This work is the study and investigation of modern CNN architecture models used through transfer learning and checking the accuracy and efficiency with new image dataset. We compared the most widely used CNN transfer learning models available and observed that results were quite comparable with ...
Image classification is an important application for deep learning. With the advent of quantum technology, quantum neural networks (QNNs) have become the focus of research. Traditional deep learning-based image classification involves using a convolutional neural network (CNN) to extract features from ...
Xnor-net: Imagenet classification using binary convolutional neural networks[C]//ECCV. Springer, Cham, 2016: 525-542. [4] Li Z, Ni B, Zhang W, et al. Performance guaranteed network acceleration via high-order residual quantization[C]//IEEE ICVV. 2017: 2584-2592. [5] Hou L, Yao Q,...
我们提出了二值CNN的两种变体:二值权重,其中W的元素是二值张量和XNOR网络,其中 I 和 W 的元素都是二值张量。 3.1 Binary-Weight-Networks Training Binary-Weights-Networks:训练CNN的每次迭代都涉及三个步骤;前向传递、后向传递和参数更新。为了训练具有二值权重的CNN(在卷积层中),我们仅在前向传播和反向传播期...
We evaluate our approach on the ImageNet classification task. The classification accuracy with a Binary-Weight-Network version of AlexNet is the same as the full-precision AlexNet. We compare our method with recent network binarization methods, BinaryConnect and BinaryNets, and outperform these ...
由于深度模型具有多层结构和数百万个参数,因此深层 CNN 具有很强的学习能力,通常具有令人满意的性能。例如,VGG-16 网络包含大约 1.4 亿个 32 位浮点参数,对于 ImageNet 数据集上的图像分类任务,可以达到 92.7% 的 Top-5 测试准确率。 整个网络需要占用 500M 字节以上的存储空间,并再一次推理过程中执行 1.6x10 ...
XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks ECCV2016 http://allenai.org/plato/xnornet 本文介绍了两种二值网络: Binary-Weight-Networks 和 XNOR-Networks,Binary-Weight-Networks 只对滤波器 filters 进行二值化,XNOR-Networks 同时对 滤波器 和滤波器的输入进行二值化,这里的滤...
XNOR-Net ImageNet Classification Using Binary Convolutional Neural Networks 本人想把算法思想实现在mxnet上(不单纯是一个layer),有意愿一起的小伙伴可以联系我,本人qq(邮箱):564326047(@qq.com),或者直接在下面留言。 一、Introduction A. 相关工作 论文中提到了好几种加速DNN的方法,由于小w精力有限,并没有一一...
In this article, we will explore the concepts, advantages, and disadvantages of Inception V3 PyTorch classification and PyTorch binary classification.Inception V3 PyTorch ClassificationInception V3 is a popular convolutional neural network (CNN) architecture that is widely used for image classification ...
对于alexnet网络在imagenet上做二值的话,原始的准确率为56.6%,使用bwn为56.8%,使用xnor-net则是44.2%。可以看到,bwn比原始的还高一点。xnor-net则下降了将近10多个点,下降的比较多。在resnet实验中 bwn和xnor-net下降地同样很厉害,bwn将近下降了9个点。xnor-net下降了20个点。这实际工作中这似乎不能接受。在...