fast-rcnn的主目录中,执行python ./tool/demo.py 可以查看结果 可选参数为: --cpu,--net vgg16等。