搜索资源列表
fast-rcnn-master
- Fast Region-based Convolutional Networks for object detection. Fast R-CNN** is a fast framework for object detection with deep ConvNets. Fast R-CNN - trains state-of-the-art models, like VGG16, 9x faster than traditional R-CNN and 3x faster than
vgg16
- 这是vgg16神经网络基于python的实现，可以用来识别图像中出现的物体。(This is the vgg16 neural network based on Python implementation, can be used to identify objects in the image.)
vgg16
- 在使用深度神经网络时我们一般推荐使用大牛的组推出的和成功的网络。如最近的google团队推出的BN-inception网络和inception-v3以及微软最新的深度残差网络ResNET。(In the use of deep neural network we generally recommend the use of cattle group launched and successful network. Such as the recent google team launched B
VGG16
- VGG16模型python实现，可以运行的。VGG16模型python实现，(VGG16 model Python implementation, can run. VGG16 model Python implementation,)
vgg16
- vgg16的tensorflow实现，用python语言编写，导入pycharm即可使用(vgg 16 TensorFlow realization)
vgg16_dpsh
- 深度学习网络vgg的实现，文件内包括网络中各层网络的搭建(Implementation of deep learning network Vgg)
tensorflow-fcn-master
- fcn语义分割的实现，基于VGG16等网络(The implementation of FCN semantic segmentation, based on VGG16 and other networks)
fenlei
- 利用深度学习进行遥感图像场景分类 这里我们对NWPU-RESISC45数据集的场景图像进行分类 我们将卷积神经网络应用于图像分类。我们从头开始训练数据集。此外，还应用了预先训练的VGG16 abd ResNet50进行迁移学习。(Scene Classification of Remote Sensing Images Using Deep Learning Here we classify scene images from NWPU-RESISC45 dataset We apply
利用tensorflow编写的vgg16网络跑cifar10
- 利用tensorflow编写的vgg16网络跑cifar10，使用python语言，精确度较高.
tensorflow-vgg16-train-and-test-master
- vgg深度学习，图像识别，用于图像的分类，在python上运行(vgg deep learning, image recognition, used for image classification, running on Python)
VGG
- VGG16分类网络,可以用于分类问题，采用了迁移学习(VGG16 classification net)
使用vgg16训练cifar数据集
- 神经网络 深度学习 慕课平台 tensorflow2.1 使用vgg16训练cifar10分类数据集
汪星人识别项目
- python语言，使用keras框架，用vgg16提取图片特征然后用全连接层分类(Python language, using keras framework, extracting image features with vgg16 and classifying with full connection layer)